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Universita degli Studi di Milano - Bicocca

Scuola di Scienze

Dipartimento di Scienze dell’Ambiente e del Territorio e di Scienze della Terra

Corso di Laurea Magistrale in Scienze e Tecnologie per l’Ambiente e il Territorio

Optimization of supercritical CO2devulcanization process of cryo

ground tires and characterizationof the treated material

Relatore:Professoressa Marina LasagniCorrelatore:Ivan Mangili

Tesi di Laurea di:Matteo Oliveri

Matricola:716969

Anno Accademico 2012/2013

U N I V E R S I T A D E G L I S T U D I D I M I L A N O - B I C O C C A

S C U O L A D I S C I E N Z E

Dipartimento di Scienze dell’Ambiente e del Territorio e di Scienze della TerraCorso di Laurea Magistrale in Scienze e Tecnologie per l’Ambiente ed il Territorio

OPTIMIZATION OF SUPERCRITICAL CO2DEVULCANIZATION PROCESS OF CRYO GROUND TIRESAND CHARACTERIZATION OF THE TREATED MATERIAL

Relatore:Prof.ssa Marina LasagniCorrelatore:Dott. Ivan Mangili

Tesi di Laurea di:Matteo OLIVERI

Matricola:716969

Anno Accademico 2012/2013

The only limit, is the one you set yourself.

Felix Baumgartner

C O N T E N T S

Abstract VIII

Sommario XI

I Introduction 1

1 I N T R O D U C T I O N 21.1 Natural Rubber . . . . . . . . . . . . . . . . . . . . . . . 51.2 Synthetic Rubber . . . . . . . . . . . . . . . . . . . . . . 81.3 Rubber Processing . . . . . . . . . . . . . . . . . . . . . 101.4 Rubber Curing . . . . . . . . . . . . . . . . . . . . . . . . 111.5 Rubber Reclaming . . . . . . . . . . . . . . . . . . . . . . 121.6 Devulcanization Reaction Mechanism . . . . . . . . . . 161.7 Work Aim . . . . . . . . . . . . . . . . . . . . . . . . . . 18

II Material and Methods 19

2 M AT E R I A L 202.1 Ground Tire Rubber Characterization . . . . . . . . . . 20

3 G E N E R A L P R O C E D U R E 25

4 E X T R A C T I V E A N D A N A LY T I C A L T E C H N I Q U E S 284.1 Soxhlet Extraction . . . . . . . . . . . . . . . . . . . . . . 284.2 Supercritical Fluid Treatment . . . . . . . . . . . . . . . 304.3 Solvent Extraction to estimate Cross-link Density . . . . 364.4 Sol and Gel Fraction . . . . . . . . . . . . . . . . . . . . 394.5 Elemental Analysis CHNS . . . . . . . . . . . . . . . . . 39

5 T H E O P T I M I Z AT I O N O F T H E P R O C E S S 435.1 Analysis of the problem . . . . . . . . . . . . . . . . . . 445.2 Planning of the experiment . . . . . . . . . . . . . . . . 465.3 Model quality . . . . . . . . . . . . . . . . . . . . . . . . 465.4 Full Factorial Design . . . . . . . . . . . . . . . . . . . . 475.5 Multiple Linear Regression . . . . . . . . . . . . . . . . 505.6 Statistical treatment of regression . . . . . . . . . . . . . 525.7 Models validation and the problem of over-fitting . . . 545.8 Misuse of the ANOVA for 2k factorial experiments . . 575.9 Assessing the significant effects . . . . . . . . . . . . . . 575.10 Residual Analysis . . . . . . . . . . . . . . . . . . . . . . 58

II

Contents

6 C O M P U N D I N G 616.1 Polymers . . . . . . . . . . . . . . . . . . . . . . . . . . . 636.2 Fillers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 646.3 Processing AIDS . . . . . . . . . . . . . . . . . . . . . . . 666.4 Actvators, Vulcanizing Agents and Accellerators . . . . 666.5 Antidegradants . . . . . . . . . . . . . . . . . . . . . . . 676.6 Special compounding ingredients . . . . . . . . . . . . . 686.7 Compound properties and tests . . . . . . . . . . . . . . 696.8 Compounding composition . . . . . . . . . . . . . . . . 72

III Results and Discussion 75

7 C H E M I C A L R E S U LT S 767.1 Regression Models . . . . . . . . . . . . . . . . . . . . . 767.2 Influence of devulcanization process on cross-link network 857.3 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 86

8 M E C H A N I C A L P R O P E RT I E S O F T H E N E W B L E N D S 88

IV Conclusion 92

9 C O N C L U S I O N 93

Bibliography 98

III

L I S T O F F I G U R E S

Figure 1 Tires Landfill . . . . . . . . . . . . . . . . . . . . 4Figure 2 Isoprene units constituting Natural Rubber (NR)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Figure 3 Extracting procedure of NR . . . . . . . . . . . 7Figure 4 Monomers constituting Styrene-Butadiene Rub-

ber (SBR) . . . . . . . . . . . . . . . . . . . . . . 10Figure 5 Cross-link network sulfur formation . . . . . . 11Figure 6 Vulcanizate properties as a function of the ex-

tent of vulcanization . . . . . . . . . . . . . . . 12Figure 7 Reactions Mechanisms . . . . . . . . . . . . . . 13Figure 8 de-vulcanizing reagents . . . . . . . . . . . . . 15Figure 9 Simplified reaction scheme . . . . . . . . . . . . 17Figure 10 GTR Cumulative Distribution of Particel Size . 21Figure 11 Scanning Electron Microscope (SEM) pictures

of Ground Tires Rubber (GTR) particle . . . . . 21Figure 12 Thermogravimetric Analysis (TGA) of the GTR

in N2 and heating rate of 20�C/min . . . . . . . 22Figure 13 General Procedure Scheme . . . . . . . . . . . . 27Figure 14 Soxhlet Scheme . . . . . . . . . . . . . . . . . . 28Figure 15 Buchi Extraction System B811 . . . . . . . . . . 29Figure 16 Supercritical state . . . . . . . . . . . . . . . . . 30Figure 17 Transition from CO2 to Supercritical Carbon

Dioxide (scCO2) (by NASA) . . . . . . . . . . . 32Figure 18 Essential model of Supercritical Fluid Extraction

(SFE) . . . . . . . . . . . . . . . . . . . . . . . . 33Figure 19 Overview of SFE µ–plant . . . . . . . . . . . . . 34Figure 20 Q-Ation Reaction cell . . . . . . . . . . . . . . . 35Figure 21 Plant Flow Sheet . . . . . . . . . . . . . . . . . . 37Figure 22 Buchi Rotavapor R-200 . . . . . . . . . . . . . . 38Figure 23 Elemental Analyzer Perkin Elmer 2400 Series II 40Figure 24 Separation of gases for CHNS determinations 41Figure 25 The fundamental steps of screening phase . . . 45Figure 26 Main effect of temperture on the Gell fraction

response by Modde® . . . . . . . . . . . . . . . 49Figure 27 Scaled and Centered Coefficient for Cross-link

Density (SW) model . . . . . . . . . . . . . . . . 51Figure 28 Difference between determination coefficient

in fitting (R2) and determination coefficient inprediction (Q2) . . . . . . . . . . . . . . . . . . . 55

Figure 29 Confidence intervals . . . . . . . . . . . . . . . 58

IV

List of Figures

Figure 30 Example of Normal Probability Plot . . . . . . 59Figure 31 Effect of carbon black level on compound prop-

erties. . . . . . . . . . . . . . . . . . . . . . . . . 65Figure 32 The effect of heat history (processing) on scorch

safety. . . . . . . . . . . . . . . . . . . . . . . . . 70Figure 33 Oscillating disc rheometer. . . . . . . . . . . . . 71Figure 34 Rheometer cure curve. . . . . . . . . . . . . . . 72Figure 35 Typical tensile stress-strain curve. . . . . . . . . 72Figure 36 Normal Probability Plot of residuals . . . . . . 81Figure 37 Normal Probability Plot of residuals . . . . . . 82Figure 38 3-D plot for cross-link density . . . . . . . . . . 83Figure 39 3-D plot for experimental responses . . . . . . 84Figure 40 3-D plot for Sulfur Content . . . . . . . . . . . . 85Figure 41 Comparision between Experimental Curve and

Horikx Function . . . . . . . . . . . . . . . . . . 86Figure 42 Validation Results . . . . . . . . . . . . . . . . . 87Figure 43 Affects of the treatment on the elogation at break 88Figure 44 Stress Strain curves . . . . . . . . . . . . . . . . 89Figure 45 Affects of the treatment on the tensile strength 90Figure 46 Affects of the treatment on the moduli . . . . . 91

V

L I S T O F TA B L E S

Table 1 Current Rubber waste situation . . . . . . . . . 3Table 2 Major plant sources of natural rubber . . . . . 6Table 3 Chemical Composition of Fresh Latex . . . . . 7Table 4 Worldwide capacity for major elastomers in kilo-

tonnes . . . . . . . . . . . . . . . . . . . . . . . . 10Table 5 GTR Composition . . . . . . . . . . . . . . . . . 22Table 6 Additives found by Gas chromatography–mass

spectrometry (GC-MS) analysis of the acetoneextract. . . . . . . . . . . . . . . . . . . . . . . . 23

Table 7 Additives found by GC-MS analysis of the chlo-roform extract. . . . . . . . . . . . . . . . . . . . 24

Table 8 Critical values for some fluid used as Supercrit-ical Fluid . . . . . . . . . . . . . . . . . . . . . . 32

Table 9 K factors calculation . . . . . . . . . . . . . . . 41Table 10 Optimized combustion times for soil analysis. 42Table 11 Experimental Planning . . . . . . . . . . . . . . 48Table 12 Passenger tire tread recipe . . . . . . . . . . . . 61Table 13 Three different groups of polymers . . . . . . . 63Table 14 Effects of activator and accellerator on cure time

of NR compounds . . . . . . . . . . . . . . . . . 67Table 15 Rubber Compound Tests and American Society

for Testing and Materials (ASTM) Designations 70Table 16 Treatment conditions of GTR for compounding 73Table 17 Blends formulation . . . . . . . . . . . . . . . . 74Table 18 Full Factorial design and experimental responses. 76Table 19 Analysis of Variance for the interaction model

of the responses. . . . . . . . . . . . . . . . . . . 77Table 20 Regression model for each experimental response 78Table 21 Estimated effects and standard error calculated

through the three central experiments. . . . . . 79Table 22 Estimated effects and standard error calculated

through higher order interactions. . . . . . . . 80Table 23 Validation experiment conditions within the

studied domain. . . . . . . . . . . . . . . . . . . 87

VI

L I S T O F A B B R E V I AT I O N S

ANOVA Analysis of Variance

ASTM American Society for Testingand Materials

BR Butadiene Rubber

CB Carbon Black

DD Diphenil Disulphide

DPNR Deproteinised Natural Rubber

ELTS End of Life Tires

EPDM Ethylene-propylene-dieneMonomer

GC-MS Gas chromatography–massspectrometry

GTR Ground Tires Rubber

IR Isoprene Rubber

MLR Multiple Linear Regression

MSS Model Sum of Squares

NR Natural Rubber

OENR Oil Extended Natural Rubber

OVAT One Variable At Time

phr Parts per Hundred Rubber

PRESS Predictive Error Sum ofSquares

Q2 determination coefficient in pre-diction

R2ADJ adjusted determination coeffi-

cient in fitting

R2 determination coefficient in fitting

RSS Residual Sum of Squares

S Sulfur Content

SBR Styrene-Butadiene Rubber

SBS styrene-butadiene-styrene blockcopolymer

SCF Super Critical Fluid

scco2 Supercritical Carbon Dioxide

SEM Scanning Electron Microscope

SFE Supercritical Fluid Extraction

SFT Supercritical Fluid Treatment

SSLOF Lack-of-fit Sum of Squares

SSPE Error Sum of Squares

SW Cross-link Density

TGA Thermogravimetric Analysis

T-GTR Treated Ground Rubber Tires

TPNR Thermoplastic Natural Rubber

TSS Total Sum of Squares

VII

A B S T R A C T

The tires problem does not concern their production, although theirdisposal. In 2011 about 3,2 million of post consumer tires were re-moved from all types of vehicles in Europe. In Europe the directive2005/64/EC, concerning automotive vehicles recycling, obliges theautomotive industry to recycle 95% of the weight of used cars fromJanuary 2015 onwards; thus, the rubber industry cannot avoid consid-ering the recycling of used rubber products.

Up to now, among all possible ways to take on the problem of ELTs,one of the most commonly applied is to dump them in a landfill. But, alarge scrap of stockpiled tires presents a threat to human health and toenvironment. The main option to face the problem of ELTs is recovered,it is possible through two ways: the recovery of material and the re-covery of energy. Taking into account the hypothesis of energy recover,it should consider the environmental cost of burning tires, indeedthey generate large amounts of air polluting emissions, pyrolytic oils,and ashes. Thus the real challenge for the rubber industry is reclaiming.

Waste tires, being made of high quality rubbers, could represent a largepotential source of raw material for the rubber industry. The mainreason for the low-scale current application of rubber recycling is thatwaste tires are infusible due to the three-dimensional cross-linkingnetwork, which is hard to decompose.

The pulverization of ELTs is one of the most used recycling techniques.The direct introduction of Ground Tires Rubber (GTR) in new blendswith virgin rubber results in bad properties due to the presence of thesulfur cross-link network that leads to a weak adhesion and deteriora-tion of the final properties. In the last decade a green devulcanizationprocess was developed for cured rubbers employing supercritical CO2(scCO2) as reaction medium for some devulcanizing reagents, in partic-ular Diphenil Disulphide (DD).

Most of the previous studies investigated this reclaiming process onseveral types of rubber, Natural Rubber (NR), synthetic rubbers andalso Ground Tires Rubber (GTR). Most of these researches were carriedout in order to find the best devulcanizing conditions and some ofthem focused on finding a relationship among several variables thatcan affect the process and the yield of devulcanization. These studieshave analyzed the process parameters just considering One Variable At

VIII

Abstract

Time (OVAT), but this approach does not allow exploring the influenceof the interaction among variables on the response.

The aim of the present study was to investigate the scCO2 devulcaniza-tion process of a GTR by a two level full factorial experimental design.This approach allows understanding which variables and interactionshave a significant influence on the process and how it is possible tocontrol these parameters. The scCO2 devulcanization process of a GTRwas investigated studying, as variables, the effect of treatment time,temperature, percentage of devulcanizing reagent DD and CO2 pres-sure. The cross-link density, the sol fraction, the gel fraction and thesulfur content were chosen as experimental responses. The cross-linkdensity, the sol and gel fraction gave information on the degree ofdevulcanization. Instead, the sulfur content was an important quanti-tative indicator of the reaction between the DD and the GTR. Sixteenexperiments were carried out to investigate the experimental domain;three experiments were added to investigate the performance in thecenter of the experimental domain and to estimate the model validity,the reproducibility and the experimental error.

The dependence of each experimental response on the factors was mod-elled applying the Multiple Linear Regression (MLR). The Analysisof Variance (ANOVA) is normally used to determine which factorsand which interactions had a significant influence on the process. Tofurther discriminate whether the factors and interactions were real, therelevance of the effects for the factors and interactions was evaluatedcomparing each computed effect with the standard error (SE) througha t-test. Moreover, under the assumption that the highest order interac-tions are largely due to noise, the effect of these interactions can providea reference set for the estimation of the standard error. In any cases, themost significant variables resulted temperature, DD percentage andtheir interaction. Moreover, for a sound evaluation of the model, theresiduals distribution was studied both with the method of NormalProbability Plot and with Shapiro-Wilk normality test. In both case thenormal distribution for the residuals was confirmed.

In contrast to the other studies the full factorial design allowed to un-derstand both the influence of pressure resulted negligible and thetreatment time is the least important factor.

In order to point out the best fitting model and to confirm the signifi-cant variables for each experimental response, the stepwise approachwas used to find the best combination of factors and interactions eval-uating the coefficient of determination (R2), the adjusted R2 and thecoefficient of determination for prediction (Q2). An external validation

IX

Abstract

allows to confirm that the final regression model developed in thestudy resulted in a reliable prediction of devulcanization indicatorswithin the experimental domain.

Indications regarding the reaction mechanism between DD and rubberwere also obtained. The high temperature and the subsequent decom-position of the DD generate radicals that react with the rubber chainand with the cross-link network increasing the sulfur content of theTreated GTR.

Moreover, the effect of scCO2 devulcanization process on a GTR usingDD was investigated in order to evaluate the possible use of the re-claimed material in new blends. The mechanical properties of the newblends were studied: increasing of elongation at break and a decreasingof modulus and tensile strength were observed.

These results have an important outcome since this devulcanizationprocess can be carried out in a short time and at relatively low pressure,with subsequent energy saving.

X

S O M M A R I O

Ogni anno il numero di pneumatici che “arrivano a fine vita” (Pneu-matici Fuori Uso, PFU) e sempre piu in aumento. Solo nel 2011 laquantita di PFU europei ammontava a circa 3,2 milioni di tonnellate.In Europa, per via delle restrizioni all’uso delle discariche e grazie alladirettiva 2005/64/EC concernente il riciclo degli autoveicoli, dal 2015,il 95% in peso delle macchine usate dovra essere obbligatoriamentericiclato. Per questo motivo, il problema degli PFU deve essere presoseriamente in considerazione.

Fino ad ora, tra tutte le vie possibili per affrontare la questione deglipneumatici usati, la piu utilizzata e stata quella dello stoccaggio in dis-carica. Le principali possibili alternative per smaltirli sono il recuperodi energia o il riciclo del materiale. Valutando la prima ipotesi e nec-essario includere i costi ambientali generati dalla combustione e dallapirolisi degli pneumatici, poiche queste generano emissioni tossiche,oli pirolitici molto inquinanti e ceneri che devono essere comunquesmaltite. Gli pneumatici usati rappresenterebbero una fonte di gommadi alta qualita, ma la percentuale del loro riciclo e scarsa per colpa dellavulcanizzazione, un trattamento chimico che genera dei ponti zolfotra le catene polimeriche, (cross-link network) conferendo alla gommaalta elasticita e aumentando la resistenza all’usura; questi legami sonopero difficili da rompere e rendono quindi complesso il riciclo di queimateriali che hanno subito questo trattamento.

La polverizzazione degli PFU e una delle tecniche di riciclaggio piudiffuse. L’introduzione diretta in mescola del polverino da pneumatici(Ground Tires Rubber, GTR), insieme a gomma vergine, genera prodotticon scarse proprieta meccaniche. Diversi processi di devulcanizzazione,chimici, meccanici e termomeccanici, sono stati sviluppati per ovviarea questo problema: negli ultimi anni e stato sviluppato un processodi devulcanizzazione chimica in CO2 supercritica (scCO2), che sfruttacome agente devulcanizzante il Difenil Disolfuro (DD).

In letteratura, molti autori hanno studiato questo processo di devul-canizzazione su diversi tipi di gomma: naturale, sintetica e anche GTR.Molte di queste ricerche sono state condotte per comprendere qualisiano le variabili influenti nel processo e conseguentemente per scoprirequali siano le per migliori condizioni per questo tipo di trattamento. Inquesti studi pero, il processo e stato analizzato considerando la vari-azione di una variabile alla volta (One Variable At Time, OVAT); questo

XI

Sommario

approccio non permette di comprendere l’influenza, sulla risposta,delle interazioni tra le variabili.

L’obiettivo di questo lavoro e stato quello di studiare la devulcanizazz-ione del polverino da pneumatici in fase supercritica attraverso undisegno fattoriale completo a due livelli. Quest’approccio permettedi comprendere sia quali variabili e interazioni tra queste possiedanouna significativa influenza sul processo sia comprendere quali sonole condizioni di ottimo di devulcanizzazione. Le variabili studiatesono state: tempo (durata del trattamento) temperatura, pressione epercentuale in peso tra DD e gomma. Le risposte analizzate eranorispettivamente il contenuto di zolfo (S) come indicatore quantitativodella reazione tra DD e GTR e la densita di cross-link (Sw), la frazionesolubile (Sol) e la frazione insolubile (Gel) come indicatori del grado didevulcanizzazione.

Sono stati eseguiti un totale di 19 esperimenti, 16 dei quali per studiareil dominio sperimentale e 3 prove sia per comprendere il comporta-mento al centro del dominio, sia per stimare la riproducibilita e lavalidita del modello attraverso lo studio dell’errore sperimentale.

Le relazioni che regolano le risposte e i fattori sono state modellateattraverso l’uso della regressione lineare multipla. Oltre all’analisi dellavarianza in regressione, ANOVA, la significativita dei fattori e stataanche valutata comparando ogni effetto con l’errore standard tramiteun t-test. Inoltre, assumendo che le interazioni a piu di due variabilisiano principalmente dovute al rumore sperimentale, la somma deiloro effetti e stata sfruttata come un ulteriore stima dell’errore standarde anche in questo caso e stato eseguito un t-test. In tutti i casi i fattoririsultati significativi sono stati: temperatura, rapporto tra DD e gommae la loro interazione. In aggiunta, per una piu accurata valutazione delmodello, e stata studiata la distribuzione dei residui di ogni risposta siaattraverso il metodo grafico del Normal Probability Plot che attraversoil test di normalita di Shapiro-Wilk. Entrambi i test confermano unadistribuzione normale per tutte le risposte.

Diversamente dagli studi di letteratura precedenti, l’uso di un disegnofattoriale completo ha permesso di comprendere che l’influenza dellapressione e del tempo di trattamento sono trascurabili e quindi nonincidono significativamente sulle risposte.

Al fine di trovare il miglior modello che descrivesse i dati e per con-fermare la significativita delle variabili sulle risposte sperimentali, unapproccio step-wise e stato utilizzato per ricercare la migliore combi-nazione tra variabili ed interazioni, valutando il coefficiente di deter-

XII

Sommario

minazione R2, R2adj e il coefficiente di determinazione in predizione Q2.

Attraverso una validazione esterna e stato possibile dimostrare che ilmodello finale di regressione risulta efficace nell’effettuare predizionisulla devulcanizzazione di un campione all’interno del dominio speri-mentale.

Sono state anche ottenute indicazioni riguardanti il meccanismo direazione tra disolfuro e gomma vulcanizzata; infatti l’alta temperaturae la conseguente decomposizione del DD comporta la generazione diradicali, che reagendo con la catena polimerica e il cross-link network,incrementano il contenuto di zolfo nel polverino.

Per studiare la qualita e la conseguente riciclabilita del GTR devulcan-izzato, sono state realizzate mescole contenenti diversa percentuale digomma trattata e sono state valutate alcune proprieta meccaniche. Irisultati hanno rivelato che l’introduzione in mescola del GTR trattatogenera un incremento dell’elongazione a rottura ma un decrementodel modulo e del carico a rottura rispetto all’aggiunta del GTR tal quale.

Questi risultati possono avere importanti risvolti pratici, poiche graziea questo studio e stato dimostrato che il processo di devulcanizzazionein fase supercritica puo avvenire in tempi brevi e a pressione relativa-mente bassa, con conseguente notevole risparmio di energia.

XIII

Aǹљ�ҫ%ȾɝɒɅȈɥȄɝȩɅȾ

1

1I N T R O D U C T I O N

This chapter gives an introduction into the topic of the thesis: rubberdevulcanization. Why are waste tires and rubber an important prob-lem of international meaning? Have any methods been developed inan attempt to solve this problem?

With the development of the petroleum chemical industry, the num-ber of products in the three main synthetic categories are increasing,and their production is increasing too. Nowadays, more than onehundred million tons polymer materials are discarded as waste everyyear worldwide [10] . These materials cannot return to the ecologicalenvironment through natural biological degradation, hydrolyzation ordecomposition, like plants or animals, because they cannot degradethemselves. They belong to the class of non-environmental materials.

Rubber is one of the three main polymer material groups; the annualconsumption of virgin rubber (natural and synthetic) is approximately21.4 million tons, and the output of rubber products is more than 43million tons worldwide [7]. With the development of the rubber indus-try, a lot of waste rubber is produced in the world every year. The mainsource of waste rubber is discarded rubber products, such as: usedtires, rubber pipes, rubber shoes, rubber scraps and others.

Focusing the attention on tires, when they are no longer sufficientlysafe or efficient to be reused, they become waste and are referred toas End of Life Tires (ELTs). This extensive and intensive consumptionof rubber, in particular in the tire industry, causes a serious problemin terms of rubber waste. The tire industry is the largest producer ofvulcanized rubber. In 2000, about 2.5 million tons of post-consumertires were wasted in Europe [52]. About one billion post consumer tireswere removed from all types of vehicles worldwide. Discovering andresearching a solution for this problem is a big challenge for the rubberindustry. In particular, in Europe the directive 2005/64/EC, concerningautomotive vehicles recycling, obliges the automotive industry to recy-cle 95% of the weight of used cars from January 2015 onwards; thus,the rubber industry cannot avoid considering the recycling of usedrubber products.

Up to now, among all possible ways to take on the problem of ELTs,one of the most commonly applied is to dump them in a landfill [5].

2

I N T R O D U C T I O N

The ”current” rubber waste situation is presented in the Table 1. At theend of the 1950s, only about one fifth of the rubber hydrocarbon usedby the United States and Europe was reclaimed. By the middle of the1980s less than 1% of the worldwide polymer consumption was in theform of reclaim.

Table 1. Current Rubber waste situation

Treatment FR DE IT UK BE NL SE USAmethod (%) 1996 1996 1996 1996 1996 1996 1996 1994

Retreading 20 17.5 22.5 31 25 60 6 -Recycled 16 11.5 12 16.5 10 12 12.5 28Energy 15 46.5 23 27 32.5 28 66 72Landfill 45 4.5 40 23 7.5 0 7 -Export 4 16 2.5 2.5 25 NA 8.5 -

A large scrap of stockpiled tires presents a threat to human health andthe environment for several reasons. These tires provide an ideal breed-ing ground for mosquitoes and insects in general and consequentlyincrease the infection risk. Stockpiles may also catch fire as a resultof lightning strikes, equipment malfunctions, or arson. The longer astockpile is unabated, the more likely it chatches fire. Some experts con-sider the question of “if” a stockpile will catch fire but “when” it willburn [8]. Tire fires typically cause air, surface water, soil, groundwater,and residual contamination that have negative impacts on humans,animals, and plant life. When ignited, the scrap tire piles generatedense, black smoke, containing partially combusted hydrocarbons.The smoke plume can negatively impacts residences and business inits path as well as the air quality in a broad area for a long time. Inaddition to smoke, some tire fires produce large quantities of pyrolyticoils containing hazardous compounds. Under certain conditions, theseoils can penetrate porous soils contaminating the groundwater thatmay be used as drinking water. The oils can also reach surface waterand cause substantial fish kills, as the oils quickly deplete dissolvedoxygen level. Finally, the residuals (ash, wire, and unburned rubber)from a tire fire often require special handling and disposal.This situation not only adds any value to the waste material, but alsoit generates a negative cost due to: the transporting of the material tothe landfill sites, the establishing and the maintaining the landfills soto satisfy environmental requirements.

Given the non-sustainability of ELTs landfill, the main option to takeon the problem of ELTs is recovered, it is possible through two ways:the recovery of material and the recovery of energy. The high grosscalorific value of ELTs, close to that of coal [34], allows their use asa source of energy in paper mills, cement works or in thermoelectric

3

I N T R O D U C T I O N

Figure 1. Tires Landfill

power plants. Taking into account the hypothesis of energy recover, itis necessary to consider the cost of transport and of grinding. In factbecause of their size, it is necessary to grind the tires before burningthem. The cost of burning tires, should also include the environmentalcost because they generate large amounts of air polluting emissions,pyrolytic oils, and ashes. In particular, the last two, must be stored inlandfill.

The best way for handling post-consumer tires is the rubber reclaim-ing. Reclaiming is a procedure where the scrap tire rubber and thecured rubber waste is converted, using mechanical and thermal en-ergy and chemicals, into a state in which it can be mixed, processed,and vulcanized again [7]. The problem of rubber recycling relates tothe special structure of the polymers, which generally can be dividedinto two groups: thermoplastic and thermosetting materials. Ther-moplastics soften when heated, making it possible to re-shape themat higher temperatures. Thermosetting materials, instead, are cross-linked on heating and therefore cannot be softened or remodeled byany temperature increase. This makes thermosets more difficultly re-cyclable than thermoplastics: the three-dimensional network has tobe broken in order to make the material re-processable, the so-calledreclaiming process. With more details the vulcanization process is anirreversible reaction among the elastomer, sulfur, and other chemicals,which produce cross-links among the elastomer molecular chains andleading to the formation of a three-dimensional chemical network. Thecross-linked elastomers are solid, insoluble, and infusible thermoset-ting materials. This makes the direct reprocessing of end life tires andwaste rubbers impossible. Charles Goodyear, who invented the sul-fur vulcanization process, more than 150 years ago [13] , was also thefirst to initiate efforts to recycle cured rubber wastes through a grind-

4

1.1 N AT U R A L R U B B E R

ing method. Rubber devulcanization is a process in which the wasterubber or vulcanized waste product is converted, using mechanical,thermal, or chemical energy, into the state in which it can be mixed, pro-cessed, and vulcanized again [20]. Strictly speaking, devulcanization insulfur-cured rubber can be defined as a process where poly-, di-, andmono-sulfidic blends, formed during the previous vulcanization, istotally or partially broken. This means devulcanization can be definedas a process that causes the breakup of the chemical network alongwith the breakup of the macromolecular chains [38].

Waste tires, being made of high quality rubbers, should represent alarge potential source of raw material for the rubber industry. The mainreasons for the low-scale current application of rubber recycling arethose many stringent requirements for quality of rubber products andthose, correct but, stringent regulations for environmental protection.Thus reclaimed rubber is neither produced, nor requested, nor usedbecause of its high production cost. However, the increasing legislationfor restricting landfills requires a search for economical and environ-mental methods of recycling discarded tires and waste rubbers.

The aim of this work is to obtain an efficient and high quality materialthrough a development of devulcanization process of cryo-ground rub-ber from recycled truck tires, employing supercritical CO2 as carrier forDiphenyl Disulfide, the effective devulcanizing reagent. The optimumconditions for the devulcanization have been investigated, throughchemometric techniques, as well as the consequent structure and theproperties of the devulcanized rubber.


Natural rubber, also called caoutchouc, consists in mainly of poly �cis � 1, 4 � isoprene, it is a natural polymer most widely used as elas-tomer due to its unique properties. In fact natural rubber, either aloneor in combination with other materials, presents in most of its usefula large stretch ratio, high resilience, and extremely waterproof. Sofor more products there are no alternative materials except syntheticrubbers.

Natural rubber consists in thousands of isoprene units linked togetherto in a polymer. The cis � 1, 4 � polyisoprene is a compound with amolecular weight of 100,000 to 1,000,000.

The major commercial source of natural rubber is the Para rubber tree(Hevea brasiliensis), a member of the spurge family, Euphorbiaceae.This species is widely used because it grows well, near tropics, under

5


Figure 2. Isoprene units constituting NR

S S

S

CH2 CH

CHH2C

H2C CH2

Butadiene Unit Styrene Unit

CCH

H2C

CH2

C

CH3 CH3

CH

H2C

CH2

CCH

H2C

CH2

C

CH3 CH3

CH

H2C

CH3

ncis1,4-polyisoprene subunit

culture producing a lot of latex for several years long. More specificallythe economic life period of rubber trees in plantations is around 32years, up to 7 years of immature phase and about 25 years of productivephase. Many other plants (Table 2) produce forms of latex rich inisoprene polymers, but they suffer from disadvantages such as lowrubber content, high resin content thus they require more elaboratedand expensive processes to produce usable rubber.

Table 2. Major plant sources of natural rubber

Family Species Popular Name

Euphorbiaceae Hevea brasiliensis Para rubberCompositae Parthenium argentatum Guayule rubber

Euphorbiaceae Manihot glaziovii Ceara rubberMoracea Castilla elastica Panama rubberMoracea Ficus elastica India rubber

Apocynaceae Funtumia elastica Lagos silk rubber

However, Parthenium argentatum, which yields guayule rubber hasgained some importance in the last two decades, mainly due to thepossibility of cultivating it in the semi-arid regions of South-WesternUSA and Mexico.

From Hevea brasiliensis, the sap is extracted through an woodcutinto the bark and collecting the fluid in vessels; this process is called”tapping” (Figure 3) .

After it, the fluid is processed and refined until it has obtained a pro-cessable rubber. In the year 2001 about 10 million hectares (100,000 km2)are currently planted with it, producing around 6.7 million tones ofNR annually . The main area of growth of latex consumption was Asiafrom 1984 to 2003, the average growth rate for the region was 9.2% ayear, increasing its annual consumption from 144,000 tonnes to 692,000tonnes. This is 5% above the world rate for the same period. As of2003, the annual consumption level of the other five regions are: NorthAmerica 101,000 tonnes, Latin America 33,000 tonnes, European Union78,000 tonnes, other European countries 14,000 and Africa 4,000 tonnes

6


Figure 3. Extracting procedure of NR

[41]. The global NR consumption is split among tires (75%), automotivemechanical products (5%), nonautomotive mechanical products (10%),and miscellaneous applications such as medical and health-relatedproducts (10%) [5].

Typically when the crop is collected from plantations, in addition torubber there is water and a small percentage (up to 5% of dry mass)of other materials, such as proteins, fatty acids, resins and inorganicmaterials (Table 3).

Table 3. Chemical Composition of Fresh Latex

Contistuent PercentageRubber 30-40Proteins 1-1.5Resins 1.5-3

Minerals 0.7-0.9Caebohydrtes 0.8-1

Water 55-60

Before the processing in the factory, latex is sieved to remove contam-inants such as bark shavings, leaves, sand and small clots of rubber.Fresh field latex is not suitable for storage because it is open to bacterialattack and degradation. Therefore, latex needs to be process into aform which is suitable for long-term storage and marketing. The useof preservatives such as ammonia (at a concentration of 0.7 to 1.0% byweight) makes possible to prevent bacterial activity and enhance itsstability. In fact, during storage, the acid esters present in latex, arehydrolysed into ammonium soaps, which improves the mechanical sta-bility of latex. The second process is the latex concentration to remove asubstantial quantity of serum from field latex, thus making latex richerin rubber. To obtain a concentrated NR the major processes employed

7

1.2 S Y N T H E T I C R U B B E R

include four subprocesses: evaporation to remove only water and threeother subprocesses as electrodecantation, creaming and centrifuging toget the partial removal of non-rubber constituents and smaller rubberparticles.

The physical methods of modification of NR involve incorporation ofadditives which do not chemically react with rubber. The additivesinclude various rubber compounding ingredients and polymers suchas synthetic rubbers and thermoplastics. The commonest modificationsare the following:

• Oil incorporation in NR generates the Oil Extended Natural Rub-ber (OENR), it contains 20 to 25 phr of aromatic or naphthenicoil. In general, the increase of oil content reduces tensile strengthand resilience, but if the rubber is vulcanized it retains good tearresistance and possess high wear resistance. OENR also showsgood skid resistance on wet surfaces when used in tire tread.

• Polyolefines incorporation in NR generate the ThermoplasticNatural Rubber (TPNR). The main polyolefines are blended arepolypropylene and polyethylene; the varying proportions, as theratio varies materials with a wide range of properties are obtained.The elastic properties of TPNR are considerably improved if therubber phase is partially cross-linked during mixing

• Deproteinised Natural Rubber (DPNR) is a NR with low proteinand mineral content; The proteins and other hydrophilic non-rubbers can absorb moisture leading to reduction in modulusand electrical resistance and increase in stress relaxation andcreep.


Synthetic rubber consist several artificial elastomers mainly synthe-sized by petroleum byproducts. Rubberlike materials consists in rel-atively long polymeric chains having a high degree of flexibility andmobility, which are joined into a network structure. A network is ob-tained by linking polymer chains together. This linkage may be eitherphysical or chemical:

P H Y S I C A L C R O S S - L I N K S or “thermo-reversible cross-links” is, ingeneral, not permanent and may disappear on swelling or in-crease in temperature thus not to be considered.

C H E M I C A L C R O S S - L I N K S may be obtained by randomly joiningsegments in already formed chains, by random copolymerization,or by end-linking functionally terminated chains. Sulfur cures,

8


peroxide cures, and high-energy irradiations are familiar methodsof random cross-linking.

In a typical elastomer, the number of skeletal bonds in a network chainhas a range from about 100 to 700 . Networks with chains shorter than100 bonds have low extensibility. Those having chains much largerthan 700 bonds may have very high extensibility but are too weakto serve as load-carrying materials. It is possible to prepare bimodalnetworks, however, by end-linking very short and very long chains toform networks of significant toughness [5].

A wide variety of synthetic elastomers have been developed to over-come some of the natural rubber performance deficiencies. Some ofthese shortage are:

• Poor resistance to light, oxygen and ozone weathering,

• Poor heat resistance,

• Poor resistance to organic fluids.

In addition synthetic elastomers have supplanted many of the appli-cations of NR since the properties of NR can be less modified thanthe synthetic rubber. An example of this is the partial replacementof NR by Butadiene Rubber (BR) in sidewalls of tires since the latteroffers lower rolling resistance. Similarly, treads of tires are made withSBR since they last longer than treads made with NR.. The world-wide capacity (Table 4) for all synthetic elastomers is about 13,000kilotones per year and is comparable to natural rubber which is es-timated to be 17,000 kilotones per year. Elastomers such as Butadi-ene Rubber (BR), styrene-butadiene-styrene block copolymer (SBS),Isoprene Rubber (IR), Ethylene-propylene-diene Monomer (EPDM),Styrene-Butadiene Rubber (SBR) are the majority of this volume.

Tire production consumes approximately 60% of the global syntheticrubber production [5]. Of this, SBR is the largest-volume polymer,representing over 65% of the synthetic rubber used in tires. BR rankssecond in production output. SBR finds extensive use in tire treadsbecause it offers wet skid and traction properties while retaining goodabrasion resistance. BR is frequently found in treads, sidewalls, andsome casing components of the tire because it offers good abrasionresistance, and tread wear performance and enhances resistance to cutpropagation. BR can also be blended with natural rubber, and manyauthors have reported that such compositions give improved fatigueand cut growth resistance [35].

9

1.3 R U B B E R P R O C E S S I N G

Table 4. Worldwide capacity for major elastomers in kilo-tonnes

Elastomer Capacity, (Kt/year)

NR (for comparison) 5500000 treesPolybutadiene 2600Polyisoprene 1500Ethylene-propylene polymers 1150Styrene-butadiene rubber 5300Styrene block copolymers 650Halobutyl rubber 315Butyl rubber 800Ethylene-olefin plastomers 300

Figure 4. Monomers constituting SBR

S S

S

CH2 CH

CHH2C

H2C CH2


CCH

H2C

CH2

C

CH3 CH3

CH

H2C

CH2

CCH

H2C

CH2

C

CH3 CH3

CH

H2C

CH3


1.3 R U B B E R P R O C E S S I N G

Methods for processing rubber include various operations like mixing,calendaring, extrusion, all processes being essential to bring cruderubber into a state suitable for shaping the final product. The formerbreaks down the polymer chains, and lowers their molecular massso that viscosity is low enough for further processing. After this hasbeen achieved, rubber is first compounded with chemical additiveslike sulfur, Carbon Black (CB) and accelerators. The function for whichchemical additives are generally incorporated into the rubber as stabi-lizers, flame-retardants, colorants, plasticizers etc. to optimize productproperties and performance. This operation converts the rubber into adough-like mixture which is called ”compound” then milled into sheetsof desired thickness. The rubber may then be extruded or molded be-fore being cured.

10

1.4 R U B B E R C U R I N G

1.4 R U B B E R C U R I N G

Most rubber articles, such as tires, cannot be made without vulcaniza-tion because unvulcanized rubber is generally not very strong, doesnot maintain its shape after a large deformation, and can be very sticky.The first commercial method for vulcanization (also called curing) hasbeen attributed to Charles Goodyear, in his process the NR was heatedwith sulfur. Vulcanization can be defined as a process which increasesthe retractile force and reduces the amount of permanent deformationremaining after removal of the deforming force [5]. Thus vulcanizationincreases elasticity while it decreases plasticity. In other words, vulcan-ization is a process of chemically producing network junctures by theinsertion of cross-links between polymer chains (Figure 5).

Figure 5. Cross-link network sulfur formation

The accelerated-sulfur vulcanization of these rubbers along with the vul-canization of other rubbers which are vulcanized by closely related technol-ogy (e.g., ethylene-propylene-diene monomer rubber [EPDM], butyl rubber[IIR], halobutyl rubbers, and nitrile rubber [NBR]) comprises more than 90%of all vulcanization. Nevertheless, we give some consideration to vulcaniza-tion by the action of other vulcanization agents such as organic peroxides,phenolic curatives, and quinoid curatives.

Dynamic vulcanization (DV) is also considered. DV is the crosslinking ofone polymer in a blend of polymers during its mixing therein, all polymers of the blend being in the molten state. The process is used in the preparationof thermoplastic elastomeric compositions from rubber–plastic blends.

II. DEFINITION OF VULCANIZATION

Vulcanization is a process generally applied to rubbery or elastomericmaterials. These materials forcibly retract to their approximately originalshape after a rather large mechanically imposed deformation. Vulcanizationcan be defined as a process which increases the retractile force and reducesthe amount of permanent deformation remaining after removal of the deform-ing force. Thus vulcanization increases elasticity while it decreases plasticity.It is generally accomplished by the formation of a crosslinked molecularnetwork (Fig. 1.).

322 Aubert Y. Coran

FIGURE 1 Network formation.

The retractile force, to resist at deformation, is proportional to thenumber of network supporting polymer chains per unit volume ofelastomer. A supporting polymer chain is a linear polymer molecularsegment between network junctures. An increase in the number ofjunctures or crosslinks gives an increase in the number of supportingchains. A crosslink may be a group of sulfur atoms in a short chain,a single sulfur atom, a carbon to carbon bond, a polyvalent organicradical, an ionic cluster, or a polyvalent metal ion. The process is usu-ally carried out by heating the rubber, mixed with vulcanizing agents,under pressure. This process causes profound changes at the molecularstructure of the polymer chains.

The major effects of vulcanization on use-related properties (Figure 6)are:

11

1.5 R U B B E R R E C L A M I N G

Figure 6. Vulcanizate properties as a function of the extent of vulcanization

energy-to-break increase with increases in both the number of network chainsand hysteresis. Since hysteresis decreases as more network chains are devel-oped, the energy-to-break related properties are maximized at some interme-diate crosslink density.

It should be noted that the properties given in Fig. 2 are not functions onlyof crosslink density. They are also affected by the type of crosslink, the typeof polymer, and type and amount of filler, etc.

Reversion Reversion is a term generally applied to the loss of networkstructures by nonoxidative thermal aging. It is usually associated with isoprenerubbers vulcanized by sulfur. It can be the result of too long of a vulcaniza-tion time (overcure) or of hot aging of thick sections. It is most severe at tem-peratures above about 155°C. It occurs in vulcanizates containing a largenumber of polysulfidic crosslinks. Though its mechanism is complex, a gooddeal about the chemical changes that occur during the reversion of naturalrubber has been deduced [5].

Sometimes the term “reversion” is applied to other types of nonoxidativedegradation, especially with respect to rubbers not based on isoprene. Forexample, thermal aging of SBR (styrene-butadiene rubber), which can causeincreased crosslink density and hardening, has been called reversion since itcan be the result of overcure.

324 Aubert Y. Coran

FIGURE 2 Vulcanizate properties as a function of the extent of vulcanization.

• Static modulus increases to a greater extent than the dynamicmodulus1.

• Hysteresis is reduced with increasing cross-link formation2.

• Tear strength, fatigue life, and toughness are related to the break-ing energy. Values of these properties increase with small amountsof cross-linking, but they are reduced by further crosslink forma-tion.

• Properties related to the energy-to-break increase with increasesin both the number of network chains and hysteresis.

The chemistry of unaccelerated vulcanization is controversial. Someresearchers [5] have studied the unaccelerated vulcanization and theyproposed the mechanisms where are involved free radicals (Figure 8a),some other researchers [5], instead, have promoted ionic mechanisms(Figure 8b).The second type of mechanism explain that the reactionsgive unsaturated and saturated products, because sulfur atoms areconnected to both secondary and tertiary carbon atoms.


The process of fabricating many rubber articles, especially tires, in-volves vulcanization, an irreversible reaction between the elastomer,sulfur and other chemicals producing cross-links between the elastomermolecular chains and leading to the formation of a three-dimensional

1 The dynamic modulus is a composite of viscous and elastic behavior, whereas staticmodulus is largely a measure of only the elastic component of rheological behavior.

2 Hysteresis is the ratio of the rate-dependent or viscous component to the elasticcomponent of deformation resistance. It is also a measure of deformation energy thatis not stored (or borne by the elastic network) but that is converted to heat.

12


Figure 7. Reactions MechanismsThe chemistry of unaccelerated vulcanization is controversial. Many slow

reactions occur over the long period of vulcanization. Some investigators havefelt that the mechanisms involved free radicals [7–9]:

SCHEME 1

7 Vulcanization 329

(a) Free radicals reaction mechanismThe chemistry of unaccelerated vulcanization is controversial. Many slow

reactions occur over the long period of vulcanization. Some investigators havefelt that the mechanisms involved free radicals [7–9]:

SCHEME 1

7 Vulcanization 329

(b) Ionic reaction mechanism

13


chemical network. The cross-linked elastomers are solid, insolubleand infusible thermoset materials, thus this creates problem in rubberrecycling [41]. In contrast to thermoplastics, which can be melted andreprocessed, it is not possible to restore thermoset rubber to its virginstate by any means including the use of heat, chemicals or mechanicalaction.

Focusing the attention on tires, several methods have been developedin attempt to solve the problem and to find more effective ways fortheir recycling. These methods include:

G R I N D I N G : This method was invented by Goodyear about 150 yearsago [13]. Presently, there are three methods of grinding wasterubber: ambient grinding, cryogenic grinding and wet-ambientgrinding [15, 16]. Vulcanised scrap rubber is first reduced to a 50mm x 50 mm or 25 mm x 25 mm chip. Then a magnetic separatorand a fibre separator remove all the steel and polyester fragments.This can then be further reduced using ambient ground mill orground into fine particles while frozen using cryogenic grinding[26] to produce GTR. A method for obtaining fine-mesh rubber iscooling scrap tires in liquid nitrogen below their glass transitiontemperature and then pulverising the brittle material in a hammermill. Cryogenically ground rubber has a much finer particle sizevarying from 30 to 100 mesh. The cryogenic grinding allow lessdegradation of the rubber because it is generated less heat in theprocess.

P Y R O LY S I S A N D I N C I N E R AT I O N : Pyrolysis is the thermal decom-position of rubbers in the absence of air and oxygen to produceoils and gases for reuse by petrochemical industries. Carbonblack and other solid content remaining after pyrolysis can beutilized as fillers. Another established method of utilizing wornrubber tires is to burn them for their energy value. This is theincineration method.

P H Y S I C A L R E C L A I M I N G :

1. Microwave processes: the microwave process applies theheat very quickly and uniformly on the waste rubber. Themethod uses a controlled amount of microwave energy todevulcanise a sulfur cured elastomer to a state in which itcould be re-vulcanised. On the basis of the relative bondenergies of carbon-carbon, carbon-sulfur, and sulfur-sulfurbonds, it was presumed that the scission of the sulfur-sulfurand sulfur-carbon cross-links actually occurred. However,the material to be used in the microwave process must bepolar enough to accept energy at a rate sufficient to generatethe heat necessary for devulcanisation [34].

14


2. Ultrasonic processes: it is a process in which a vulcanisedrubber was devulcanised by ultrasonic waves after treat-ment for twenty minutes. The process claimed to breakdown carbon-sulfur bonds and sulfur-sulfur bonds, but notcarbon-carbon bonds. The properties of the revulcanisedrubber were found to be very similar to those of the origi-nal vulcanisates. The ultrasonic waves at certain levels, inthe presence of pressure and heat, can break up the three-dimensional network in cross-linked rubber. The process ofultrasonic devulcanisation is very fast, simple, efficient andsolvent and chemical free[34].

C H E M I C A L R E C L A I M I N G : described in detail following.

The principle of chemical reclaming is devulcanisation in which itis assumed that the cleavage of intermolecular bonds of the chemi-cal network, such as carbon-sulfur and/or sulfur-sulfur bonds takesplace, with further shortening of the chains [39]. Devulcanisation insulfur-cured rubber can be defined as the process of cleaving, totally orpartially, poly�, di� and mono�sulfidic cross-links which are formedduring the initial vulcanisation [51]. Chemical processing is a possiblemethod for devulcanising the vulcanised network through the use ofchemical agents that attack the C�S or S�S bonds. Some authors haveproposed a green de-vulcanization process for cured rubbers usingscCO2 as reaction medium for some de-vulcanizing reagents, in partic-ular DiphenylDisul f ide [4, 23, 28, 45]. Diphenyl Disulfide (Figure 9a)is the chemical compound with the formula (C6H5S)2. This colorlesscrystalline material is often abbreviated DD. When this material issubject at high temperature, it divides into two radicals (Figure 9b).

Figure 8. de-vulcanizing reagents

S S

S

CH2 CH

CHH2C

H2C CH2


CCH

H2C

CH2

C

CH3 CH3

CH

H2C

CH2

CCH

H2C

CH2

C

CH3 CH3

CH

H2C

CH3


(a) Diphenyl disulfide

S S

S

CH2 CH

CHH2C

H2C CH2


CCH

H2C

CH2

C

CH3 CH3

CH

H2C

CH2

CCH

H2C

CH2

C

CH3 CH3

CH

H2C

CH3


(b) Radicical

The diffusion rates of some devulcanizing reagents into the vulcan-izates rubber are expected to be enhanced by using Super CriticalFluid (SCF) as reaction media. Among several SCFs, CO2 is the mostadvantageous for the current purpose because it is chemically inac-

15

1.6 D E V U L C A N I Z AT I O N R E A C T I O N M E C H A N I S M

tive, nontoxic, nonflammable, and inexpensive. Supercritical fluidsshow unique physicochemical properties: they are of low viscosity,high diffusivity and high thermal conductivity [29]. These propertiescan be varied by changing temperature and pressure. In literature thedevulcanization of a model polymer network, i.e., sulfur-cured unfilledsynthetic polyisoprene rubber was carried out in scCO2 and it wasdemonstrated that scCO2 works very well in facilitating the penetra-tion of the devulcanizing reagent into IR vulcanizate [27]. In fact scCO2is a good swelling agent and the distribution coefficient of DD in scCO2is about four orders of magnitude higher than in toluene [27]. The DDis found to be one of the most effective reagent for de-vulcanization ofrubber.


A simplified reaction scheme proposed [42] for the reclamation of natu-ral rubber with DD is showed in Figure 9.

The radicals formed by scission of the DD are capable of hydrogenabstraction or addition to the double bonds in natural rubber [41]. Hy-drogen abstraction is relatively easy because the protons in an allylicposition are activated by the double bond (the resulting carbon radicalis resonance stabilized). The benzene-sulfide radical, therefore, ab-stracts the allylic hydrogen from the natural rubber vulcanizate to formbenzenethiol and a natural rubber vulcanizate radical. The polymerradical can now undergo main-chain scission and/or crosslink scission[40].

Most commercial tires contain a great amount of carbon black and otherfillers. CB has a great effect on the chemical and physical properties of arubber compound. The presence of carbon black in a rubber compoundcauses a significant increase in the cure rate by the chemical groupson the surface [50]. CB tends to reduce the swelling of a vulcanizate ina manner that is proportional to the filler content, while the crosslinkdensity in the rubber compound remains unchanged [30, 32]. Diffu-sivity of gases for NR vulcanizate depends on CB content as well; thehigher the fraction of carbon black the less diffusivity. Therefore, car-bon black has the possibility of affecting devulcanization using scCO2.It was demonstrated that the CB content does not interfere with thedevulcanization reaction through scCO2.

16


Figure 9. Simplified reaction schemeARTICLE IN PRESS

Fig. 19. Simplified reaction scheme proposed for the reclamation of natural rubber by diphenyldisulfide (adapted from Ref. [73]).

V.V. Rajan et al. / Prog. Polym. Sci. 31 (2006) 811–834 825

17

1.7 W O R K A I M

1.7 W O R K A I M

In this work, the devulcanization process using DD in scCO2 was stud-ied on GTR. The optimum conditions for the devulcanization wereinvestigated and were optimized, and the structure and mechanicalproperties of the devulcanized rubber were studied.

The ground rubber can be easily and homogeneously mixed with thepowder reagent and treated in a supercritical fluid reactor and repre-sents an appropriate material for this de-vulcanization method. Somesutudies analyzed the effect of the presence of unreacted diphenyldisulfide on the revulcanization process of reclaimed natural [28, 42]and butyl rubber [23] and just few researches evaluated the effect ofa large amount of residual DD on the re-vulcanization process of thereclaimed GTR [29].

The aim of the present study is to investigate the effect of the devulcan-ization process on GTR using DD as devulcanizing reagent and scCO2as solvent, in order to evaluate the possible use of the reclaimed mate-rial in new blends. The process was studied by a full factorial experi-mental design. This approach allows understanding which variablesand interactions have a significant influence on the process and how itis possible to control these parameters. Moreover, the devulcanizationprocess was investigated through a wide chemical characterization ofthe GTR and through the mechanical properties of the new blends.

18

Aǹљ�Ҭ5ǹɝȑјǹȶǹȾȈ�5ȑџɅȈɖ

19

2M AT E R I A L

This chapter describes the techniques used to characterize the rubberused in the present study.

2.1 G R O U N D T I R E R U B B E R C H A R A C T E R I Z AT I O N

The tire used in the present study was a ground rubber from wholetruck tires, collected from the local market. The rubber fraction of truckis mainly composed by natural rubber and synthetic rubber, more pre-cisely Butadiene Rubber and Styrene-Butadiene Rubber.

The tire powder was obtained by grinding rubber crumb in liquid nitro-gen, in order to prevent surface oxidation, down to reach dimensionssmaller than 40 mesh, with the exclusion and separating of metal andtextile components. The dimensions of the GTR particles were deter-mined by sieving through ASTM E-11 standard sieves of 10 differentwoven wire and weighing the respective fractions. The started materialwas prior analyzed [33] whit a following characteristic.

GTR composition

The relative and cumulative distributions of particle size (Figure 10)show that the 95% of the particles were smaller than 40 meshes withthe majority (about 80%) of dimensions between 40 and 100 meshes.

During the milling process, the nitrogen steam hindered a temperatureincreased and prevented the degradation and oxidation of the sample.Therefore surface of the cryo-ground GTR appears smooth. From theSEM picture (Figure 11) can be observed the dimensions of severalGTR particles.

The moisture content determined by drying the sample through vac-uum oven and through TGA were respectively 1.19 ± 0.2 wt% and0.9 ± 0.1 wt% of the initial weight.

Moreover, the amount of ashes determined by calcination and by TGAwere respectively 7.2 ± 0.8 wt% and 7.3 ± 1 wt% on the initial weight.The thermogravimetric (TGA/dTGA) curves of the GTR sample areshown in Figure 12. This analysis allowed the quantification of the

20


Figure 10. GTR Cumulative Distribution of Particel Size

Figure 11. SEM pictures of GTR particle

polymeric fraction. The two weight losses respectively with a maxi-mum rate at 385�C and rate at 430�C corresponded respectively to thedecomposition of NR and of synthetic rubbers. From the TGA analysisand considering the amount of the extractable, the rubber fraction wasproved to be the 53 wt% of the total weight and it was made up of 70%NR and less than 30% of synthetic rubbers (BR and SBR).

The composition of the GTR is shown in Table 5. The polymer fraction,the moisture and ashes were determined with the TGA, the extractablewere determined through the extraction procedure and the CB was de-termined as the difference between the starting weight and the weightof the other fractions.

21


Figure 12. TGA of the GTR in N2 and heating rate of 20�C/min

Table 5. GTR Composition

Parameter Value Standard Deviation

Polymer 53 1Carbon Black 30 3Extractables 9.4 0.2Moisture 0.9 0.1Ashes 7.2 0.8

Additives

The gas chromatography-mass spectrometry GC-MS analysis of theextracts led to the identification of several compounds that are shownin Tables 6 and 7. The mass-spectra of the National Institute of Stan-dards and Technology library was used, in the prior studies [33], for theidentification of the compounds. The MSD peaks were identified andassigned to a specific compound in case of a match probability higherthan 80%. Most of these compounds belong to the three main categoriesof additives: plasticizers, accelerators and antioxidants. Some peaksexhibited peculiar mass spectra. Many of these peaks resulted fromdegradation fragments of the basic structure of the rubbers. Consider-ing that the structure of the rubbers was based on poly-isoprene andpoly-butadiene, these fragments referred to characteristic fragmentsthat may been arisen from the degradation of the polymer rather thanfrom additives [6].

22


Tabl

e6.

Add

itive

sfo

und

byG

C-M

San

alys

isof

the

acet

one

extr

act.

Tim

e(m

in)

Com

poun

dTi

me(m

in)

Com

poun

d

8.96

Tolu

ene

36.5

3p-

Ani

sic

acid

,4-n

itrop

heny

lest

er9.

763-

Pent

en-2

-one

,4-m

ethy

l-38

.45

2(3H

)-Be

nzot

hiaz

olon

e10

.2A

cetic

acid

,but

yles

ter

39.9

8Te

trad

ecan

oic

acid

11.1

92-

Pent

anon

e,4-

hydr

oxy-

4-m

ethy

l-40

.85

Qui

nolin

e,6-

met

hyl-2

-phe

nyl-

13.2

3St

yren

e40

.98

2-N

apht

hale

nam

ine,

N-p

heny

l-13

.38

Cyc

lohe

xano

ne43

.23

Non

adec

ane

13.6

12-

Buto

xyet

hano

l44

.54

n-H

exad

ecan

oic

acid

14.1

7H

exyl

eneG

lyco

l45

.27

2-M

erca

ptob

enzo

thia

zole

16.9

4o-

Cya

nobe

nzoi

cac

id45

.89

Benz

othi

azol

e,2-

phen

yl-

17.5

1C

yclo

hexa

ne,i

socy

anat

o-47

.38

Poly

mer

18.3

5Bu

tane

dioi

cac

id,d

imet

hyle

ster

48.1

7O

leic

acid

22.1

4Pe

ntan

edio

icac

id,d

imet

hyle

ster

48.6

7O

ctad

ecan

oic

acid

22.6

1O

xalic

acid

,cyc

lohe

xylp

enty

lest

er49

.3Po

lym

er24

.23

2-D

odec

ene

49.4

2Po

lym

er25

.74

Hex

aned

ioic

acid

,dim

ethy

lest

er50

.86

2-N

apht

hale

nam

ine,

N-p

heny

l-25

.87

Benz

othi

azol

e51

.15

Poly

mer

26.4

Neo

deca

noic

acid

52.5

91,

4-Be

nzen

edia

min

e,N

-(1,

3-di

met

hylb

utyl

)-N

’-phe

nyl-

28.7

2O

xalic

acid

,cyc

lohe

xyli

sohe

xyle

ster

52.9

1Po

lym

er30

.55

3-Te

trad

ecen

e53

.05

Poly

mer

31.1

3C

yclo

hexa

nam

ine,

N-c

yclo

hexy

l-54

.61

Poly

mer

32.3

3Q

uino

line,

1,2-

dihy

dro-

2,2,

4-tr

imet

hyl-

56.2

6Po

lym

er32

.94

o-C

yano

benz

oic

acid

58.4

N,N

-Dip

heny

l-p-p

heny

lene

diam

ine

34.9

8D

odec

anoi

cac

id58

.97

N-(

2,4-

dini

trop

heny

l)-1,

4-ph

enyl

ened

iam

ine

35.9

7H

exad

ecen

e

23


Tabl

e7.

Add

itive

sfo

und

byG

C-M

San

alys

isof

the

chlo

rofo

rmex

trac

t.

Tim

e(m

in)

Com

poun

dTi

me(m

in)

Com

poun

d

30.1

32,

2,4-

Pent

anon

e,4-

hydr

oxy-

4-m

ethy

l39

.95

Tetr

adec

anoi

cac

id32

.35

Cyc

lohe

xana

min

e,N

-cyc

lohe

xyl-

40.4

3Be

nzen

e,(1

-eth

ylde

cyl)-

32.9

5Be

nzoi

cac

id,4

-cya

no-

40.9

8N

onad

ecan

e34

.59

Benz

ene,

(1-b

utyl

hexy

l)-41

.37

Benz

ene,

(1-m

ethy

lund

ecyl

)-34

.85

Benz

ene,

(1-p

ropy

lhep

tyl)-

41.6

Benz

ene,

(1-p

enty

loct

yl)-

35.3

9Be

nzen

e,(1

-eth

yloc

tyl)-

41.8

7Be

nzen

e,(1

-but

ylno

nyl)-

36.4

Benz

ene,

(1-m

ethy

lnon

yl)-

42.1

83Be

nzen

e,(1

-pro

pyld

ecyl

)-37

.02

Benz

ene,

(1-p

enty

lhex

yl)-

42.5

31,

2-Be

nzen

edic

arbo

xylic

acid

,bis

(2-m

ethy

lpro

pyl)

este

r37

.13

Benz

ene,

(1-b

utyl

hept

yl)-

42.7

8Be

nzen

e,(1

-eth

ylun

decy

l)-37

.42

Benz

ene,

(1-p

ropy

loct

yl)-

43.6

8Be

nzen

e,(1

-met

hyld

odec

yl)-

38Be

nzen

e,(1

-eth

yloc

tyl)-

44.5

4H

exan

edio

icac

id38

.42

2(3H

)-Be

nzot

hiaz

olon

e45

.25

2-M

erca

ptob

enzo

thia

zole

39.4

2Be

nzen

e,(1

-pen

tylh

epty

l)-50

.86

2-N

apht

hale

nam

ine,

N-p

heny

l-39

.53

Benz

ene,

(1-b

utyl

octy

l)-50

.94

Oct

adec

anoi

cac

id39

.87

Benz

ene,

1-pr

opyn

yl-

24

3G E N E R A L P R O C E D U R E

This chapter describe the general procedure used to carried out and analyzedeach experiment.

Since the aim of this work was to study, trough an experimental design,a devulcanization process which employes supercritical CO2 as carrierfor devulcanizing agent (DD), it was necessary a solvent extractionpretreatment to remove all the chemicals which affect with the experi-mental analyses. Indeed, the rubber used in the present study belongedto industrial market. It have precise and restrictive requirements thatinvolve many treatment, some of which, need additions of chemicaladditives. Thus the first step of the procedure followed for each experi-ment was to carry out a soxhlet extraction with acetone for about 48hours (384 cycles) and another extraction with chloroform for about 12hours (90 cycles) (in according with ISO 1407 and ASTM D 297 standard.More specifically approximately 8-10 g of rubber powder was placedin a cellulose thimble, when the extraction was over, the powder wasdried at 50�C in vacuum for 24 hours. After the purification process,the sample consisted only of rubber and carbon black since it lost allthe extractable compounds during the extraction process and all theacetone still present was removed by vacuum oven.

Therefore, the purified GTR was ready for the treatment. The rubberwas mixed with the right amount of DD, and then treated in scCO2at certain level of temperature and pressure for a specific time, howplanned in the experimental design. After the treatment, the samplewas extracted in acetone for 384 cycles to remove the excess of unre-acted devulcanized reagent. Then the sample was dried again at 50�Cin vacuo for 24 hours. After the cooling at room temperature, it wasready to be analyzed.

Four different analytical techniques were used to analyse the materialafter each treatment and to gain information on the de-vulcanizationmechanism (Figure 13). Every measurements were repeated at leastthree times in order to estimate the standard error. The four responseswere:

C R O S S - L I N K D E N S I T Y ( S W ) : The cross-link density and the estima-tion of the de-vulcanization percentage were carried out accord-ing to ASTM D 6814-02 standard through swelling measurements.

25

G E N E R A L P R O C E D U R E

About 1 g of each sample was weighed and placed in toluenefor 72 hours at room temperature to achieved the equilibrium.At the end of 72 hours each swollen sample was taken out andit was immediately weighed. Then it was dried in vacuo for 24hours and weighed after being cooled to room temperature in adesiccator. The toluene solution with extractable (the extracts)were dried and the remaining solid fraction was weighed. Thecrosslink density was calculated according to the Flory-Rehnerequation [11], taking into account the Kraus’s correction [30].

S O L A N D G E L F R A C T I O N ( S O L & G E L ) : About 1 g of each sam-ple was extracted for 14 hours (110 cycles) in soxhlet with tolueneas solvent to separate the solubile fraction from the gel fraction.Then it was dried under vacuum for 24 hours and weighed afterbeing cooled to room temperature in a desiccator. Instead, thesoluble fraction was dried and weighed.

S U L F U R C O N T E N T ( S ) : the elemental analisys is a process where asample is analyzed for its elemental composition. This analytictechnique is both qualitative, because it can determines whatelements are present and either quantitative because it allows todetermine the weight percentage of each element. In this studythe analysis was only focused on the percentage of sulfur in eachsample.

26

G E N E R A L P R O C E D U R E

Figure 13. General Procedure Scheme

Raw(NR, BR, SBR)

Curing Agents

Compund

Vulcanizate

Grinding

Pre-Treatment(soxhlet extraction)

Devulcanization(SCO2 Treatment)

Post-Treatment(soxhlet extraction)

AnalisysCross-linkDensity

Sol Fraction Gel Fraction

sulfur Content

27

4E X T R A C T I V E A N D A N A LY T I C A L T E C H N I Q U E S

This chapter describes the main extractive and analytical techniques used inthe present study.

4.1 S O X H L E T E X T R A C T I O N

The Soxhlet Extraction is one of the most used techniques to extract anyinterest compound from a solid matrix. Typically, soxhlet extraction isonly required where the desired compound has a limited solubility ina solvent, and the impurity is insoluble in that solvent. In this work thesoxhlet was used with a different application, indeed the compound ofinterest were both the solid fraction and the extractable compounds.

Figure 14. Soxhlet Scheme

The solid material containing some ofthe desired compound is placed in-side a thimble made by thick filterpaper, which is loaded into the mainchamber of the soxhlet extractor. Theextraction solvent is put into a dis-tillation flask and the soxhlet extrac-tor is now placed into this flask. Thesoxhlet is then equipped with a con-denser. The solvent is heated to re-flux and its vapor travels up a distilla-tion arm, and floods into the chamberhousing the thimble of solid. The con-denser ensures that any solvent vaporcools, and drips down into the cham-ber housing the solid material. Thechamber containing the solid mate-rial is slowly filled with warm solvent.The extractable compounds will thendissolve in the warm solvent. Whenthe soxhlet chamber is almost full, thechamber is automatically emptied bya siphon side arm, with the solvent

running back down to the distillation flask. The thimble ensures thatthe rapid motion of the solvent does not transport any solid material

28

4.1 S O X H L E T E X T R A C T I O N

to the still pot. This cycle may be allowed to repeat many times, overhours or days. During each cycle, a portion of the soluble compounddissolves in the solvent. After many cycles the desired compound isclean and the extractable part is concentrated into the distillation flask.

There are several advantage in the use of this techniques: the extractionis easy, there is continuos contact between the sample and the warmsolvent and it is used few solvent because it is recycled. There are alsosome disadvantage due to the organic solvent consummations becausethey are expensive, toxic for people and for environmental and alsothis purification technique requires a long time to complete the process.

Each extraction was carried out by BUCHI EXTRACTION SYSTEM B811(Figure 15), since it is an automatic soxhlet and allows to define a num-ber of cycle and to keep that constant. This allow a better reproducibil-ity. The adjustable optical sensor detects the number of predefinedcycles and controls the magnetic glass valve to release solvent.

Figure 15. Buchi Extraction System B811

The ground rubber needed more Soxhlet Extractions with several sol-vents:

1. Pre-treatment purification extractions:

• The first extraction was carried out with acetone in order toremove the low molecular weight substances. About 7 g ofrubber powder were placed in the thimble in the extraction

29

4.2 S U P E R C R I T I C A L FL U I D T R E AT M E N T

tube. The solvent was heated to boiling point (about 56�C)and the process was run for 48 hours (384 cycles) .

• The second extraction was carried out in chloromethanesfor 12 hours (90 cycles) in order to reach the total extractionof extractable compounds.

2. After the treatment, in scCO2 with DD, the sample needed an-other extraction in acetone to remove the excess of unreacteddevulcanized reagent. Also in this case the process was run for48 hours (384 cycles) .

3. After the last extraction a portion of each sample was extractedin toluene as solvent to separate the soluble fraction from the gelfraction by extraction of 14 hours (110 cycles).

The two solvents for pretreatment are in according with ISO 1407 andASTM D 297 standard, in order to reach a total extraction. They werechosen due to their higher solubility in comparison with NR and BR.To achieve the constant weight, after each extraction the sample wasdried in vacuum for 24 hours.


The SFE is one of the most recent and innovative chemical techniques.The use of Super Critical Fluid (SCF) as solvents lets, in many cases,to reduce time, cost and hazard than the traditional techniques withtraditional solvents. For this reasons, SCF are widely used , in this workSupercritical Carbon Dioxide (scCO2) was used,whit DD, to optimizedthe devulcanization process in GTR.

Figure 16. Supercritical state

(a) Pressure-Temperature Diagram (b) Pressure-Density Diagram

30


The supercritical state is a particular state of aggregation in which thereexist no separation between liquid and gas phases. This state can reachby any substance when it is at a temperature and pressure above itscritical point. In Figure 17a is shown the Pressure-Temperature phasediagram, where the boiling line separates the gas and the liquid regionand ends in the critical point, where liquid and gas phases disappearto become a single supercritical phase. This phenomenon can be alsoobserved in Figure 17b where the Density-Pressure phase diagram forcarbon dioxide is shown. At well below the critical temperature, e.g.,280�K, as the pressure increases, the gas compresses and eventually(at just over 40 bar) condenses into a much denser liquid, resulting inthe discontinuity in the line (vertical dotted line). The Supercriticalfluid system consists of 2 phases in equilibrium, a dense liquid anda low density gas. As the critical temperature is reached (300�K), thedensity of the gas at equilibrium becomes denser, and that of the liquiddecrease. At the critical point, (304.1�K and 73.8 bar) the density doesnot show any more difference, and the 2 phases become one fluid phase.Thus, above the critical temperature a gas cannot be liquefied by pres-sure. At slightly above the critical temperature (310�K), in the vicinityof the critical pressure, the line is almost vertical. A small increase inpressure causes a large increase in the density of the supercritical phase.

Many other physical properties also show large gradients with pres-sure near the critical point, e.g. viscosity, the relative permittivity andthe solvent strength, which are all closely related to the density. In ad-dition, there is no surface tension in a supercritical fluid, as there is noliquid/gas phase boundary. By changing the pressure and temperatureof the fluid, the properties can be ”tuned” to be more liquid- or moregas-like.

Solubility in a supercritical fluid tends to increase with density of thefluid (at constant temperature). Since density increases with pressure,solubility tends to increase with pressure. Given that in this workscCO2 was used as carrier to transport DD into the rubber.

Compared to a classical liquid solvent extraction, the use of a supercrit-ical fluid with similar solvating power will be performed in a shortertime, thanks to the better transport properties. In addition, the greaterselectivity for certain types of analytes often makes them preferable tothe common liquid solvents. In Table 8 is show the critical values forsome common used compounds.The choice of the fluid to use in supercritical fluid treatment is mainlylinked to practical and technological aspects of the instrumentationavailable and of the extractable matrix. Among several supercriticalfluids, CO2 is the most advantageous for the current purpose because

31


Table 8. Critical values for some fluid used as Supercritical Fluid

Fluid Critical Pressure (bar) Critical Temperature (�C)

CO2 73.8 31.1H2O 221.2 374.3NH3 112 132.2

it is chemically inactive, nontoxic, nonflammable, inexpensive andwidely available [27]. Moreover at the atmospheric conditions it isat gaseous state and this allows, when the process is completed, toresidual CO2 in the polymer matrix to be easily and rapidly removedby releasing pressure. Therefore the residual of scCO2 return to gasphase and evaporate leaving little or no solvent residues. Furthermore,CO2 has an easily accessible critical point (the critical temperatureand the critical pressure are 31.1�K and 73.8 bar, respectively) [27, 33].The Figure 17 shows some frames which illustrate the sequence of thetransition from CO2 to scCO2.

Figure 17. Transition from CO2 to scCO2 (by NASA)

For the reason previous listed CO2 is the most fluid chosen as superciti-cal fluid [19, 25, 48]. The numerous advantages of this type of extractionmake scCO2 usable for large scale process [3] as the decaffeination ofgreen coffee beans, the extraction of hops for beer production, and theproduction of essential oils and pharmaceutical products from plants.For research purposes the apolarity of CO2 makes it unsuitable forsome applications, but it can be overcome with the add of a percentorganic polar solvent (modifier). Among the most used are methanol,toluene and water.

The most simple scheme for a SFE is based on the usage of one ore twosyringe pumps for compress the CO2 and if is necessary for compressa modifier. The extractor fluid is exposed to a compress and pumpingphase, which allows its transportation into a thermostated chamberwhere is placed the cell containing the sample. The cells can be makewith several materials depending on the application, and it can have acapacity between 1 and 100 mL. The collection of extract output sampleis performed using some restrictors at the end of pressure line, theyconsisting in a silica or metal capillary immersed in a small amount

32


of solvent. The volatile analytes which may be removed due to therapid expansion of CO2, are often captured by using trap. The essentialmodel of SFE is shown in Figure 18.

Figure 18. Essential model of SFE

The reaction process can work only in static condition, when the cellreach the determinated pressure the flow work only to mantein thework pressure. The interaction between fluid and GTR is substantiallydue to a increase of solubility of CO2 and it let the swelling of therubber. The efficiency of this mechanims is dedeterminated trought thechosen of the process paramiters as pressure, temperature, time and therelationship between weight of rubber and weight of DD . Thanks to acell the volume of fluid is maintained constant during the treatment.

SFE experimental plant

The instrument used was a industrial prototype for Supercritical FluidExtraction (SFE) µ–plant (Figure 19), it uses CO2 as extraction fluid.The plant was designed to be use with extraction cells with capacityup to 100 mL, so it is possible to process significant amounts of sample.

The main difference between SFE µ–plant and the other commercialinstruments is the cell capacity, because generally in the common SFEplants are possible to process only a volume from 1 to 10 mL. For thisreason this industrial prototype has a complex system of parametersregulation and is characterized by an high operation range (Pressure:from 73.6 bar to 690 bar and Temperature: from 31.1�C to 400�C).The inferior limits corresponds to the CO2 critical parameters, underwhich the carbon dioxide is not in the supercritical state, while themaximum working conditions are determinate by fluid dynamics com-ponents and by the technological solution adopted.

33


Figure 19. Overview of SFE µ–plant

The extractor pressure is generated by a double couple of syringe pumpISCO DX100, they can provide fluid under pressure until 690 bar andthey have capacity slightly higher than 100 mL; The first pump is dedi-cated to the CO2 while the second one is dedicated to for an eventuallymodificatory. To avoid a phenomenon of reverse flow there is a pres-ence of check valves in the line-out because this phenomenon couldpollute the pumps and prejudice the integrity of the same. The Man-agement of the pump is supervised by the panel control ISCO SX200which controls is possible control the recharg and the pressurization.The devulcanization process occurs into the extraction cell, where isplaced the sample to be treated. For this work the experiments wascarried out using Q-Ation reaction cell (Figure 20), made in Titaniumby 50 ml capacity which can work until 690 bar and 400�C.

During each experiment the operating temperature is reached by aforced convection oven, Carbolite LHT5/30. It is equipped with a wideheating chamber, where is placed the extraction cell. Inside at the oventhere is also part of the dynamics fluid components and control. Atthe end of the process line is placed a manual restrictor called ISCOAdjustable Restrictor, thought which it is possible to modular the exitflow. In this work the restrictor was used only for modular the exitflow and it wasn’t use also for recover the exhausted solvent.The dynamics fluid components is composed by hoses, fittings, rupturediscs, tested safety, two and three-way needle valves, line filter, checkvalves and ball valves. All these components are made of stainlesssteel AISI 376, with the exception of some elements of mechanical sealsand gaskets, which are made of Grafoil, Teflon, Peek or EPDM. Thetubing used is from 1/8” with internal passage from 5/100”, whichmakes it able to withstand an internal pressure of 60000 psi at room

34


Figure 20. Q-Ation Reaction cell

temperature. The used of a reduce internal passage lets to reduce thedead volume of plant with resulting saving of the extraction fluid andat the same time it lets to support the final cleaning operations. Theinterconnection system is composed by conical seals that guaranteefew losses than Ogiva System and it is usable also in safe with highpressure systems.

The existing plant includes also a system of regulation and controlof critical process parameters, such as pressure and temperature. Ifthe maximum operating conditions are exceeded this control systemstart up alarms on the control panel, the emergency system provides tointerrupt power to the oven, activate the cooling fan, and to open thevent valve to the overpressure lung.

Process line

The CO2 storage tank is connected with G1 pump by a stainless steelflexible tubing, it is PI1 provided with: pressure gauge, check valveCV4 and vent valve NV1. The loading of modifier occurs by G2 pumptrough stainless steel flexible tubing immersed in the solvent (checkvalve NV2). The solvent lines join in a three-way union (where startsthe mixing of the extraction fluids) which goes until the needle valveNV5. Downstream of this valve, inside of the oven B1, is placed theextraction cell C1. At the exit, the line goes towards the cooling coilsE2. From here the line continues to the filter line LF1, whose task is toprevent the damage of the follow components caused by corpusclesmatrix eventually escaped from C1. Therefore, the extraction fluidreaches the three way/two pressure valve NV7, if the pressure is too

35

4.3 S O LV E N T E X T R A C T I O N T O E S T I M AT E C R O S S - L I N K D E N S I T Y

high, it provide to vent overpressure lung D1. In working conditions,instead, the line goes towards the ball valve BV1 from which it arrivesin an end fitting, where the fluid can continues towards the needlevalve NV9, which precedes the vent open-loop system (under a hood),or can goes to the needle valve to which is connected the NV8 restrictor,and finally the recovery system of the analytes (trap).

In this application, for each sample, about 7 g of GTR was weighed andmixed whit the correct ratio of DD and placed into a quartz storage.The storage cell was placed into the cell with some glass beads usedto reduce the internal volume. Then the cell was placed into the ovenand it was connected at the system. While the temperature was raisingthe pressure was slowly increased until the system had not reached thetreatment conditions; only at this moment the time of treatment wasstarted.


Each sample was extracted in toluene. This extraction allowed to esti-mate one of the experimental response of this work: the SW. Cross-linkDensity represents the number of moles of sulfur cross-link per unitvolume of the rubber, this is the most important structural property ofa vulcanized rubber [1]. This property is based on the determination ofthe absorption of a proper solvent and subsequent swelling of the rub-ber . The cross-link density and the estimation of the de-vulcanizationpercentage were carried out according to ASTM D 6814-02 standardthrough swelling measurements. Toluene was chosen as a solvent con-sidering the solubility parameter.

To measure the swelling and consequently the cross link density, about1 g of each sample was weighed (initial weight, Wini) and placed intoluene for 72 hours at room temperature, and any 24 hours the sol-vent was changed and stored for further measurements. At the end of72 hours the swollen sample was taken out and the excess liquid onthe specimen surface was removed by absorption paper. The swollenweight (Ws) was immediately measured with utmost care. After thisstep, the swollen sample was dried in vacuo for 24 hours and weighed(Wd) after being cooled at room temperature in a desiccator. The toluenesolution with extractables was dried with BUCHI ROTAVAPOR R-2001

and the remaining solid fraction was weighed. To obtain an accu-rate estimation of cross-link density for any sample the the swellingmeasurement was carried out in triplicate.

1 The Rotavapor is an instrument used to remove solvents form a solution by evapora-tion at low pressure.

36

4.3 S O LV E N T E X T R A C T I O N T O E S T I M AT E C R O S S - L I N K D E N S I T YFi

gure

21.P

lant

Flow

Shee

t

37


Figure 22. Buchi Rotavapor R-200

The crosslink density X was calculated according to the Flory-Rehnerequation [11], it is based on the statistical theory and the thermody-namics of mixing of a solvent with rubber network chains:

X =�

hln

�1 � vrg

�+ vrg + cv2

rg

i

2Vov1/3rg

(1)

The interaction parameter between rubber and the swelling solvent,c = 0.39, was chosen considering NR as the main polymer and tolueneas solvent. The density of rubber was approximated to the density ofthe NR, considering the content of sulfur and it was 0.92 g/cm3. V0 is,instead the molar volume of the solvent (106 mol/cm3 for toluene).

The Flory-Rehner equation is, strictly spoken, valid only for non-filledsystems. Because this equation don’t take into account the contentof carbon black. CB strongly adheres to the rubber and cannot beremoved during swelling thus the rubber has less space to swell [1]. Inthis work the samples were composed by GTR so there was presence ofthe filler. The content of CB (Wc) was determined by TGA and it was33 wt.% of Wini. The CB density was chosen equal to 1.85 g/cm3 andthe universal constant C was chosen to be 1.17. Kraus [30] suggested asemi-empirical correction to account for the restriction of swelling dueto the presece of the filler compound. Using the equation 2 it becamepossible to calculate vrg, a parameter referred to the volume fraction ofpolymer in the swollen mass as if the swollen sample was composedonly by rubber without fillers.

vrg

vr f=

1 � V �h3c

⇣1 � v1

rg/3⌘+ vrg � 1

iV

1 � V(2)

38

4.4 S O L A N D G E L F R A C T I O N

Where vr f is the volume fraction of the rubber in the swollen rubberphase (excluding carbon black), it was calculated adding the volumesof the rubber to toluene as in Equation 3.

vr f =

Wd � Wc

0.92Wd � Wc

0.92+

Wt

0.87

(3)

In the equation 3, 0.92 and 0.87 are the densities of the natural rubberand toluene in g/cm3, respectively. The weight difference between Wsand Wd gives the weight of the toluene absorbed during swelling, Wt.The weight difference between Wini and Wd yields the total amount ofoil and soluble rubber extracted during swelling.

4.4 S O L A N D G E L F R A C T I O N

About 1 g of each sample (W1) was extracted for 14 hours (110 cycles)in soxhlet with toluene as solvent to separate the soluble fraction fromthe gel fraction. The toluene solution containing the sol componentand the insoluble product containing the gel component. Then the gelfraction was dried under vacuum for 24 hours and weighed (W2) afterbeing cooled to room temperature in a desiccator. Instead, the solublefraction was dried by using rotavapor and weighed (W3).

The sol fraction and the gel fraction were respectively explained byEquation 4 and 5 [28]:

Sol =✓

W2

W1

◆⇤ 100% (4)

Gel =✓

W3

W1

◆⇤ 100% (5)

4.5 E L E M E N TA L A N A LY S I S C H N S

This analytic technique is both qualitative, because it can determineswhat elements are present and either quantitative because it allows todetermine the weight percentage of each element. In this study theanalysis was only focused on the percentage of sulfur in each sample,because this information allowed to understand, for each sample, howstrong was the devulcanization reaction. During the devulcanizationreaction the radicals formed by scission of the disulfide can give addi-tion to the double bonds in natural rubber and one atom of sulfur isbounded to the polymer chain [33]. So more strong is the devulcaniza-tion reaction more high is the percentage of sulfur in the sample.

39


The Perkin Elmer 2400 Series II CHNS/O Elemental Analyzer (Figure23) was used in the present study. It uses a combustion method toconvert the measured elements (C, H, N, S, O) to simple gases. Thesegases are mesured as a function of thermal conductivity. A knownstandard is first analyzed to calibrate the instrument. The calibrationfactor is then used to determinate unknowns sample. Thanks to theweighing of sample the instrument provides as a result a percentageby weight of each element.

Figure 23. Elemental Analyzer Perkin Elmer 2400 Series II

The system uses a steady state, wave front chromatographic approachto separate the measured gases. This approach involves separating acontinuous homogenized mixture of gases through a chromatographiccolumn. As the gases elute, each gas separates as a steady-state step,with each subsequent gas added to the previous one. Consequently,each step becomes the reference for the subsequent signal.

In the Figure 24, the nitrogen signal is equals at the difference betweenthe Nitrogen Read and Zero Read; this is valid also for the CarbonSignal that is equals at the difference between the Carbon Read minusthe Nitrogen Read and so on for Hydrogen and Sulfur Signals.

Before carry out one analysis it was necessary calibrate the machinewith the calculation of k factor and blank. In particular, K f actor ordetector calibration factor, is determined when a know standard isanalyzed to calibrate the analyzer in terms of micrograms of carbon,hydrogen, nitrogen and sulfur. This calibration factor is then used todetermine unknowns samples. In this cases the used standard is NCSFC 28112 which has the following composition: C 78,64%, H 5.01%, N1.31%, S 2.10%.

40


Figure 24. Separation of gases for CHNS determinations

The associated formulas2 for calculating each of the four K f actor (ex-pressed in counts per micrograms) are as follows (Table 9).

Table 9. K factors calculation

Nitrogen K factor (NKF) Hydrogen k factor (HKF)

NKF = [(HR�CR)�HB]⇤100SW⇤HTheory Wt.% HKF = [(HR�CR)�HB]⇤100

SW⇤HTheory Wt.%

Where: Where:NR= Nitrogen Read HR= Hydrogen ReadZR= Zero Read CR= Carbon ReadNB= Nitrogen Blank HB= Hydrogen BlankSW= Sample Weight (µg) SW= Sample Weight (µg)

Carbon K factor (HKF): Sulfur k factor (SKF)

CKF = [(CR�NR)�CB]⇤100SW⇤CTheory Wt.% SKF = [(SR�HR)�SB]⇤100

SW⇤STheory Wt.%

Where: Where:CR= Carbon Read SR= Sulfur ReadNR= Nitrogen Read CR= Hydrogen ReadCB= Carbon Blank SB= Sulfur BlankSW= Sample Weight (µg) SW= Sample Weight (µg)

The Blank values were used to reach the necessary correction for thedetermined elements. They were also used to determine very lowconcentrations, or to determine the absence of an element. Blank runswere performed by running the empty tin vials through the analyzer.When blanks run alternately with samples, the system averages theblank values. For CHNS analysis, appropriate definitions for a blankrun are essential and it is defined as follows:

2 In the K factor calculations below, the sample weight (SW) of the analytical standardis expressed in micrograms.

41


Nitrogen Blank(NB) = Nitrogen Read � Zero Read (6)

Carbon Blank(CB) = Carbon Read � Nitrogen Read (7)

Hydrogen Blank(HB) = Hydrogen Read � Carbon Read (8)

Sul f ur Blank(SB) = Sul f ur Read � Hydrogen Read (9)

In this study, all samples were dried at 45�C prior to analysis. Eachsample was placed in a large tin capsule and precisely weighed usinga Perkin Elmer AD6 Autobalance. Samples ranged in weight from 10to 80 mg. To ensure complete combustion was achieved, the followingoptimized combustion conditions were studied and are shown in Table10.

Table 10. Optimized combustion times for soil analysis.

Optimize Combustion Time (Sec.)

Oxygen Full 1Combustion Time 10Oxygen Boost 1 1Oxygen Boost 2 0

42

5T H E O P T I M I Z AT I O N O F T H E P R O C E S S

The aim of this chapter is to explain the experimental planning andexperimental design principles. Indeed, showing how is possible toobtain the maximum information with a minimum number of theexperiments.

The Experimentation involves areas concerning applied and industrialresearch and it regards several situations as the development of newproducts, the setup of methods and procedures, the optimization ofchemical reactions and so on. However, the experimentation is not thecharacterizing element of the science and it does not have any crucial orprivileged role [43]. Indeed it is part of a theoretical system. It involvesone or more theories, hypothesis, concepts, definitions, proceduresand all of them are referred to a well-defined technical-mathematicallanguage. The experimentation can be interpreted only within thetheoretical system in which it are defined.

The experimental planning allows to reach several goals and it allowsto define and understand in details one problem. The experimenter’saim is to obtain, through experimentation, the best obtainable informa-tion about the target, with the minimum number of experiments, inthe shortest time and at the lowest costs [47]. The experiments shouldbe planned keeping into account the previous knowledge about theprocess under studying, the target to achieve and the available instru-mentation.

The set of experiments has meaning only inside a model, which iscomposed by:

FA C T O R S , or variables (x), which change under control in order tostudy their effects on the process.

T H E E X P E R I M E N TA L D O M A I N , a subset of points in the space offactors, where it is possible to carry out an experiment. The limitsof this domain are the extreme values of factors.

T H E R E S P O N S E F U N C T I O N is a mathematical model that correlatesfactors and response. It represents the characteristics of the pro-cess under studying (y).

The methodology of experimental research can be divided into twophases. The screening represents the first one: the variables that have

43

5.1 A N A LY S I S O F T H E P R O B L E M

more influence on the process, are investigated together within theirmost appropriate experimental domain. In this phase some prelimi-nary experiments are performed to find the significant factors. Theoptimization is the second phase. In this case, the optimal conditionsfor the process are investigated, defining if there is a unique optimumor if it is necessary to find a compromise to satisfy several targets.

As described in figure 25, the fundamental steps of screening phaseare:

• Analysis of the problem,

• Planning of experiments,

• Carrying out of experiments,

• Statistical treatment of the data and analysis of the statisticalresults,

• Return to step one.


This first step can be developed before performing experiments, assoon as some information from preliminary experiments are obtained,or just considering prior knowledge about the phenomenon understudying. The analysis of the problem is made up of some additionalsteps:

• Definitions of response functions: this step consists of establish-ing some suitable experimental measures to provide the necessaryinformation to solve the problem. Some examples are: the yieldof a chemical reaction or the accuracy of an analytical method. Inthe present study, the chosen responses were: swelling, sol andgel fraction and sulfur content.

• Objective definitions: this step allows to define the desired trendfor the experimental responses; for example the percent of swellingmust be maximized.

• System analysis: which factors can affect the response? Thefactors can be a continuous variable as the temperature, or dis-continues as the solvent A or the solvent B. In this phase theformulations of any hypothesis about relationships among re-sponses and factors take place. If for theoretical reasons it iscertain that one factor has not any influence on the responsesthis factor will not be taken into account, otherwise it has tobe included. In this work, the factors taken into account were:

44


Figure 25. The fundamental steps of screening phase

Analisys of the problem

Definitions of response functions

Objective definitions

Definitions of the constraintson the experimental responses

System analysis

Definition of experimental domain

Definition the relationship betweenfactors and responses

Planning ofthe experiment

Execution ofthe experiments

Statistical treatment ofexperimental data and

results analisys

45

5.2 P L A N N I N G O F T H E E X P E R I M E N T

temperature, pressure, amount of the devulcanizing reagent andreaction time.

• Definition of experimental domain: it is necessary to analyze anyfactor that has influence on each response. The experimental do-main is defined taking into account both the previous knowledgeregarding the relationship between responses and factors and therange for the factors. For instance, in this work, the pressure canonly assume as minimum value 70 bar, in order to satisfy the su-percritical condition [28]. Then the domain of each factor is scaledbetween +1 and -1 that represent respectively that maximum andminimum values that the factor can assume. This procedure iscalled scaling of variables [2].

• Definition the relationship between factors and responses: thisstep consists in the choice of the best model to describe the re-lationship between factors and responses. The most commonlyused models are the linear, the quadratic or the logarithmic ones[47].

5.2 P L A N N I N G O F T H E E X P E R I M E N T

This phase consists in defining and listing the experiments. In eachexperiment the values of any factors are specified in order to assess theexperimental responses in several specific conditions. The experimentalplanning cannot be random but it is necessary to use mathematicaland statistical techniques to get a suitable model. Example are full andfractional factorial designs, Plackett-Burman designs, Doehlert designsetc..[2].

5.3 M O D E L Q U A L I T Y

Model quality consists in two terms: the experimental error and theerror due to the experimentation planning.

• The experimental error is the error due to the analytical pro-cedures and instrumental analysis; this represents an intrinsiccharacteristic of each instrument and of any procedure test. Itcan be partially avoided performing some replications. To reducethe experimental error it is necessary to use procedures that guar-antee maximum reproducibility. An uncontrolled variation, of anot considerer factor, is random, this is one of the most impor-tant assumption in the experimental planning. However, duringmeasurements, the change in an uncontrolled factor as humidity,deterioration of a reagent or the damage of the instrument give asystematic deviation in the results. The experimental error should

46

5.4 F U L L FA C T O R I A L D E S I G N

be independent of variation due to uncontrolled factors. The riskof having side effects can be limited using the randomization ofthe experiment [2].

• The error due to experimentation planning the experimentationplanning is important both to avoid unnecessary experiments andboth to chose only the experiments that contain all the necessaryinformation.

In the present study, a full factorial designs was chosen, among the mostcommon methods, for the experimentation planning in the screeningphase [46]. The purpose of a full factorial design is to estimate, ascorrectly as possible, which may be the influence of the factors variationon the response. By using this model it is also possible to estimate theinfluence of the simultaneous change of two or more factors.


The two-level full factorial design corresponds to a balanced and or-thogonal arrangement of experiments [2, 36, 47]. This arrangementenables to assess the effect of one factor independently of all the others.In the full factorial designs each factor is investigate at fixed levels. Withk investigated factors the two-level full factorial design has N = 2k

runs. It have to be observed in this way neither replicated experimentand central point are taken into account. For instance, if the designconsists in two levels, the variables can be assumed only two values,the minimum and the maximum.

In the present study it was chosen a design with four factors (time, tem-perature, pressure and the ratio between DD and GTR) and two levels,so sixteen experiments were carried out to investigate the experimentaldomain. Three experiments were added to investigate the performancein the center of the experimental domain and to estimate the modelvalidity, the reproducibility and the experimental error. Table 11 showstreatment conditions, these were chosen considering the type and theproperties of the rubber tire material [4, 24, 27], the maximum operat-ing level for the equipment (400�C and 70 bar), and the supercriticalconditions for CO2 (31.1 �C and 73 bar). A fully randomized executionof the nineteen experiments was carried out in order to minimize theerror due to the planning of the experiments.

E FF E C T S C A L C U L AT I O N

In the full factorial designs, the experiments are organized in order toensure that each experimental response is used more than one time to

47


Table 11. Experimental Planning

Sample Time DD/GTR Temperature Pressure(min) ( %) (�C) (bar)

C0 150 0.13 130 160C1 150 0.13 130 160C2 150 0.13 130 160

P3 60 0.01 70 80P4 60 0.25 70 80P5 240 0.01 70 80P6 240 0.25 70 80P7 60 0.25 70 240P8 60 0.01 70 240P9 60 0.01 190 80

P10 60 0.01 190 240P11 60 0.25 190 240P12 240 0.01 70 240P13 60 0.25 190 80P14 240 0.01 190 80P15 240 0.25 70 240P16 240 0.01 190 240P17 240 0.25 190 80P18 240 0.25 190 240

calculate the effects of the factors. The effects that can be divided asfollows:

• The medium effect

• The main effects (effects of original factors x1, x2, ..., xn)

• The effects of interactions at two factors (the synergistic or antag-onist effects between two factors)

• The effects of interactions at three factors (the synergistic or an-tagonist effects between three factors)

• The effects of interactions due to all factors

In the real phenomena the probability to have significant effects de-creases with the increase of the interactions order [2, 43] . However,it is always necessary to evaluate if this assumption is realistic in thecontext of the studied problem.

Medium effect

Is the average of the response considering any experiments carried out.

48


Main effects

Each factor, defined in the experimental matrix, is orthogonal withthe others, thus they results independent among themselves and it ispossible to calculate their individual effect. The main (average) factoreffect is defined as the change in the response due to varying one factorfrom its low level to its high level, and keeping the other factors at theiraverage (Equation 10) [9] . Figure 26 shows the main effect of time onthe sol fraction response.

Main Effect = Yhigh level o f variable � Ylow level o f variable (10)

Figure 26. Main effect of temperture on the Gell fraction response by Modde®

The general symmetry of the design ensures that there is a similar setof measures for any other effects. Notice that all the observations areused to estimate the main effects. To obtain equal precision, a OVAT se-ries of experiments would have required sixteen runs for each factor [2].

In the OVAT method , the variables that could possibly affect the per-formance of the process are maintained at a fixed level except for one,which is varied until the best conditions are reached. This approachprovides only the evaluation of the effect of a single factor keepingfixed conditions of the other factors [2, 47].

Interaction effects

Obviously the main effects are an incomplete description of the influ-ence of any factor on the response. Indeed, the evaluations of interac-tion effects allow to point out the presence of significant addictive orantagonist effects between factors.

When the main effects were calculated, the treatments were consideredto be in two halves with the factors at their lower levels in one half andthe higher levels in the other. Having made this division, the responseobtained for these treatments were averaged and the average at lowerlevel subtracted from the average at higher level. It is, also, possible to

49

5.5 M U LT I P L E L I N E A R R E G R E S S I O N

consider any interactive effect in the same way. Indeed each interactiveeffect may be calculated from a difference between two averages:

• Half of the sixteen in one average, which can be considered asthe lower level for that interaction.

• The remaining treatment will thus make up the other half, whichcan be considered as the higher level for the interaction [49].

Graphically, the interactions effect represent the trend of the responsein the diagonal planes.


The full factorial design allows to calculate the factors effects manuallybut typically the computing of them is carried out by using the leastsquares analysis. This method has a number of advantages [9], notably:

• The tolerance of sight fluctuations in the factor settings,

• The ability to handle a failing corner where experiments couldnot be performed,

• The estimation of experimental noise,

• The production of a number of useful model diagnostic tools.

When least squares analysis is applied to the modeling of several factorsit is commonly known as MLR [9]. Using MLR, the dependence of eachexperimental response, yi , on the factors was modeled applying theequation 11:

yi = b0 +k

Âi=1

(bi ⇤ j) + # (11)

where i represents the i � th experiment, b0 is the constant term, k is thenumber of the parameters (factors and their interactions) in the model,bj is the j-th variable coefficient and jij ij is the i � th factor value and #is the error.

Theoretically, in multiple-regression analyses there are no limitationsto the number of independent variables (ui) that can be proposedas influencing the dependent variable (y). In this case the extendedformula is the following:

y = b0 + b1 ⇤ x1 + ... + b12 ⇤ x1 ⇤ x2 + ... + bn ⇤ x1 ⇤ x2 ⇤ xn (12)

In matrix form, the model calculation is made according to the equation13:

b =�XTX

��1 ⇤ XTY (13)

50


where b is the vector of coefficients, X is the matrix model, XT is itstranspose matrix and Y is the respose vector. In particular (XTX)�1 isknown as dispersion matrix, the diagonal contains the multiplicativecoefficient, which multiplied for the experimental error provides anestimation of the variance of the model coefficients. In the full factorialdesign the contribution to the uncertainty of the coefficients is equal forany factors and each interactions and is 1/n where n is the number ofthe experiments. The off-diagonal elements represent the experimentalerror for the calculation of the covariance between the coefficients ofdifferent factors. The covariances are all equal to zero. This charac-teristic of full factorial design is due to the orthogonally of the factorsand interactions. Therefore using this method the uncertainty of thecoefficients is the same for any coefficients and is the minimum possible[43].

In the present study the regression model corresponds to a twisted in a5 � dimensional space formed by four factors and one response (MLRgenerates a singular model for any response). This hyper-plane is fittedin order to minimize the sum of the squares residuals, that representsthe gap from the experimental points to the hyperplan.

An important consequence of the MLR is that the outcome is not mainand interaction effect estimates, but a regression model consisting ofcoefficients reflecting the influence of each factors and interactions. Inaddition to the numerical values it is convenient to display the regres-sion coefficients in bar charts as represent in the Figure 27.

Figure 27. Scaled and Centered Coefficient for SW model

Once the factors effects have been calculated, it is possible to evaluatewhich among these are relevant. In other words, it results necessary todefine which are the variable, or the interactions, that really contributeto give the shape of the response function. Only with these results itbecame possible to create the final model and to analyze its quality andvalidity.

51

5.6 S TAT I S T I C A L T R E AT M E N T O F R E G R E S S I O N


Once calculated the coefficients of the regression model some statisti-cal parameters are evaluated to describe the quality of the regressionmodel. The most used parameters are described following.

TSS =n

Âj=1

�yj � y

�2 (14)

where the summation runs on all the n objects, yj is the i-th experimen-tal response and y is the average of experimental resposes. Total Sumof Squares (TSS) is characterized by n � 1 degrees of freedom.

The Model Sum of Squares (MSS) represent how well the model de-scribe the data being modelled:

MSS =n

Âj=1

(yi � y)2 (15)

where the summation runs on all the n object, yi is the j-th responsecalculated by the model and y is the average value of experimentalresponses. In particular MSS represents the variability of the modelledvalues and it can be compared to TSS, which measures the variabilityin the observed data, and to the RSS, which measures the variabilitythe model errors. The Residual Sum of Squares (RSS) is shows in thefollowing equation:

RSS =n

Âj=1

�yj � yj

�2=

n

Âj=1

= e2j (16)

where n is the number of the runs , yi is the j-th response calculatedby the model, yj is the j-th experimental response and e is the differ-ence (error) between the experimental and the calculated values forthe j-th sample. The RSS parameter is characterized by n � p degreesof freedom (where p is the number of parameters with the intercept).The MLR minimizes exactly RSS. A value near to zero means that themodel is a perfect model, meanwhile a value of RSS comparable withTSS represents a completely inadeguate model.

One fundamental assumption of MLR is that the independent variables,x, are estimated without error since the error is only due to the modelresponse [43]. Otherwise if the variables are affected by significanterrors, it is necessary to use other regression methods, as the pooledregression.

52


One other relevant parameter is the Lack-of-fit Sum of Squares (SSLOF)which represent the variability imputable to the inadequacy of themodel:

SSLOF =L

Âi=1

ni

Âj=1

�yij � yj

�2 (17)

where i represents the number of runs for the L levels, L is the numberof experimental conditions in which the experiments are carried out, niis the number of replicates done for each level and yj is their average.The lack of fit is characterized by L � p degrees of freedom.

As oppose to SSLOF ,the pure Error Sum of Squares (SSPE) represent thevariability imputable to the pure experimental error (Equation 18).

SSPE =L

Âi=1

ni

Âj=1

�yij � yj

�2 (18)

where i runs on all the L levels, that is the number of experimentalconditions in which the experiments were carried out, ni is the numberof replicates done for each level. This parameter is characterized byn � p degrees of freedom.

The following equation 19 shows an important consideration, indeedthe RSS is composed by the sum of pure error and the error due to lackof fit:

RSS = SSPE + SSLOF (19)

The degrees of freedom are:

d fRSS = (n � L) + (L � p) = n � p (20)

All the sums of squares (SS) defined above can be transformed intovariances (MS), dividing the sums for the corresponding degrees offreedom.

It is also fundamental make an assessment of quality for the modelbefore doing conclusions on the role played by each variable. For thispurpose, the parameter most common used to evaluated the quality ofa regression model is the R2 , which is obtain by the quantities definedby equation 21:

R2 = 1 � RSSTSS

(21)

R2 represents the variance in fitting express by the regression model. Inother words, it describes how much the model fit the experimental data.A value of R2 close to one indicating a perfect model, because it can

53

5.7 M O D E L S VA L I D AT I O N A N D T H E P R O B L E M O F O V E R - FI T T I N G

describe the one-hundred percent of the variability of the experimentalresponse, while a value of R2 equal to zero indicates a model where theresiduals have the same order of magnitude of TSS.

The determination coefficient cannot decrease as the significant com-ponents rise. This means that the R2 value remains constant, or growswhile in the model is additioned a variable, also if this one does notaffect the response.

One alternative parameter widely used to evaluate the quality of aregression model is:

R2adj = 1 � RSS/(n � p)

TSS/(n � 1)(22)

In this formula, the RSS and TSS are divided by their degree of freedom.Therefore, the addition of a meaningless variable penalizes the value ofadjusted determination coefficient in fitting (R2

adj). Both R2 and R2adj

describe the model capacity to fit the experimental data.

An additional index commonly used is the standard error; calculate byRSS: is shown in equation 23. This is a measure of the deviation of themodel from the experimental data.

sy =

sRSS

n � p(23)


Cross validation is useful for overcoming the problem of over-fitting.Over-fitting is one aspect of the larger issue of what statisticians refer toas shrinkage [14]. Over-fitting is a term used when the model requiresmore information than the data can provide. It generally occurs when amodel is excessively complex, such as too many parameters relative tothe number of observations. When overfitting bases the models, theygenerally show poor predictive performance. Prediction error refers tothe discrepancy or difference between a predicted value (based on amodel) and the actual value. In the regression model, prediction errorexplains the ability of the model to predict the outcome of new cases.

The explained variance in fitting (R2) always increases when the sig-nificant components rise, otherwise a grow of significant componentsinvolves an enhancement of explained variance in prediction till a pointwhen any increase of the components considered, involves a deteri-oration in the quality of the model itself (Figure 28). This inflectionrepresents the maximum model prediction power. Further additions in

54


components will therefore only negative affects the model. This meansevaluate the predicting power is better than simply evaluating R2 be-cause it does not indicate how well a model can make new predictionson cases it has not already seen.

Figure 28. Difference between R2 and Q2

This mean the structure of the model needs always to be tested by vali-dation techniques, which the presence of overfitting. The deteriorationin the quality of the model could be caused by correlation, experimentalnoise, specificity of the sample ecc. . . In general the model validationoccurs with a precise logic. A portion of the overall data is used toconstruct the training set, while the other part is used to build theevaluation set. The training set is used to create a reduced (simplified)model which thereafter is used to predict the response of the object ofthe evaluation set. The totality of the predictions is use to computingone or more parameters that lead to evaluate the predictive capacity offinal model.

Several cross-validation techniques exist. They are different mainlyfor two reason: the former for the modality wherewith the objects aredivided into training and evaluation set and the latter for the numberof training and evaluation set that are generated. Among the mostcommon methods to evaluate the prediction goodness of the model,the leave one out method is used with experimental designs that havefew experiments.

The leave one out method [43, 47] consists to create n model, wheren correspond to the number of objects, in which an object at time isexcluded. So each model is calculated with n � 1 objects and it isused to predict the missing response. The gap between the experi-mental response and the predictive response is accumulated for allthe n objects in order to estimate a parameter Predictive Error Sumof Squares (PRESS) that has a relationship with the model predictive

55


power. The PRESS calculating is completely similar, in the shape, tothe RSS calculating:

PRESS =n

Âj=1

�yj � yj/j

�2 (24)

where the summation runs on all n objects, yj/j is the j-th responsepredicted by the model and yj is the j-th experimental response. Thenotation j/j indicates that the j-th object was not present in the calcu-lated model.

In analogy with the definition of determination coefficient, it is nowpossible defining a similar quantity, determination coefficient in pre-diction (Q2), which is able to estimate the predictive power of themodel:

Q2 = 1 � PRESSTSS

(25)

This prediction coefficient doesn’t necessarily grow with the grow ofnumber of variables in the model, but on the contrary is very sensitiveto the presence of variables that lead noise and/or are not otherwiserelevant to the response. For this reason, Q2 can also assume negativevalues if there is not a variable that affect the experimental response.

This prediction coefficient does not necessarily grow with the grow ofnumber of variables in the model, but on the contrary is very sensitiveto the presence of variables that lead noise and/or are not otherwiserelevant to the response. For this reason, Q2 can also assume negativevalues if there is not a variable that affect the experimental response.

In order to find the best prediction model, it is possible to comparethe PRESS of several models built with a different number of relevantfactors and their interactions. Stepwise method is commonly used toevaluate the best model using Q2 . Stepwise approach tests the addi-tion of each variable using a chosen model comparison criterion. Thevariable which shows the higher variance among the others is added tothe model if and only if it helps to improve the predictions coefficients.This procedure is repeated until no more improvement is observed [12,47].

After choosing the best model for one response, it is also necessarya real model validation using experiments within the experimentaldomain but different from those used to build the model. Only afterthis validation there will be the evidence of the real goodness of themodel.

56

5.8 M I S U S E O F T H E A N O VA F O R 2K FACTORIAL EXPERIMENTS

5.8 M I S U S E O F T H E A N O VA F O R 2K FACTORIAL EXPERIMENTS

Analysis of variance (ANOVA) is an extremely powerful statistical tech-nique that can be used to separate and estimate the different causes ofvariation. This method is widely employed ,but the use of ANOVA forthe 2k factorial designs is confusing and makes little sense [2]. Considerthe analysis of a two-level design with k factors in r replications andhence N = r ⇤ 2k observations. In the ANOVA approach the 2k � 1 de-grees of freedom for treatments are partitioned into individual ”sumsof squares, for effects, equal to N(e f f ect)2/4, each of which is dividedby its single degree of freedom to provide a mean square. Thus, in-stead of seeing the effect (y+y�) itself, the experiment is presentedwith N(e f f ect)2/4, which must then be referred to an F table with onedegree of freedom. The p values associated with the null hypothesis atthe 5% and 1% of probability should not be used mechanically to decidewhat effects are real and what effects should be ignored. In the properuse of statistical methods information about the size of an effect andits possible error must be allowed to interact with the experimenter’ssubject matter knowledge [2].

5.9 A S S E S S I N G T H E S I G N I FI C A N T E FF E C T S

When the number of the degrees of freedom do not allow the analysisof variance, the significant effects can be calculated trough the evalua-tion of the experimental error.

One simple method to understand the significant effects is to comparethe uncertainty of the calculated effects to the experimental error. Thisassessing is carried out trough t test [43]. The degrees of freedom forthe t distribution are equal to the number of degrees of freedom used tomeasure the experimental error. One way to calculate the experimentalerror is using replicated experiments, as shown in the equation 26:

s2y =

Âj=1n (yi � y)2

n � 1(26)

where s2y is the experimental variance, yi is the measured value for the

j-th replication, y is the average value for the responses and n is thenumber of repetition.

The experimental error is the square root of the experimental variance:

sy =

sÂj=1

n (yi � y)2

n � 1(27)

57

5.10 R E S I D U A L A N A LY S I S

The uncertainty of the effects, b, is shown by the equation 28:

sb = 2

rs2

n(28)

If this quantity is multiplied for the uncertain of b, calculated for thereference value of t distribution, it is possible to define the confidenceintervals of the values of effects1 (Figure 29).

Figure 29. Confidence intervals

Moreover, under the assumption that the highest order interactions arelargely due to noise, the effect of these interactions (bj) can provide areference set for the estimation of the standard error. The standard errorfor the main effects and two-factor interactions was also calculatedapplying Equation 29 [2]:

sy =

vuutÂj=1M

⇣b⇤j⌘2

M(29)

where bj denotes the coefficients relative to the terms of higher orderthan the second and M is the number of these higher order interactions.

Once calculated the experimental error, in order to assessing the sig-nificant effects, each variable and their interactions are divided by sy.The results can be compared with the tn distribution. If the tn value issmaller than the calculated ratios, the effects are significant, otherwisethey are irrelevant.


The analysis of residuals is important to assessing the validity of theresults obtained by a regression model. This analysis if focused onmistakes committed during the calculation of the response using themodel calculated. The residual ei is given by the difference between theexperimental response and the average value estimated by the model:

ei = yi � yi (30)

1 with n � p degree of freedom.

58


If the model is good, the residual must derive only from the experimen-tal error. Therefore the residues should be distributed in according to anormal distribution around zero. The normal probability plot is widelyused to graphically assess the presence of anomalies in the residualdistribution. The presence of anomaly residues means that the modelcannot be used since it is affected by a systematical error.

The vertical axis gives the normal probability of the residuals distribu-tion. The horizontal axis corresponds to the numerical values of theresiduals. The standard methodology to create a normal probabilityplot, for the evaluation of the residual normality, is summarized in thefollowing step:

• Sort the residuals in ascending order, excluding from these theaverage, which cannot be considered as the sum of experimentalerrors.

• Count the number, n, of the estimated residuals.

• Split the interval [0� 100] into n intervals of equal width (100/p).

• Represent the midpoint of the first interval against the smallestresidual; then, represent the the second interval middle pointagainst the second residual, and so on.

The x axis reports the values of residual and in the y axis reports thecumulative probability which corresponds to of the middle point of theregarded interval. If an approximately straight-line trend is obtainedand the resultant line passes through the point (0, 50%), it means itis a normal distribution (Figure 30). The points out of the line pointrepresent the outlier.

Figure 30. Example of Normal Probability Plot

59


Shapiro and Wilk Test is a statistical test which allows to numericalassess the normality of residuals [44]. Indeed, it evaluates if the residu-als derive from a normal distribution. The test is obtained dividing alinear combination of the sorted residuals for their variance (Equation31). The hypothesis of normality is evaluated comparing the observedvalue of W with wa alfa-th quantile of the statistic test. If W is biggerthan wa there is no evidence of non-normality for this test-set.

W =Ân

i=1(ai ⇤ ei)2

Âni=1(ei � e)2 (31)

To compute the value of W, given a complete random samples of sizen, (x1, x2, ..., xn), one can proceed as following:

• Sort the sampless in ascending order x1 < x2 < ... < xn.

• Calculate the variance s2 = Âni=1(xi � x)2

• If n is even, n = 2k, compute:

b =n

Âi=1

an�i+1 (yn�i+1 � yi) (32)

If n is odd, n = 2k + 1, the computation is just below (equation33), since ak+1 = 0 when n = 2k + 1.

b = an (yn � y1) + ... + (yk+2 � yk) (33)

where the value of y, the sample median, does not enter the com-putation of b. For both the equation the values of a is tabulated.

• Compute W = b2/s2

• Choose significant level.

• Compare W with the tabulated p-valued. Small values of Windicate non-normality.

60

6C O M P U N D I N G

The purpose of this chapter is to give a basic introduction on thestep of compounding, functions of major ingredients, some commonproperties and some selected test method.

”COMPOUNDING, is a term that has evolved within the tire and rubber in-dustry, is the materials science of modifying a rubber or elastomer or a blendof polymers and other materials to optimize properties to meet a given serviceapplication or set of performance parameters” [34].

Rubber compounding includes selection of proper ingredients to enableone to process and vulcanize compound to give desirable physical andchemical properties, especially after aging [17]. Elastomers by them-selves are not useful until they are properly mixed with ingredients andcured. Depending on the type of rubber products, various ingredientsare used. Each ingredient has specific function in imparting desiredproperties. Ingredients in compound formulations (also called recipes)are based on Parts per Hundred Rubber (phr). A typical passenger tirerecipe [17] is listed in Table 12:

Table 12. Passenger tire tread recipe

Ingredient phr

SBR 60cis-Polybutadiene 40

CB 70Aromatic Oil 37.5Zinc Oxide 5Stearic acid 1

TMQ (antioxidant) 26PPD (antiozonant) 1.5

Sulfur 1.5Sulfenamide (accellerator) 1

TMTD 0.2

During the designing of a rubber formulation a range of objectives andrestrictions have to be taken into account. Product performance require-ments will dictate the initial selection of formula ingredients. Thesematerials must be environmentally safe, meet occupational health andsafety requirements, be processable in the product manufacturing fa-cilities, and be cost effective.

61

C O M P U N D I N G

Compounding rubber involves several steps. The first is the recipeselection of proper ingredients and exactly weighted. The selectedformulation should be easily processable, mixable and extrudable withmaximum scorch safety. It can be modeled and cured within a reason-able time period to produce a specific product with desired properties.If possible, these steps should be performed at the lowest cost.

Compounded rubber has many unique characteristics not found inother materials, such as dampening properties, high elasticity, and abra-sion resistance [34]. Hence rubber has found use in applications suchas tires, conveyor belts, large dock fenders, building foundations, auto-motive engine components, and a wide range of domestic appliances.The ingredients available to the materials scientist for formulating arubber compound can be divided into the following categories:

P O LY M E R S : This is the most important ingredient in rubber formula-tion. Specific polymers are selected for desired compund proper-ties.

FI L L E R S : These are used to reinforce or enhance properties of elas-tomers while reducing cost of the compound. In black compoundCB are used. For white compounds, silica, clay, calcium carbon-ate, etc, can be used.

P R O C E S S I N G A I D S : These materials are used to help in mixing, cal-endering, extrusion and molding by lowering the viscosity of acompound. Examples are various oils and plasticizers.

A N T I D E G R A D A N T S : These chemicals are used to protect rubber bothin uncured and cured states, from oxidation, ozonation and agingand, therefore, aid in extending product life.

V U L C A N I Z I N G A G E N T: These chemicals provide, upon heating, thecross-linking of the elastomer molecules. Thus after vulcaniza-tion the elastomer became harder and more thermally stable.Sulfur is the primary vulcanizing agent. However, in some casesperoxides are also used. Curing, vulcanization, and cross-linkingare synonymous and are used interchangeably.

A C C E L E R AT O R S : These materials accelerate the vulcanization by in-creasing the rate of cross-linking reactions.

A C T I VAT O R S : These chemicals form complexes with accelerators andfurther activate the curing process. Zinc oxide and stearic acidare commonly used activators.

In addition, special materials, not normally used in rubber compunds,may be needed to impart certain characteristics to compounds. Exam-ples are: blowing agents, fabric-rubber adhesion promoters, tackfiersand flame retardants.

62

6.1 P O LY M E R S

6.1 P O LY M E R S

The rubber polymers can be sorted in three categories:

1. General purpose rubbers

2. Solvent resistant

3. Heat resistant

Examples of these are included in Table 13.

Table 13. Three different groups of polymers

Type Rubber Applications

General Purpose

Natural Tires, belts,Synthetic PolyisoprenePolybutadiene

hosesStyrene-ButadieneButylEthylene-propylene-diene (EPDM)

Solvent Resistent Nitrile Oil seals, oil hoses,Neoprene automotive gaskets, tank

linign

Heat ResistantSilicone Spark plugPolyacrylate cables,Flurocarbon gaskets, seals

World rubber usage is around 18 million metric tons, it can be dividedbetween natural rubber, which constitutes about 46% of global con-sumption, and synthetic rubber (64%). About synthetic rubber themain type are BR and SBR that represent respectively 47% and 18%.[34].

As explained in Chapter 1, NR is a natural polymer most widely usedas elastomer due to its unique properties. Indeed NR presents in mostof its useful a large stretch ratio, high resilience, and extremely water-proof, so for more products there are no alternative materials exceptsynthetic rubbers. Natural rubber consists of thousands of isopreneunits linked together.

Synthetic rubber consist, instead, of several artificial elastomers mainlysynthesized by petroleum byproducts. A wide variety of syntheticelastomers have been developed to overcome some of the performancedeficiencies of NR. In addition synthetic elastomers have supplantedNR since the properties of NR can be less modified than the syntheticrubber.

63

6.2 FI L L E R S

It is common to blend more than one type of rubber within a giventire tread compound, since the compunding, processing and curing ofNR, SBR and BR are similar. BR is generally used in blends with NRand SBR to improve abrasion resistance of the blend. For compoundscontaining these elastomer, normal ingredients are: fillers, processingadditives, antioxidant, antiozonants, vulcanizing agents, accelerator,and activators. SBR and BR require less sulfur then NR and moreaccellerator for same state of cure.

6.2 FI L L E R S

Fillers, or reinforcement aids, such as CB, clays, and silicas are addedto rubber formulations to meet material property targets such as tensilestrength and abrasion resistance. The fillers can be grouped into twomain categories:

• Carbon Black: Mainly used to reinforce the rubber compounds,and to some extent, it involves lower compound cost.

• White fillers: Used in non-black compounds to semi-reinforceand to lower cost. Silica filler is used to provide reinforcement.

Carbon Black

Carbon Blacks are produced by either furnace or thermal processes andare respectively know as furnace or thermal balcks. CB technology isas complex since an extensive range of blacks are available. The choiceis based on which sets of properties the compound needs.

As an empirical guide, an increase in a carbon black aggregate sizeor structure will result in an improvement in cut growth and fatigueresistance. A decrease in particle size results in an increase in abrasionresistance and tear strength, a drop in resilience, and an increase inhysteresis and heat buildup.

Figure 31 illustrates the general trends for tread-grade carbon blackloading and the effect on compound physical properties. As carbonblack level increases, there are increases in compound heat buildupand hardness and, in tires, an increase in rolling resistance and wetskid properties [34]. Tensile strength, compound processability, andabrasion resistance, however, go through an optimum after which theseproperties deteriorate.

64

6.2 FI L L E R S

Figure 31. Effect of carbon black level on compound properties.

White Fillers

Non-black fillers contribution to compound properties depends on thesurface area. High surface area filler provides higher reinforcement.

Addition of silica to a rubber compound offers a number of advantagessuch as improvement in tear strength, reduction in heat buildup, andincrease in compound adhesion in multicomponent products such astires. Two fundamental properties of silica and silicates influence theiruse in rubber compounds: ultimate particle size and the extent of hy-dration. For proper reinforcement with silica, saline coupling agentis added in the first stage of mixing cycle. Silicas, when compared tocarbon blacks of the same particle size, do not provide the same level ofreinforcement, though the deficiency of silica largely disappears whencoupling agents are used with silica.

A series of additional filler systems merit brief discussion, not becauseof their reinforcement qualities but because of their high consumption.These include kaolin clay (hydrous aluminum silicate), mica (potassiumaluminum silicate), talc (magnesium silicate), limestone (calcium carbon-ate), and titanium dioxide. As with silica, the properties of clay canbe enhanced through treatment of the surface with silane couplingagents. Clays improved tear strength, an increase in modulus, im-proved component-to-component adhesion in multicomponent prod-ucts, and improved aging properties. Titanium dioxide finds extensiveuse in white products such as white tire sidewalls where appearance isimportant.

65

6.3 P R O C E S S I N G A I D S

6.3 P R O C E S S I N G A I D S

These ingredients are added to rubber compounds to help in process-ing, e.g., mixing, extrusion etc. Most commonly used processing aidsare oils. Oils don’t chemically react with rubber molecules but simply”lubricate” molecules, lower the viscosity and, thus, improve process-ability while reducing compound cost [17]. Thus, the use of oils inrubber compounds permits the application of higher molecular weightelastomers for better properties.

Other processing aids used in rubber compounds are fatty acids, waxes,organic esters and low molecular weight polymers. Chemical peptizersalso reduce polymer viscosity meanwhile reducing molecular chainlength. Peptizer react with free radicals formed during mixing andhence shorten the molecular lenth. They are often used in breaking-down natural rubber.

6.4 A C T VAT O R S , V U L C A N I Z I N G A G E N T S A N D A C C E L L E R AT O R S

The vulcanization system constitutes the fourth component in an elas-tomeric formulation and functions by creating cross-links betweenadjacent polymer chains in the compound. A typical vulcanizationsystem in a compound consists of three components:

• Activators

• Vulcanizing agents

• Accelerators

Activators

The vulcanization activator system consisting of zinc oxide and stearicacid. Zinc oxide and stearic acid are most commonly used activa-tor. Stearic acid and zinc oxide levels of 2.0 and 5.0 phr, respectively,are accepted throughout the rubber industry as being adequate forachievement of optimum compound physical properties, when usedin combination with a wide range of accelerator types.

Vulcanizing agents

The most common vulcanizing agent is sulfur. The higher is the sulfurcontent in the compound, the more is thee cross-link density, the unitof devulcanization. The cross-link can be given by mono-, di-, poly-sulfidic blends. Polysulfidic cross-link gives poor aging properties,poorer long-term flexlife. Mono- or di-sulfidic cross-links on the other

66

6.5 A N T I D E G R A D A N T S

hand, provide poor fatigue life. Therefore, compounds are designed togive the best overall properties with minimum trade-offs.

Accellerators

Sulfur alone takes a commercially prohibitive lenght of time to cure arubber compound. Therefore, chemical accellerators are used to speedup the curing rate. Additionally, activators are used to further activatethe curing process. Table 14 is an example of natural rubber compoundshowing effects of an activator and accellerator. An activator reducedthe cure time, but still it is too long. The accellerator made it muchfaster.

Table 14. Effects of activator and accellerator on cure time of NR compounds

NR Compounds Cure Time at 140°C

1100 phr NR

8 hours8 phr sulfur

2100 phr NR

3 hours8 phr sulfur3 phr zinc oxide

3

100 phr NR

12 minutes8 phr sulfur

3 phr zinc oxide1.5 phr CBTS

Elastomers with no or low unsaturation are generally cured with perox-ides. Such cross-links are C�C type and are thermally stable. Peroxidescures therefore, give improved compression set, lower creep and stressrelaxation. Poor flexlife and low tear strength are major disadvantageof peroxide cures.

6.5 A N T I D E G R A D A N T S

Good aging properties of rubber compounds are essential for providingacceptable service life. The type of elastomer used is the principal fac-tor in considering aging properties. In general the more saturated themain chain of elastomer, the better are the aging properties. For exam-ple, EPDM and BR are quite stable as compared to more unsaturatedpolymers such as SBR. Unsaturation sites are susceptible to oxidationand ozonization effects. Chemical antioxidants and antiozonants areused to extend the service life or products containing dine rubbers.

67

6.6 S P E C I A L C O M P O U N D I N G I N G R E D I E N T S

C H E M I C A L A N T I O X I D A N T S extended the life of rubber productsby first reaching with polymeric free radicals and stopping thepropagation of polymer oxidation. A good antioxidant shouldhave low volatility , be stable at high temperatures and be solublein rubber. Commercially available antioxidants generally fall inthree groups: secondary amines, phenolics, and phosphites. Theamine-type antioxidant are normally used in black compounds,while phenolics and phosphites are relatively non-staining andare normally used in non-back compounds.

C H E M I C A L A N T I O Z O N A N T S such 6PPD, DPPD and IPPD, diffuseon the surface, reacts with ozone before rubber molecules havechance to react with ozone. This reactions avoid the decompo-sition of double bonds in the main chain. A good antiozonantsshould be reactive with ozone, should have acceptable solubilityand diffusion in the rubber.

6.6 S P E C I A L C O M P O U N D I N G I N G R E D I E N T S

In addition to the four primary components in a rubber formulationthere are a range of secondary materials such as plasticizers, resins,and coloring agents.

P L A S T I C I Z E R S : the term plasticizer is used more frequently to de-scribe the class of materials which includes esters, pine tars, andlow molecular weight polyethylene. Pine tars are highly com-patible with natural rubber, give good filler dispersion, and canenhance compound properties such as fatigue resistance andcomponent-to-component adhesion which is important in tiredurability. Other low-volume plasticizers include factice (sulfur-vulcanized vegetable oil); fatty acid salts such as zinc stearate,which can also act as a peptizer; rosin; low-molecular-weightpolypropylene; and organosilanes such as dimethylpolysiloxane.

P E P T I Z E R S serve as either oxidation catalysts or radical acceptors,which essentially remove free radicals formed during the initialmixing of the elastomer. This prevents polymer recombination, al-lowing a consequent drop in polymer molecular weight, and thusthe reduction in compound viscosity. Examples of peptizers arepentachlorothiophenol, phenylhydrazine, certain diphenylsulfides,etc.

R E S I N S : have been classified in an almost arbitrary manner into hy-drocarbons, petroleum resins, and phenolic resins. Hydrocarbonresins tend to have high glass transition temperatures so that atprocessing temperatures they melt, thereby allowing improve-ment in compound viscosity. They will, however, harden at room

68

6.7 C O M P O U N D P R O P E RT I E S A N D T E S T S

temperature, thus maintaining compound hardness and modulus.Within the range of hydrocarbon resins, aromatic resins serve asreinforcing agents, aliphatic resins improve tack, and intermedi-ate resins provide both characteristics. Coumarone-indene resinsystems are examples of such systems.

S H O RT FI B E R S : may be added to compounds to further improve com-pound strength. They can be processed just as other compound-ing ingredients. Short fibers include nylon, polyester, fiberglass,aramid, and cellulose. The advantages of adding short fibers toreinforce a compound depend on the application for which theproduct is used; however, general advantages include improvedtensile strength, improved in fatigue resistance and cut growthresistance, increase in stiffness, increased component or prod-uct stiffness, improved cutting and chipping resistance as in tiretreads.


Laboratory tests on rubber compounds are run to determine properties,and, to some extent, to predict service life of products. Mainly the testson rubber can be grouped in two categories:

T E S T O N U N C U R E D R U B B E R C O M P O U N D S Compound viscosityis measured by using viscometers and gives an indication of howa compound might process. Curemeters are used to determinecompound scorch time for processing safety and cure rate at aspecified temperature.

T E S T O N C U R E D R U B B E R C O M P O U N D S The most commonly mea-sured property of rubber is tensile strength which indicate theultimate strength of rubber. The elongation at which the samplebreaks is the ultimate or breaking elongation. Stress at a specificelongation is known modulus at that elongation. Typically rubberindustry looks at modulii at 100% and 300% elongation. The mostcommon aging tests include hot air aging, ozone aging and thenmeasuring residual tensile strength and elongation. Tire treadcompounds are additionally tested for abrasion, tear resistanceand traction. Common rubber test and their ASTM standards arelisted in table 15.

Following are brief discussed the tests carried out in the present study.

Uncured tests

Important characteristics related to the vulcanization process are thetime elapsed before cross-linking starts, the rate of cross-link formation

69


Table 15. Rubber Compound Tests and ASTM Designations

Type Compound Property Test ASTM

UncuredViscosity Mooney Viscosimeter D1646-96

Scorch time and Rheometer D2084-95cure rate

Cured

Tensile strength, Tensile Tester D412-87elong., modulusHardness Dorometer Hardness D2240-97

Flex resistance DeMattia Flex Tester D430-95Compression resistance Compression Set D395-87(94)

Abrasion resistance Pico Abrasion D228-88(94)Tear resistance Die C Tear Test D624-91

Air aging resistance Hot Air Aging Test D573-88(94)

Ozone resistance Static Ozone Test D518-86(91)Dynamic Ozone Test D3395-86(94)

once it starts, and the extent of cross-linking at the end of the process.There must be sufficient delay or scorch resistance (resistance to prema-ture vulcanization) to permit mixing, shaping, forming, and flowingin the mold before vulcanization. Then the formation of cross-linksshould be rapid and the extent of cross-linking must be controlled(Figure 32).

Figure 32. The effect of heat history (processing) on scorch safety.

of a rubber sample, each vulcanized for a different length of time at a giventemperature.

In order to measure the vulcanization characteristics, the rubber isenclosed in a heated cavity (Fig. 5). Imbedded in the rubber is a metal discthat oscillates sinusoidally in its plane about its axis.Vulcanization is measuredby increase in the torque required to maintain a given amplitude (e.g., degreesof arc) of oscillation at a given temperature. The torque is proportional to alow strain modulus of elasticity. Since this torque is measured at the elevatedtemperature of vulcanization, the portion of it due to viscous effects isminimal. Thus it has been assumed that the increase in torque during vulcan-

326 Aubert Y. Coran

FIGURE 4 The effect of heat history (processing) on scorch safety.

FIGURE 5 Oscillating disc rheometer.

Scorch resistance is usually measured by the time at a given temper-ature required for the onset of crosslink formation as indicated by anabrupt increase in viscosity. During this test, fully mixed but unvulcan-ized rubber is contained in a heated cavity. Imbedded in the rubberis a rotating disc. Viscosity is continuously measured (by the torquerequired to keep the rotor rotating at a constant rate) as a function oftime. The temperature is selected to be characteristic of rather severe

70


processing (extrusion, calendering, etc.). Both the rate of vulcanizationafter the scorch period and the final extent of vulcanization are mea-sured by rheometer.

In order to measure the vulcanization characteristics, the rubber isenclosed in a heated cavity (Figure 33). Imbedded in the rubber is ametal disc that oscillates sinusoidally in its plane about its axis.

Figure 33. Oscillating disc rheometer.

of a rubber sample, each vulcanized for a different length of time at a giventemperature.

In order to measure the vulcanization characteristics, the rubber isenclosed in a heated cavity (Fig. 5). Imbedded in the rubber is a metal discthat oscillates sinusoidally in its plane about its axis.Vulcanization is measuredby increase in the torque required to maintain a given amplitude (e.g., degreesof arc) of oscillation at a given temperature. The torque is proportional to alow strain modulus of elasticity. Since this torque is measured at the elevatedtemperature of vulcanization, the portion of it due to viscous effects isminimal. Thus it has been assumed that the increase in torque during vulcan-

326 Aubert Y. Coran

FIGURE 4 The effect of heat history (processing) on scorch safety.

FIGURE 5 Oscillating disc rheometer.

Vulcanization is measured by increase in the torque required to main-tain a given amplitude (e.g., degrees of arc) of oscillation at a giventemperature. The torque is proportional to a low strain modulus ofelasticity. Since this torque is measured at the elevated temperature ofvulcanization, the portion of it due to viscous effects is minimal. Thusit has been assumed that the increase in torque during vulcanizationis proportional to the number of cross-links formed per unit volumeof rubber. The torque is automatically plotted against time to give aso-called rheometer chart, rheograph, or cure curve.

The cure curve gives a rather complete picture of the overall kinetics ofcrosslink formation and even crosslink disappearance (reversion) fora given rubber mix (Figure 34). In some cases, instead of reversion, along plateau or marching cure can occur.

Tensile tests

The ability of rubber to stretch several times its original length is one ofits chief characteristics. Nevertheless, tensile stress-strain is a commontest used, also, as a general guide to quality. A typical stress-straincurve is shown in Figure 35. The properties measured are tensilestrength, elongation at break and stress at a given elongation (modulii).

71

6.8 C O M P O U N D I N G C O M P O S I T I O N

Figure 34. Rheometer cure curve.

cure can occur. The cure meter is, therefore, extensively used to control thequality and uniformity of rubber stocks (also called rubber compounds).

Vulcometry started as a research tool to study vulcanization. It was thenused to control uniformity of rubber mixed in the factory. Also programmedtemperature-profile vulcometry has been used to develop recipes for indus-trial use. The cure temperature–time profile of an industrial mold can beimposed on the curing cavity of the cure meter. The test sample can then bevulcanized in the cure meter under the same conditions as those encounteredin the factory. Both the extent of cure and temperature can be simultaneouslydisplayed as functions of time.

V. VULCANIZATION BY SULFUR WITHOUT ACCELERATOR

Initially, vulcanization was accomplished by using elemental sulfur at aconcentration of 8 parts per 100 parts of rubber (phr). It required 5 hours at140°C.The addition of zinc oxide reduced the time to 3 hours.The use of accel-erators in concentrations as low as 0.5phr has since reduced the time to asshort as 1 to 3 minutes. As a result, elastomer vulcanization by sulfur withoutaccelerator is no longer of much commercial significance. (An exception tothis is the use of about 30 or more phr of sulfur, with little or no accelerator,to produce molded products of hard rubber or “ebonite.”) Even though unac-celerated sulfur vulcanization is not of commercial significance, its chemistryhas been the object of much research and study.

328 Aubert Y. Coran

Crosslink Density

Scorch Time(Induction Period)

Vulcanization Time

Reversion

Normal Cure

Marching Cure

FIGURE 7 Rheometer cure curve.

Figure 35. Typical tensile stress-strain curve.

Rubber Technologist’s Handbook

320

10.3.3 Tensile stress-strain properties

The ability of rubber to stretch several times its original length is one of its chiefcharacteristics but it should be noted that at least as many rubber products are used incompression or shear as are used in tension. Nevertheless, tensile stress-strain is thesecond most common test (after hardness) and the strength properties are used as ageneral guide to quality.

The standard test given in ISO 37 [29] is arbitrary, the result being dependent on testpiece geometry and speed. Dumbbell test pieces are generally used with four sizes beingspecified, although there is also provision for rings. A typical stress-strain curve is shownin Figure 10.3. The properties measured are tensile strength, elongation at break andstress at a given elongation (rubber technologist’s modulus).

Figure 10.3 Typical tensile stress-strain curve

Tensile tests are carried out on a machine comprised of grips to hold the test piece,means of separating the grips at constant rate of traverse, a load cell to measure forceand an extensometer to measure elongation. Tensile machines are often ‘universal’ andcan also be used for tear and adhesion tests and stress-strain characteristics in compressionand shear. Specification of the apparatus is given in ISO 5893 [30]. Although the test haschanged relatively little, there have been great advances in apparatus with most machinesbeing computer controlled and laser and video extensometers to make non-contactmeasurement of elongation.


The behaviors of GTR or T-GTR in new blends was investigated inorder to evaluate the possible use of the reclaimed material. Takinginto account the results obtained by full factorial design, the GTR for

72


compounding was treated with the conditions shown in Table 16. Thisconditions has been chosen since it represent a good trade off betweentwo requirements:

• There was a fair amount of DD so it was possible to understandhis behaivour in the re-vulcanization process.

• The treatment conditions allow to achieve a strong degree ofdevulcanization.

Table 16. Treatment conditions of GTR for compounding

Time Temperature ratio DD/rubber Pressure(hours) (�C) (%) (bar)

2 180 10 150

GTR and T-GTR were mixed with virgin rubber in order to obtainseveral compounds. Blends formulations are shown in Table 17. As ref-erence, a blend with NR as the only polymeric material was prepared.

The blends were formulated considering the composition of the start-ing material in order to reach the same amount of rubber and CB ineach blend [33].

In order to remove the unreacted DD and to evaluate the influence ofthe DD on the revulcanization process, a half of T-GTR was extractedwith hot acetone (TE-GTR). Moreover, in order to compare the prop-erties of the new blends, the GTR was also extracted with hot acetone(E-GTR).

Each blend was mixed for 30 minutes using a two-roll mill and the cur-ing characteristics were studied using a Moving Die Rheometer (MDR200, Alpha Technologies) at 150 �C. The resulting curves allowed toevaluate the maximum and minimum torque (MH, ML), the scorchtime (TS2) and was especially used to evaluate the optimum curingtime for the tensile test.

Tensile test samples were prepared by compression molding at the opti-mum curing temperature and the dumbbell shape specimens were cutout. Mechanical properties were measured at room temperature usingtensile testing machine, (Instron tensile tester, Model 5567, Instron).

73


Tabl

e17

.Ble

nds

form

ulat

ion

Form

ulat

ion

(phr

)R

ef5-

GTR

5T-

10-

10T-

20-

20T-

5E-

5TE-

10E-

10TE

-20

E-20

TE-

NR

GTR

GTR

GTR

GTR

GTR

GTR

GTR

GTR

GTR

GTR

GTR

NR

100

97.5

97.5

9595

9090

97.5

97.5

9595

9090

GTR

05

010

020

00

00

00

0T-

GTR

00

50

100

200

00

00

0E-

GTR

00

00

00

05

010

020

0TE

-GTR

00

00

00

00

50

100

20C

arbo

nBl

ack

3735

.535

.534

3431

3135

.535

.534

3431

31Si

licon

Dio

xide

1515

1515

1515

1515

1515

1515

15Z

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eari

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4.5

4.5

4.5

4.5

4.5

4.5

4.5

4.5

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Wax

es1

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11

11

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11

11

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r1.

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31.

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31.

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31.

31.

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(25%

-75%

)4

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CBS

1.2

1.2

1.2

1.2

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Res

in0.

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74

Aǹљ�ҮIлɥȶɝɖ�ǹȾȈ�ѐȄɥѝȩɅȾ

75

7C H E M I C A L R E S U LT S

The aim of this chapter is to show the properties of the best modelsand to explain the evaluation methods of results.

7.1 R E G R E S S I O N M O D E L S

The experimental conditions and the responses obtained are resumedin Table 18 .A preliminary regression model, evaluated for each response, wasan interaction model including the four factors and all two-factor in-teractions [9]. Higher order interactions were omitted since in realphenomena, the probability to have significant effects decreases withthe increase of the interaction order [2]. This assumption allowed toexclude three- and four-factor interactions to favor the quality of themodel and to avoid over-fitting.

The ANOVA was carried out on this first model, thus it was possible topoint out which factors, and interactions were statistically significant[54]. Table 19 shows the ANOVA for the four responses containingall the factors and two-factor interactions. The P values of tempera-ture, DD and their interactions are lower than 0.05 and therefore theseparameters proved to be significant for the models [46].The standard error, estimated both using the highest order interactionand the central points, was also used to evaluate whether the effect ofthe factors or two-factor interactions were significant. Table 21 showsthe estimated effects and their standard error using the three centralpoints.

Indeed, Table 22 shows the estimated effects and their standard errorusing the higher order interactions. Table 21 and 22 also show the 95%confidence interval for each effect used to determine whether the effectof the factors and interactions were significant.

Both this approach and the ANOVA, applied to each experimentalresponse, identified the same significant factors and interactions. Thetemperature, content of DD and their interaction resulted the only sig-nificant parameters, while the other factors and interactions resultednegligible. Only for the crosslink density the DD resulted not signifi-cant, whereas the T and the interaction between the T and DD resulted

76


Table 18. Full Factorial design and experimental responses.

Sample t DD/rubber T P CD SF GF SC(min) (wt%) (C) (bar) (mmol/cm3) (wt%) (wt%) (wt%)

E1 60 1 70 80 0.073 2.3 98.2 2.43E2 240 1 70 80 0.07 2.6 97.8 2.4E3 60 25 70 80 0.073 1.8 99.1 2.45E4 240 25 70 80 0.068 2.6 98.2 2.37E5 60 1 190 80 0.046 9.8 91.6 2.43E6 240 1 190 80 0.042 10.9 91.1 2.53E7 60 25 190 80 0.035 14.8 85.9 2.79E8 240 25 190 80 0.019 22.6 79.8 2.84E9 60 1 70 240 0.07 3.2 97.8 2.37

E10 240 1 70 240 0.067 1.7 98.4 2.36E11 60 25 70 240 0.073 5.8 97.1 2.39E12 240 25 70 240 0.076 2.3 97.9 2.43E13 60 1 190 240 0.046 11.8 89.6 2.43E14 240 1 190 240 0.036 8.5 91.8 2.35E15 60 25 190 240 0.021 15.4 83 2.87E16 240 25 190 240 0.033 23.5 79.2 3.17

Center 1 150 13 130 160 0.043 4.6 95.4 2.24Center 2 150 13 130 160 0.039 6.8 93.6 2.43Center 3 150 13 130 160 0.046 6.3 94.4 2.51

significant. Also for this experimental response, the DD had to be con-sidered significant in order to preserve the hierarchy among the factors.

The effect of pressure variation was negligible; as a result, the treatmentcan be conducted at relatively low pressure. Nevertheless, pressuremust at least be equal to the condition of supercritical CO2 because inthis physical state the scCO2 acts as a solvent media for the devulcan-ization reaction. The treatment time is the least important factor, indeedthe devulcanization reaction employing DD involves radical reactions,resulting very fast in comparison to the treatment times tested.

The final model was calculated by multiple linear regression for eachexperimental response, considering only the significant factors. Inorder to confirm that the models obtained were the optimal ones, astepwise approach [47] was used to find the best combination of fac-tors and interactions evaluating the coefficient of determination forprediction (Q2). Table 20 shows the obtained regression coefficients.Moreover, the Table 6 shows that the model obtained for the gel fractionexhibits the highest R2, Q2 and the lowest difference between thesetwo coefficients, resulting in the best model.

The final regression models were statistically significant at 95% andwithout any lack of fit considering the same probability [2]. Moreover,in order to evaluate the model adequacy, the residuals distribution was

77


Table 19. Analysis of Variance for the interaction model of the responses.SW SF

Variable DF SS P value SS P valueConstant 1 < 0.001 < 0.001

t 1 0.0000422 0.419 6.002 0.258DD 1 0.000169 0.127 90.25 ¡0.001

T 1 0.00533 < 0.001 564.063 < 0.001P 1 0.000001 0.899 1.44 0.33

t ⇤ DD 1 0.0000123 0.659 17.222 0.111t ⇤ T 1 0.00000625 0.752 19.36 0.247t ⇤ P 1 0.0000562 0.354 6.502 0.28

DD ⇤ T 1 0.000324 0.046 66.423 < 0.001T ⇤ P 1 0.000004 0.8 0.422 0.695

DD ⇤ P 1 0.000025 0.531 1.96 0.5Residuals 8 0.000465 50.827

Total 18 0.00643 824.472

GF S

Variable DF SS P value SS P-valueConstant 1 < 0.001 < 0.001

t 1 4.101 0.258 0.00526 0.596DD 1 81.451 < 0.001 0.253 0.005

T 1 534.766 < 0.001 0.305 0.003P 1 2.976 0.33 0.00106 0.811

t ⇤ DD 1 8.851 0.111 0.00681 0.547t ⇤ T 1 4.306 0.247 0.0127 0.416t ⇤ P 1 3.706 0.28 0.00276 0.7

DD ⇤ T 1 82.356 < 0.001 0.214 0.008T ⇤ P 1 0.456 0.695 0.00681 0.547

DD ⇤ P 1 1.381 0.5 0.0298 0.225Residuals 8 22.091 0.138

Total 18 746.437 0.975

Table 20. Regression model for each experimental response

Response Constant DD T DD ⇤ T R2 R2adj Q2

SW 0.0838684 0.00054167 -0.0002229 -6.25E-06 0.9 0.89 0.86Sol -2.37609 -0.169965 0.0621702 0.00282986 0.87 0.85 0.8Gel 102.271 0.221614 -0.0553907 -0.003151 0.94 0.92 0.9

S 2.3513 -0.010408 0.00021441 0.00016059 0.79 0.75 0.69

studied. Figures 36, 37 shows the normal probability plots of the resid-uals for each response. No evident anomalies are present for Cross-linkDensity (36a), Sol (36b) and Gel (37a). For sulfur content (37b), two runs

78


Tabl

e21

.Est

imat

edef

fect

san

dst

anda

rder

ror

calc

ulat

edth

roug

hth

eth

ree

cent

rale

xper

imen

ts.

Effe

ctSW

SEt⇤

SESo

lSE

t⇤SE

Gel

SEt⇤

SES

SEt⇤

SE

Ave

rage

0.05

3±

0.00

2±

0.00

98.

7±

0.6

±2.

68.

7±

0.5

±2.

22.

54±

0.07

±0.

30t

-0.0

03±

0.00

2±

0.00

91.

2±

0.6

±2.

6-1

±0.

5±

2.2

0.04

±0.

07±

0.30

DD

-0.0

07±

0.00

2±

0.00

94.

8±

0.6

±2.

6-4

.5±

0.5

±2.

20.

25±

0.07

±0.

30T

-0.0

37±

0.00

2±

0.00

911

.9±

0.6

±2.

6-1

1.6

±0.

5±

2.2

0.28

±0.

07±

0.30

P-0

.001

±0.

002

±0.

009

0.6

±0.

6±

2.6

-0.9

±0.

5±

2.2

0.02

±0.

07±

0.30

t⇤D

D0.

002

±0.

002

±0.

009

2.1

±0.

6±

2.6

-1.5

±0.

5±

2.2

0.04

±0.

07±

0.30

t⇤T

-0.0

01±

0.00

2±

0.00

92.

2±

0.6

±2.

6-1

±0.

5±

2.2

0.06

±0.

07±

0.30

t⇤P

0.00

4±

0.00

2±

0.00

9-1

.3±

0.6

±2.

61

±0.

5±

2.2

0.03

±0.

07±

0.30

DD⇤

T-0

.009

±0.

002

±0.

009

4.1

±0.

6±

2.6

-4.5

±0.

5±

2.2

0.23

±0.

07±

0.30

T⇤

P-0

.001

±0.

002

±0.

009

-0.3

±0.

6±

2.6

-0.3

±0.

5±

2.2

0.04

±0.

07±

0.30

DD⇤

P0.

003

±0.

002

±0.

009

0.7

±0.

6±

2.6

-0.6

±0.

5±

2.2

0.09

±0.

07±

0.30

79


Tabl

e22

.Est

imat

edef

fect

san

dst

anda

rder

ror

calc

ulat

edth

roug

hhi

gher

orde

rin

tera

ctio

ns.

Effe

ctSW

SEt⇤

SESo

lSE

t⇤SE

Gel

SEt⇤

SES

SEt⇤

SE

Ave

rage

0.05

3±

0.00

3±

0.00

88.

7±

1.2

±3.

18.

7±

0.6

±1.

52.

54±

0.05

±0.

13t

-0.0

03±

0.00

3±

0.00

81.

2±

1.2

±3.

1-1

±0.

6±

1.5

0.04

±0.

05±

0.13

DD

-0.0

07±

0.00

3±

0.00

84.

8±

1.2

±3.

1-4

.5±

0.6

±1.

50.

25±

0.05

±0.

13T

-0.0

37±

0.00

3±

0.00

811

.9±

1.2

±3.

1-1

1.6

±0.

6±

1.5

0.28

±0.

05±

0.13

P-0

.001

±0.

003

±0.

008

0.6

±1.

2±

3.1

-0.9

±0.

6±

1.5

0.02

±0.

05±

0.13

t⇤D

D0.

002

±0.

003

±0.

008

2.1

±1.

2±

3.1

-1.5

±0.

6±

1.5

0.04

±0.

05±

0.13

t⇤T

-0.0

01±

0.00

3±

0.00

82.

2±

1.2

±3.

1-1

±0.

6±

1.5

0.06

±0.

05±

0.13

t⇤P

0.00

4±

0.00

3±

0.00

8-1

.3±

1.2

±3.

11

±0.

6±

1.5

0.03

±0.

05±

0.13

DD⇤

T-0

.009

±0.

003

±0.

008

4.1

±1.

2±

3.1

-4.5

±0.

6±

1.5

0.23

±0.

05±

0.13

T⇤

P-0

.001

±0.

003

±0.

008

-0.3

±1.

2±

3.1

-0.3

±0.

6±

1.5

0.04

±0.

05±

0.13

DD⇤

P0.

003

±0.

003

±0.

008

0.7

±1.

2±

3.1

-0.6

±0.

6±

1.5

0.09

±0.

05±

0.13

80


appear to be highly discrepant. Nevertheless, the normal distributionfor the residuals was confirmed by the Shapiro-Wilk normality test for95% confidence interval [44] for each response.

(a) Cross-link Density

(b) Sol Fraction

Figure 36. Normal Probability Plot of residuals

81


(a) Gel Fraction

(b) Sulfur Content

Figure 37. Normal Probability Plot of residuals

82


By using the derived equations, a 3-D graph was plotted for each exper-imental response in order to visualize the influence of the temperatureand the DD on the devulcanization process. The plotted surfaces donot show any maximum.

The temperature proved to be the most important factor for this devul-canization process. Indeed, by increasing the temperature a significantvariation of responses was observed, especially at high amounts of DD.

The high T and the subsequent decomposition of the DD generateradicals that lead to the chain scission and crosslink rupture, reducingthe crosslink density, the gel fraction and increasing the sol fraction asshown in Figures (38, 39, 40) [42]. These radicals react with the rubberchain and with the crosslink network increasing the sulfur content ofthe T-GTR (Figure 4d). Moreover, the efficiency of the DD is strictlycorrelated to the temperature. Indeed, at high temperatures the DDgenerates more benzene sulfide radicals [24, 42] .

Figure 38. 3-D plot for cross-link density

83


Figure 39. 3-D plot for experimental responses

(a) Sol Fraction

(b) Gel Fraction

84

7.2 I N FL U E N C E O F D E V U L C A N I Z AT I O N P R O C E S S O N C R O S S - L I N K N E T W O R K

Figure 40. 3-D plot for Sulfur Content

7.2 I N FL U E N C E O F D E V U L C A N I Z AT I O N P R O C E S S O N C R O S S -L I N K N E T W O R K

In order to investigate, in more detail, the relative effect of degradationof the main chain and of the cross-link network the dependence ofexperimental normalized gel fraction, x/x0, versus the normalizedcross-link density, #/#0, was analyzed using the equation 34 [21, 22]:

#

#0=

✓1 + V ln

xx0

◆H✓

xx0

� e�1/V

◆(34)

where the GTR parameters are the normalization factors and H repre-sents the Heaviside step function. A good fit (R2 = 0.8042) betweenthe experimental data and equation 34 was obtained with V = 0.1189.The V parameter represents the change of normalized gel fraction withrespect to change in normalized cross-link density that is affected bythe relative amounts of inter-molecular bond breakage and main chainbond breakage [53].

The Horikx function [18] expresses the relationship between the nor-malized gel fraction and the normalized crosslink density when themain chain network of the material undergoes degradation. In the fig-

85

7.3 VA L I D AT I O N

ure 41 the experimental data stay above of the Horikx function. Thus,the experimental curve describe a decreasing in cross-link density withless decreasing in gel fraction. Indeed it can be seen that on decreasingof the normalized cross-link density by about 50%, the normalized gelfraction exhibits a decrease by only about 10%, confirming that thetreatment preferentially generated the rupture of the crosslink network.

Figure 41. Comparision between Experimental Curve and Horikx Function

These results support the hypothesis that crosslink network scissionoccurred during the devulcanization and that the DD mainly reactedwith the cross-link network rather than with the main chain.


Internal and external validations were carried out in order to test themodel predictive power within the studied domain. Indeed, for theoptimization procedure and for future predictions, a good descriptivecapacity but also a good prediction capacity of the models are required.

The leave-one-out cross validation was used for the internal validation.In this procedure, each observation is predicted by using the modeldeveloped without including that observation. The prediction powerof the regression model is given by Q2 (Table 20) which is based on this

86


procedure [9] [43] and it was already used to select the best model.

As robustness of leave one out cross validation is questionable [47], anexternal model validation based on predicting new experimental runswas also performed considering three points within the experimentaldomain. Table 23 shows the conditions for the experimental runs. Theseexperiments were chosen varying either the significant parameters forthe final model (T and DD) or the negligible ones (time and pressure).

Table 23. Validation experiment conditions within the studied domain.

Experiment Time DD Temperature Pressure

V1 60 (-) 5 150 240 (+)V2 60 (-) 10 180 240 (+)V3 120 10 180 150

The Figure 42 (a,b,c and d) shows the experimental crosslink density,sulfur and sol and gel fraction obtained for these validation points com-pared to the predicted values. It can be seen that every experimentalvalue is in accordance with the predicted ones.

Figure 42. Validation Results

87

8M E C H A N I C A L P R O P E RT I E S O F T H E N E W B L E N D S

In this chapter is discussed how the DD affect the process ofrevulcanization understanding the results of Tensile Tests of theblends containing devulcanized GTR.

Tensile Tests were carried out for each blend. It allows to study thestress-strain curve (Figure 44), the moduli at 100% and at 300%, the ten-sile strength, and the elongation at break as a function of the reclaimedrubber content (phr).

These mechanical properties displayed that blends with various amountsof GTR exhibited worse properties than the reference blend with NR,as already observed [31, 37, 40]. Only the elongation at break of T-GTRwas greater than the reference (Figure 43).

Figure 43. Affects of the treatment on the elogation at break

tensile strength (Figure 45) and the elongation at break of the blendscontaining T-GTR were higher than the properties of blends containingGTR, the opposite was observed for moduli at 100% and at 300% ofelongation (Figure 46).

88

M E C H A N I C A L P R O P E RT I E S O F T H E N E W B L E N D S

(a) Stress Strain curves of un-extracted material

(b) Stress Strain curves of extracted material

Figure 44. Stress Strain curves

89


Figure 45. Affects of the treatment on the tensile strength

These results indicated that the devulcanization treatment affected themechanical properties. In order to evaluate the influence of the devul-canizing reagent on the mechanical properties of the cured blends bothGTR and T-GTR were extracted with hot acetone, dried and then com-pounded. From the comparison between the blends containing GTRand E-GTR at various phr, it can be seen that the extraction processadversely affected all the mechanical properties of the blends. This isprobably due to the removal of most additives of the rubber tire.

To evaluate the effect of residual DD during the revulcanization reac-tion on mechanical properties, the blends containing the T-GTR canbe compared to the blends with TE-GTR. Indeed, while the tensilestrength and the elongation at break of the blends containing T-GTRwere higher than the properties of blends containing TE-GTR, the op-posite was observed for moduli at 100% and at 300% of elongation.Therefore, the treatment partially balanced the worsening of mechani-cal properties due to the removal of additives. In fact, it is clear that allmechanical properties of blends containing TE-GTR were higher thanthe properties of blends containing E-GTR. These results showed thatthe treatment with DD increases the compatibility of the gel fractionwith the virgin rubber.

90


(a) Modulo 100

(b) Modulo 300

Figure 46. Affects of the treatment on the moduli

91

Aǹљ�Ұ ɅȾȄȶɥɖȩɅȾ

92

9C O N C L U S I O N

The aim of the present study was to investigate the scCO2 devulcaniza-tion process of a GTR by varying treatment time, temperature, percent-age of devulcanizing reagent DD and CO2 pressure. A full factorialexperimental design was used to define the experimental conditionswithin the variables domain. The crosslink density, the sol fraction,the gel fraction and the sulfur content were chosen as experimentalresponses to characterize the devulcanized GTR. The crosslink density,the sol and gel fraction gave information on the degree of devulcan-ization. Instead, the sulfur content was an important quantitativeindicator of the reaction between the DD and the GTR.

The experimental dataset was modelled by using multiple linear re-gression. To discriminate whether the factors and interactions werereal, the relevance of the effects for the factors and interactions wasevaluated comparing each computed effect with the standard error (SE)through a t-test. Moreover, under the assumption that the highest orderinteractions are largely due to noise, the effect of these interactions canprovide a reference set for the estimation of the standard error. In bothcases the most significant variables were temperature, DD percentageand their interaction; instead the influence of pressure and treatmenttime resulted negligible.

For a sound evaluation of the model, the residuals distribution wasstudied for each response. The Normal Probability Plot and the Shapiro-Wilk normality test confirmed the normal distribution. The regressionmodel developed in the study was internal and exsternal evaluated. Itresulted in a reliable prediction of devulcanization indicators withinthe experimental domain.

Indications regarding the reaction mechanism between DD and rubberwere also obtained. The high temperature and the subsequent decom-position of the DD generate radicals that react with the rubber chainand with the crosslink network, increasing the sulfur content of thetreated GTR.

Moreover, the effect of scCO2 devulcanization process on a GTR us-ing DD was investigated in order to evaluate the possible use of thereclaimed material in new blends. The mechanical properties of thenew blends were studied: increasing of elongation at break and a de-

93

C O N C L U S I O N

creasing of modulus and tensile strength were observed. These resultsindicating that the most limiting factor for this devulcanization processis the amount of DD that remains in the treated GTR.

These results have an important outcome since this devulcanizationprocess can be carried out in a short time and at relatively low pressure,with subsequent energy saving.

94

B I B L I O G R A P H Y

[1] E. Bilgili, H. Arastoopour, and B. Bernstein. Pulverization of rubbergranulates using the solid state shear extrusion process: Part II. Powdercharacterization. In: Powder Technology 115.3 (2001), 277–289.

[2] G.E.P. Box, W.G. Hunter, and J.S. Hunter. Statistics for experi-menters: an introduction to design, data analysis, and model building.Wiley series in probability and mathematical statistics: Appliedprobability and statistics. Wiley, 1978. ISBN: 9780471093152.

[3] Gerd Brunner. Applications of Supercritical Fluids. In: Annual Re-view of Chemical and Biomolecular Engineering 1.1 (June 2010),pp. 321–342. ISSN: 1947-5438, 1947-5446.

[4] C. Tzoganakis. Devulcanization of Recycled Tire Rubber using Super-critical Carbon Dioxide.

[5] Sadhan K De, J. R White, and Rapra Technology Limited. Rubbertechnologist’s handbook. English. Shawbury, Shrewsbury, England:Rapra Technology Ltd., 2001. ISBN: 9781847351937 184735193X.

[6] de Voodt P. Erlandsson B. Schoenmakers P.J. Delaunay-BertonciniN. van der Wielen F.W.M. Analysis of low-molar-mass materials incommercial rubber. In: ().

[7] K.A.J Dijkhuis. “Recycling of vulcanized EPDM-rubber mecha-nistic studies into the development of a continuous process usingamines as devulcanization aids”. English. PhD thesis. Enschede:University of Twente [Host], 2008.

[8] EPA. Scrap tire cleanup guidebook. 2006.

[9] L. Eriksson. Design of Experiments: Principles and Applications.Umetrics Academy, 2008. ISBN: 9789197373043.

[10] Yi Fang, Maosheng Zhan, and Ying Wang. The status of recyclingof waste rubber. In: Materials & Design 22.2 (2001), pp. 123 –128.ISSN: 0261-3069.

[11] P.J. Flory and J. Rehner. Statistical mechanics of cross-linked polymernetworks. I and II. In: Journal of Chemical Physics 11.512 (1943).

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R I N G R A Z I A M E N T I

Le prime persone che voglio ringraziare sono la mia famiglia. Non esistono parole perdire grazie ai miei genitori Manuela e Natale e mia sorella Camilla, ai quali dedicoquesta tesi. Sono sempre stati presenti, sia nei momenti di gioia che nei momenti didifficolta, sostenendomi sempre e sorreggendomi nei nella mia insicurezza. Lo stessodiscorso e valido per i miei nonni, Franco e Angela appoggiandomi e standomi semprevicino.

Un grazie speciale va alla mia relatriche: la Professoressa Marina Lasagni, per tuttol’aiuto e la grande cortesia dimostratemi. Inoltre, ringrazio sentitamente il mio corre-latore il Dott. Ivan Mangili, che mi ha entusiasmato, rincuorato e anche agitato mache e stato sempre presente, persino dall’altra parte del mondo.

Un grazie e doveroso anche alla Dott.ssa Manuela Anzano che dal primo giorno hamesso a mia disposizione la sua esperienza e mi ha aiutato a non farmi perdere divista gli obbiettivi importanti. Ringrazio anche tutte le persone che hanno condivisocon me quest’anno di tesi, la Professoressa Elena Collina, la dottoressa Elsa Piccinellie i miei compagni Noemi, Alessandra, Ruggero e Gabriele perche anche un sorriso ouna battuta d’incoraggiamento puo fare tanto in certi momenti.

Un ringraziamento va ai miei compagni di studi, i quali sono stati sempre con me neigiorni di sconforto ma specialmente nei momenti felici. Voi avete reso l’universita unposto speciale nel quale sono sempre venuto con il sorriso e che considero una secondacasa. In queste poche righe non riesco a ringraziare tutti pero non posso non dire ungrazie al mio compagno di caffe Marco che ha accompagnato ogni mia pausa parlandodi tutto, a Sara che tra le varie cose ha reso possibile la mia avventura in LATEXe Giuliauna ragazza che dal primo giorno e stata per me un punto di riferimento (anche senon volevi fermarti a studiare matematica I con me!). Non posso non scrivere diSimone che mi dona ogni volta che lo incontro un piacevole sorriso. Un grazie specialeva Cesare con il quale ho condiviso veramente tutto, sei veramente l’amico che tuttidovrebbero avere.

Un pensiero lo voglio dedicare anche a Marica con la quale ho condiviso tanti viaggiper andare o per tornare dall’universita ma specialmente tanti ritardi! Ringrazioanche Andrea per aver osteggiato i fine settimana di preparazione degli esami ricor-dandoci che la vita non era solo composta dallo studio.

Due parole le voglio dedicare anche al mio club: il Goggler. Mi ha sempre permessodi staccare e non pensare piu ai problemi, per qualche ora. Anche se non mancano imomenti di difficolta sappiate che vi considero veramente una seconda famiglia.

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