food and bioproducts processing 8 9 ( 2 0 1 1 ) 528–536

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Food and Bioproducts Processing

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Process simulation and debottlenecking for an industrialcocoa manufacturing process

Omar Alshekhli, Dominic C.Y. Foo ∗, Ching Lik Hii, Chung Lim LawDepartment of Chemical and Environmental Engineering, University of Nottingham Malaysia, Broga Road,43500 Semenyih, Selangor, Malaysia

a b s t r a c t

Cocoa is a common ingredient for various food and confectionery products. Industrial production of this ingredient

however is normally not optimised, due to the lack of appropriate analytical tools. Furthermore, cocoa processing is

normally operated in semi-continuous mode, and this adds to the difficulty in optimising the various unit operations

involved. In this work, a computer-aided process simulation tool was used to model and debottleckneck an industrial

cocoa manufacturing process, with the aim to identify an economically viable production scheme that would double

the current production rate.

© 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Keywords: Cocoa production; Batch processes; Modelling and optimisation; Process debottlenecking

tool applied in the bulk and petrochemical industries since

1. Introduction

Cocoa (Theobroma cacao L.) is cultivated primarily for the cocoabeans which can be further processed into various productssuch as cocoa powder, liquor, butter and cake (Beckett, 1993).These products are the basic ingredients typically used in themanufacturing of various chocolate confectionery productsworldwide. In addition, cocoa powder and butter are also usedin the formulation of various pharmaceutical, cosmetic andtoiletry products.

Malaysia is a relatively new-comer in the cocoa growingand processing industries. Since the beginning of commercialcultivation of cocoa in the 1950s and the establishment of thecocoa grinding industry in 1973, the growth of cocoa plant-ing and processing industries have led Malaysia to becomeone of the major cocoa producers in the world (InternationalCocoa Organization, 2007). The cocoa industry of Malaysiagrew rapidly to reach a peak production of 247,000 tonnes ofcocoa beans in 1990. However, the growth started to slow downsoon after, and eventually reached a production of 270,260tonnes in 2006 (Malaysian Cocoa Board, 2007). From the viewpoint of economic sustainability, the main aim is to increasethe income or the return of cocoa industrial sector by increas-

ing the productivity, efficiency and quality in its processing

∗ Corresponding author. Tel.: +60 3 8924 8130; fax: +60 3 89248017.E-mail addresses: omar alshekhli 3@yahoo.com (O. Alshekhli), dom

ching-lik.hii@nottingham.edu.my (C.L. Hii), chung-lim.law@nottinghamReceived 1 March 2010; Received in revised form 17 September 2010; A

0960-3085/$ – see front matter © 2010 The Institution of Chemical Engidoi:10.1016/j.fbp.2010.09.013

through the adoption of good processing practices (Azhar,2007).

The development and modification of any industrial pro-cess is a long term task that takes considerable effort tocomplete. Furthermore, significant investments are neededfor most cases. In the food processing industry where cocoamanufacturing lies, techniques or tools that can be used toevaluate alternatives and speed up the development processcan have a great impact in overcoming the obstacles statedabove. Computer-aided process simulation (CAPS) is one of thetools that can be applied to achieve this objective. It involvesthe use of computers to perform steady-state heat and massbalancing, as well as sizing and costing calculations for a pro-cess (Westerberg et al., 1979). CAPS offer various advantagesfor process analysis. It enables the identification of missingparameters and predicts the behaviour of an integrated pro-cess under varying operating conditions. CAPS tools that areequipped with economic analysis functionality can be usedto pin-point the economic “hot-spot” of a process, i.e. the pro-cessing step of high capital and operating cost which gives lowprocess yield and/or production throughput (Koulouris et al.,2000; Petrides et al., 2002a). The CAPS is a common analytical

inic.foo@nottingham.edu.my (D.C.Y. Foo),.edu.my (C.L. Law).ccepted 20 September 2010

the late 1960s. However this tool is relatively new to other

neers. Published by Elsevier B.V. All rights reserved.

food and bioproducts processing 8 9 ( 2 0 1 1 ) 528–536 529

Table 1 – Components used in simulation flowsheet.

Components Referenced component(newly registered components)

Updated properties

Fats – –Nitrogen – –Oxygen – –Water – Normal boiling point = 160 ◦CImpurities Debris –Potassium bicarbonate Water MW = 138.2

Normal boiling point = 115 ◦CNormal freezing point = −30 [26]

Cocoa shell Glucose ––

pt(wea

wptrttfi

2

Ftcvppbvfat

Table 2 – Composition of the cocoa beans (De Zaan,1999).

Fat 45 wt%Impurities 2 wt%Shell 12 wt%Solid 33.5 wt%

Solid Biomass

rocess industries. For instance, in biochemical production,he use of CAPS has only been reported since middle 1990sPetrides, 1994; Athimulam et al., 2006). More recently, CAPSere also being used in pharmaceutical production (Petrides

t al., 2002b; Tan et al., 2006), milk processing (Bon et al., 2010),s well as water treatment processes (Petrides et al., 2001).

In this work, an industrial cocoa manufacturing processas modelled based on data collected from a productionlant as well as from the literature. CAPS was used to iden-ify the process bottlenecks that restrict higher productionates. Several debottlenecking strategies were then developedo debottleneck the manufacturing process for higher produc-ion and profitability. Economic evaluation was also performedor each debottlenecking scheme to identify the most econom-cal viable solution.

. Base case simulation

ig. 1 shows the base case simulation flowsheet for an indus-rial cocoa manufacturing process which was modelled in aommercial batch simulation software, i.e. SuperPro Designer7.5 (Intelligen, 2008). Four products are produced from thisrocess, i.e. cocoa powder, butter, cakes and liquor. The majorrocessing steps include cleaning, roasting, winnowing andreaking, alkalisation, drying, grinding, crushing and pul-erising. Components used in the simulation flowsheet areound in Table 1. Note that not all components used were

vailable in the software component databank, e.g. impuri-ies, potassium bicarbonate, cocoa shell and solid, and hence

Fig. 1 – Simulation flowshee

Water 7.5 wt%

are approximated using some reference components withsimilar physical/chemical properties (see Table 1). The rawmaterial used for the process, i.e. cocoa bean (5000 kg/batch),was also added as a new mixture (termed as stock mixturein SuperPro Designer), with the mass composition given inTable 2.

Before undergoing the main processing steps, cocoa beansare sent from silo P-1/SL-101 (i.e. procedure P-1 in vessel SL-101) for cleaning in P-2/BF-101, where air at room temperatureis used as the cleaning agent to remove all its impurities.The quantity of air is set with the auto-adjust function of thesimulation software, i.e. quantity is automatically determinedbased on the specification set by the user (Intelligen, 2008).The solid content in the cake is set to 99.99%. The cleanedcocoa beans are then sent to undergo thermal pre-treatment(P-3/DRD-101), where the beans are heated to 150 ◦C (by air at160 ◦C) to increase the natural vapour pressure within the ker-nel. A moisture removal of 2% is also specified in the unit. Thethermal pre-treatment helps the cocoa beans to separate the

shell from the kernel in the next processing step, i.e. breakingand winnowing (P-4/GBX-101). In P-4/GBX-101, the extent of

t for base case model.

530 food and bioproducts processing 8 9 ( 2 0 1 1 ) 528–536

t Cha

Fig. 2 – Operations Gant

shell removal is set as 98.5%. The product of this unit (knownas nibs) leaves at 60 ◦C, and is then sent to storage (P-6/SL-102)prior to feeding the alkalisation process (P-7/V-102).

Five operations are performed in P-7/V-102, i.e. namelymaterial transfer-in from P-5 and P-6, agitation, alkalisa-tion and transfer-out. The main operation, i.e. alkalisation,involves the treatment of cocoa nibs with potassium carbon-ate solution. A heating operation is used to approximate thisprocessing step, where high pressure steam is used to raisethe mixture temperature to 150 ◦C. Prior to the process, thepotassium carbonate solution (K2CO3) is prepared in tank P-5/V-101, by mixing 440 kg water with 70 kg K2CO3, while steamis used to raise its temperature to 100 ◦C. The alkaline-treatednibs from P-7/V-102 have a moisture content of 16 wt%. So, atray drying unit (P-8/TDR-101) is used to reduce the moisturecontent to about 5 wt%. To achieve this, water removal in thedryer is set at 73% (with 45 ◦C air of auto-adjust quantity), andthe nibs using air at 45 ◦C (with auto-adjusted quantity) thatleaves at 70 ◦C.

The nib is then forwarded to the roasting operation (P-9/DRD-102), which is approximated as a heating operationwith agitation. Steam is used in the roasting operation to raisethe nibs’ temperature to 100 ◦C. Besides, it also serves as a ster-ilisation medium for the nibs (De Zaan, 1999; Kirk-Othmer,2003). Moisture removal is set at 60% so that the roaster efflu-ent will have a moisture content of 2%. Note that no air isrequired in this processing step, so the inlet air stream is setat zero.

The roasted nibs are then forwarded to a two-stage grind-ing operations, i.e. P-10/GR-101 and P-11/GR-102. The formeris used to pre-grind the cocoa nibs into coarse particles whilethe latter is for fine grinding, to produce the cocoa liquor. Fromthe second grinder, 10 wt% of the cocoa liquor is sent to theproduct silo (P-13/SL-103), while the remaining is forwardedto the pressing unit.

The pressing unit is approximated as a plate-and-framefiltration unit (P-14/PFF-101). The pressing operation will par-

rt for base case model.

tially reduce the fat content of the cocoa liquor by meansof high pressure hydraulic presses. In the operation model(P-14/PFF-101), the removal of fats, shell, solid and moisturecontent are set as 10, 100, 95 and 50% respectively, with a fil-trate flux of 400 L/m2-h and cake thickness of 10 cm. Note thatthis unit is operated three times within the same batch oper-ation (in order to maintain the cake thickness of 10 cm). Notealso that the model assumes that there are 20 pots in the press-ing unit, with a diameter of 17 cm (giving a total area of 1.8 m2).The resulting cakes hence have a fat content of 12 wt% (DeZaan, 1999), while the liquid portion from the pressing unit isthe cocoa butter, with a solid content of 3.9 wt% and moisturecontent of 1.9 wt%. The butter is sent to the product storage (P-15/SL-104); while the cocoa cake is then stored in the storagetank (P-16/SL-105) for further processing.

Next, 10 wt% of the cocoa cake is sent to product silo (P-18/SL-106), while the remainder is crushed and pulverised inP-19/SR-101 in order to convert into cocoa powder. The finalstep of the manufacturing process involves the stabilisation ofthe cocoa powder by a cooling operation. In this unit, the hotpowder particles are cooled when flowing through the pipes,as a result of the thermal convection between the movingpowder particles and the surrounding air. Since none of thestandard models in the software represents this unit well, ithas been approximated using two individual models, i.e. sta-bilising (P-20/PC-101) and cooling (P-21/HX-101). In the latter,the outlet temperature is set at 32 ◦C. After the cooling opera-tion, the cocoa powder is sent to the product silo (P-22/SL-107).

The Operations Gantt Chart for the base case simulationmodel is shown in Fig. 2, with the details of the schedulingsummary shown in Table 3. The process time (PT) and starttime (ST) for each operation in the base case model are doc-umented. The setup time for all operations is negligible, andhence are set to zero (not shown in Table 3). Note also thatthe process time for some operations are dependent upon

other operations (in the same or another procedure). This ismodelled using the Master-Slave Relationship (MSR) function of

food and bioproducts processing 8 9 ( 2 0 1 1 ) 528–536 531

Table 3 – Scheduling summary for the base case model.

Procedure/equipment Operation Process time (min) Start time (min)

P-1/SL-101 Storing of cocoa beans 15 Beginning of batchP-2/BF-101 Cleaning cocoa beans 60 After completion of P-1P-3/DRD-101 Thermal pre-treatment 60 Starts with P-2P-4/GBX-101 Winnowing and breaking 60 After completion of P-3P-5/V-101 Charge-in water to P-5 20 Starts 27 min before the start of P-4

Charge-in K2CO3 to P-5 20 Starts with charge-in water to P-5Agitation 67 After charge-in K2CO3 to P-5Heating 7 Starts with agitationTransfer-out diluted K2CO3 MSR with P-4 Starts with P-4

P-6/SL-102 Storing of nibs 60 Starts with transfer-out in P-5P-7/V-102 Transfer-in from P-5 MSR with transfer-out in P-5 Starts with transfer-out in P-5

Transfer-in from P-6 MSR with P-6 Starts with transfer-in from P-5Agitation 115 After transfer-in from P-6Alkalisation 60 Starts with agitationTransfer-out nibs to P-8 60 After completion of alkalisation

P-8/TDR-101 Transfer-in from P-7 MSR with transfer-out in P-7 Start with transfer-out in P-7Drying 60 Starts with transfer-in from P-7Transfer-out to P-7 60 After completion of drying

P-9/DRD-102 Roasting 60 Starts with transfer-out in P-8P-10/GR-101 Coarse grinding 75 15 min before completion of P-9P-11/GR-102 Fine grinding MSR with P-10 Starts with P-10P-12/FSP-101 Flow splitting MSR with P-11 Starts with P-11P-13/SL-103 Storing of cocoa liquor 75 Starts with P-12P-14/PFF-101 Pressing 20 (3 cycles/batch) After completion of P-13P-15/SL-104 Storing of cocoa butter 60 Starts with P-14P-16/SL-105 Storing of cake 60 Starts with P-15P-17/FSP-102 Flow splitting MSR with P-16 After completion of P-16P-18/SL-106 Storing of cocoa cake 60 Starts with P-17P-19/SR-101 Crushing and pulverizing MSR with P-17 Starts with P17P-20/PC-101 Stabilising MSR with P-19 Starts with P-19P-21/HX101 Cooling 60 Starts with P-20P-22/SL-107 Storing of cocoa powder 60 Start with P-21

SffTptmbao

uperPro Designer (Intelligen, 2008). For instance, the durationor Transfer-in operation in P-8 (the Slave operation) is set toollow the Transfer-out operation in P-7 (the Master operation).he simulation model determines that the batch time for therocess is 8.25 h. In other words, the entire cocoa manufac-uring process takes 8.25 h to reach completion. However, its

inimum cycle time is determined as 3 h, which is contributedy the unit procedure with the longest operating time, i.e.

lkalisation (P-7/V-102). Based on the annual operating timef 7920 h and the minimum cycle time, the base case model

Fig. 3 – Simulation flowsheet for extended base case model (u

determines that the manufacturing process has an annualproduction throughput of 2638 batches of cocoa products.

3. Debottlenecking study

Due to increased customer demand, the plant authority isplanning for higher production in the near future. A target of

100% increase of current production has been set. This callsfor a debottlenecking study in order to overcome the current

nit procedures in box are operated in continuous mode).

532 food and bioproducts processing 8 9 ( 2 0 1 1 ) 528–536

art fo

Fig. 4 – Equipment Occupancy Ch

obstacle for higher production. Prior to these studies, the basecase model was extended where several production units areoperated in continuous mode. This is because some of theoperations are more efficient when operated in continuousmode, and hence will be implemented by the plant authorityin the near future. The simulation flowsheet of the extendedbase case is shown in Fig. 3, where the processing steps tobe operated in the continuous mode (i.e. cleaning, thermalpre-treatment, winnowing and breaking, crushing and pul-verising, as well as stabilising) are shown in dashed boxes.The annual throughput for the extended base case is main-tained as 2638 batches as in the base case model (minimumcycle time of 3 h). Three debottlenecking strategies are nextproposed in order to increase the throughput of the process.

Fig. 4 shows the Equipment Occupancy Chart for theextended base case model. The equipment bars for the alkali-sation vessel (V-102) in consecutive batches are touching each

other, which shows that this vessel is the time bottleneck: in

Fig. 5 – Equipment Occupancy Chart

r the extended base case model.

other words, the alkalisation vessel limits the throughput ofthe current production scheme. This bottleneck needs to beeliminated, which can be done by adding an additional alkali-sation vessel operating in stagger mode with the existing vessel.In practice, the new vessel will be operated in parallel with theexiting vessel, but at slightly different time. The simulationmodel will determine the appropriate start time of this newlystaggered equipment (Intelligen, 2008). Note that the feed con-tent for both vessels remains the same as before. Hence, theyoperate with the same duration. However, with the availabil-ity of the new vessel, the alkalisation process of a consecutivebatch can now start before the end of an earlier batch. This isshown in Fig. 5, where the newly added alkalisation vessel(STG01�V-102) enables the start of the alkalisation opera-tion for the 2nd and 4th batches (and other batches eventlynumbered) before the 1st and 3rd batches (and other oddnumbered batches) complete their operations. The simulation

model determined that the new minimum cycle time for the

for debottlenecking Scheme 1.

food and bioproducts processing 8 9 ( 2 0 1 1 ) 528–536 533

hart

pp5tnto

1(mOvtstto

Fig. 6 – Equipment Occupancy C

rocess is reduced to 2 h, which results in an increased annualroduction throughput to 3957 batches. This corresponds to a0% increase from the extended base case model. However,his production increase is obtained at the expense of oneew vessel (economic performance is discussed in later sec-ion). This is designated as Scheme 1 for the debottleneckingptions.

From Fig. 5, it is evident that the tray drying operation (TDR-01) becomes the next process bottleneck. A second tray dryerSTG01�TDR-101) is then installed, and operated in stagger

ode with the existing unit in Scheme 1 (see Equipmentccupancy Chart in Fig. 6). Note that the new alkalisationessel in Scheme 1 is retained in this scheme. The simula-ion model determines that the annual throughput for thischeme is increased to 5276 batches, i.e. 100% increase fromhe extended base case model (minimum cycle time is reducedo 1.5 h). This is identified as Scheme 2 for the debottlenecking

ptions, which has achieved the debottlenecking objective.

Fig. 7 – Equipment Occupancy Chart

for debottlenecking Scheme 2.

Even though Scheme 2 achieves the desired productionincrease, an additional debottlenecking option (Scheme 3) wasalso analysed to check if a better debottlenecking schemeexists. As the process bottleneck (TDR101) is eliminated inScheme 2, V-102 once again becomes the new bottleneck (seeFig. 6). Thus, in debottlenecking Scheme 3, an additional alka-lisation vessel is introduced (STG02�V-102) to be operatedin stagger mode with the two alkalisation vessels in Scheme2 (see Equipment Occupancy Chart in Fig. 7). Note that allunits added in previous schemes are maintained. The annualthroughput for this scheme is 5458 batches (minimum cycletime 1.45 h), which is an increase of 107% from the extendedbase case model. Table 4 shows the equipment used in thesimulation flowsheet in Fig. 3 and their specification.

At this end, it is worth noting that the units that areoperated in the continuous mode (i.e. cleaning, thermal pre-treatment, winnowing and breaking, etc.) have also increased

their processing capacity. This is due to the fact that the pro-

for debottlenecking Scheme 3.

534 food and bioproducts processing 8 9 ( 2 0 1 1 ) 528–536

Table 4 – Capacity of major equipment in Scheme 3.

Procedure/equipment Specification

P-1/SL-101 Volume = 4.5 m3

P-2/BF-101 Belt width = 0.2 mP-3/DRD-101 Diameter = 0.4 m; length = 2 m;

capacity = 5.2 kg/hP-4/GBX-101 Rated throughput = 3374 kg/hP-5/V-101 Volume = 0.5 m3

P-6/SL-102 Volume = 5.6 m3

P-7/V-102 Volume = 5.6 m3

P-8/TDR-101 Tray area = 62.1 m2 (4 units)P-9/DRD-102 Diameter = 1.2 m;

length = 5.8 m;capacity = 124.3 kg/h

P-10/GR-101 Rated throughput = 4016 kg/hP-11/GR-102 Rated throughput = 4016 kg/hP-12/FSP-101 Rated throughput = 4016 kg/hP-13/SL-103 Volume = 0.5 m3

P-14/PFF-101 Filter area = 1.8 m2 (3 units)P-15/SL-104 Volume = 2.5 m3

P-16/SL-105 Volume = 2.2 m3

P-17/FSP-102 Rated throughput = 1158 kg/hP-18/SL-106 Volume = 0.2 m3

P-19/SR-101 Rated throughput = 1043 kg/hP-20/PC-101 Pipe diameter = 0.3 m, rated

throughput = 1043 kg/hP-21/HX101 Area = 0.9 m2

3

Table 6 – Stream classification and their economicvalues.

Streams Type Economicvalue ($/kg)

Quantity(kg/batch)

Inlet streamsCocoa beans Raw material 2.80 5000K2CO3 Raw material 0.007 70Water Raw material 0.003 400

Outlet streamsShell Waste 0.03 591Powder product Revenue 1.50 1512Cake product Revenue 0.80 168Butter product Revenue 7.70 1935Liquor product Revenue 4.50 402

P-22/SL-107 Volume = 1.4 m

cess is now handling a larger overall throughput. When thecocoa manufacturing plant was originally constructed ago,all its units featured excess capacity: the increased capacityrequired of these units was verified with the plant manages tolie within the operational limits of these units.

Finally, it is evident that the debottlenecking exercise isnever ending, as there will always be a process bottleneckthat limits the production. For instance, in Scheme 3 V-101becomes the new process bottleneck (see Fig. 7), and calls foradditional unit to be installed. However, debottlenecking exer-cise should stop when its objective has been achieved, i.e. thecurrent production rate is doubled. A summary of the processthroughput for each scheme is presented in Table 5.

4. Economic analysis

Economic analysis is next carried out for each scheme usingthe economic evaluation functions in the software. Prior toeconomic evaluation, the various inlet and outlet streams ofthe process are specified with their associated economic val-ues. This corresponds to the purchase cost for raw material,selling price for revenue and treatment cost for waste streams(see Table 6 for stream type and their economic values). For

the comparison of different debottlenecking schemes, a goodeconomic indicator is the cost-benefit ratio (CBR), which is an

Table 5 – Throughput and economics for each production schem

Process parameters Exten

Throughput

Batch time (h) 6.45Minimum cycle time (h) 3Bottleneck equipment V-102Annual throughput (batches/year) 2,638

Economics

Capital investment ($ million) –Operating cost ($ million/year) 49.54Annual revenues ($ million/year) 50.41Cost benefit ratio (CBR) –

index based on the ratio of benefits obtained for a given expan-sion cost (Blank and Tarquin, 2003; Tan et al., 2006). This isgiven as in Eq. (1):

CBR = Rnew − Rbase

Onew − Obase + I(1)

where Rnew and Rbase are, respectively, the revenue gener-ated from the newly proposed scheme and the base case;while Rnew and Rbase correspond to the operating cost of theseschemes; and I is the capital investment made on the newlyproposed scheme. Note that since all cost terms are reportedon a yearly basis, the capital investment should also be annu-alised, by multiplying with the annualising factor in Eq. (2):

fi = i(1 + i)n

(1 + i)n − 1(2)

where i = fractional interest rate per year, n = number of years.Economic evaluations of each scheme are summarised in

Table 5, which show the capital investment, operating costand revenue. Note that to calculate the CBR, the capital invest-ment is annualised using Eq. (2) to a period (n) of 5 years andan interest rate (i) of 10% (values given by the plant author-ity). From Table 5, it is noticed that Scheme 1 requires theleast investment as well as giving the highest CBR value. How-ever, as discussed earlier, this scheme only achieves 50% of thedesired increase in throughput.

On the other hand, both Schemes 2 and 3 achieve thedesired throughput increase. Their CBR values (1.12 and 1.13,respectively) indicate that both schemes are equally economi-cally attractive in their economics (even though slightly lowerthan that in Scheme 1). For Scheme 3, an additional cap-ital investment of $454 thousand and an annual operatingcost of $2.88 million are required to generate an addi-

tional revenue of $3.48 million (as compared to Scheme 2).The selection of the appropriate debottlenecking scheme for

e.

ded base case Scheme 1 Scheme 2 Scheme 3

6.45 6.45 6.452 1.5 1.45TRD-101 V-102 V-1013,957 5,276 5,458

2.42 8.08 8.5370.65 92.31 95.1875.62 100.83 104.311.16 1.12 1.13

food and bioproducts processing 8 9 ( 2 0 1 1 ) 528–536 535

Fig. 8 – Effect of cocoa bean cost on CBR values.

ial f

ia

roitaccsfoi

Fig. 9 – Effect of financ

mplementation is dependant upon the decision of the plantuthority.

To this end, it is also worth analysing the effect of raw mate-ial cost and the financial factors (values of i and n in Eq. (2))n the CBR values of each schemes. From Table 6, it is eas-

ly observed that the cocoa bean is the main contributor forhe raw material cost (due to its relatively high purchase costnd large consumption quantity), hence it is the main item foronsideration here. Fig. 8 summarises the effect of cocoa beanost (ranging between $2.5–3.0/kg) on the CBR values for eachcheme. As shown, the CBR values range between 0.98–1.61or Scheme 1 and 1.00–1.37 for both Schemes 2 and 3. On the

ther hand, the values of i (10 and 20%) and n (5 and 10 years)

n Eq. (2) were varied and their effects on the CBR values for

actors on CBR values.

each scheme (while keeping the cocoa cost at $2.8/kg) werereported in Fig. 9. As shown, the CBR values range between 1.11and 1.17 for all schemes. Hence, it can easily be concluded thatthe financial factors do not have a significant impact on theCBR values, as compared to cocoa bean cost. The sensitivityanalysis is also useful for the plant authority to determine therobustness of the selected debottlenecking schemes. In thisregard, Schemes 2 and 3 are found to be more robust options(i.e. with lower fluctuation) as compared to Scheme 1.

5. Conclusion

An industrial cocoa manufacturing process was modelledusing a commercial batch process simulation software. The

536 food and bioproducts processing 8 9 ( 2 0 1 1 ) 528–536

Process Flowsheeting. University Press Cambridge,

extended base case process features an annual productionrate of 2638 batches of cocoa products, with the alkalisationidentified as the overall process bottleneck. Various debot-tlenecking schemes were then developed by staggering thebottleneck equipment, aiming to double the current produc-tion rate, i.e. 5276 batches/year. Two of the schemes were ableto achieve the desired output. In addition, analysis on theireconomic performance were carried based on cost-benefitratio. It was found that both schemes are equally economicallyattractive, with one of the schemes requiring higher capitaland operating costs but yielding greater production.

Acknowledgement

The assistance of KL Kris Cocoa Manufacturer in providing thetechnical details is highly appreciated. Comments from thereviewer is acknowledged.

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