api 337 emission factors for refinery equipment

8/2/2019 API 337 Emission Factors for Refinery Equipment

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%A P I P U B L 83 3 7 9 b 0 7 3 2 2 9 0 0 5 5 8 3 8 5 0 1 8AmericanPetroleum se!!!nstitute mide

Development of EmissionFactors for Leaks in RefineryComponents in HeavyLiquid Service

Healthand Environmental Affairs DepartmentPublication Number 337August 1996

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vided by IHS under license with API Licensee=Shell Global Solutions International B.V. Main/5924979112, User=LavendeNot for Resale, 03/30/2011 03:41:59 MDTreproduction or networking permitted without license from IHS

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A P I P U B L X 3 3 7 9 6 I 7 3 2 2 9 0 0558386 T 5 4 IL-

. .EnvimnnnikJ Partanrbip

One of the most significant ong-term trends affecting the future vitality of the petroleum ndustry s thepublics concerns about the environment. Recognizing this trend, API member companies have developeda positive, forward-looking strategy called STEP: Strategies for Todays Environmental Partnership. Thisprogram aims to address public concerns by improving our industrys environmental, health and safetypetformance; documenting performance improvements; and communicating them to the public. Thefoundation of STEP is the API Environmental Mission and Guiding Environmental Principles.API ENVIRONMENTAL MISSION AND GUIDING ENVIRONMENTAL PRINCIPLES

The members of the American Petroleum Institute are dedicated to continuous efforts to improve thecompatibilityof our operations with the environment while economically developing energy resources andsupplying high quality products and services to consumers. The members recognize the importance ofefficiently meeting societys needs and our responsibility to work with the public, the government, andothers to develop and to use natural resources in an environmentally sound manner while protecting thehealth and safety of our employees and the public.To meet these responsibilities, API members pledge tomanage our businesses according to these principles:

9 To recognize and to respond to community concerns about our raw materials, products andoperations.6 To operate our plants and facilities, and to handle our raw materials and products ina mannerthat protects the environment, and the safety and health of our employees and the public.9 To make safety, health and environmental considerations a priority in our planning, and ourdevelopment of new products and processes..5 To advise promptly, appropriate officials, employees, customers and the public of informationon significant industry-related safety, health and environmental hazards, and to recommendprotective measures.9 To counsel customers, transporters and others in the safe use, transportation and disposal ofour raw materials, products and waste materials.9 To economically develop and produce natural resources and to conserve those resources byusing energy efficiently.9 To extend knowledge by conducting or supporting research on the safety, health andenvironmental effects of our raw materials, products, processes and waste materials.9 To commit to reduce overall emission and waste generation..5 To work with others to resolve problems created by handling and disposal of hazardoussubstances from our operations.o* To participate with government and others in creating responsible laws, regulations andstandards to safeguard the community, workplace and environment.9 To promote these principles and practices by sharing experiences and offering assistance toothers who produce, handle, use, transport or dispose of similar raw materials, petroleumproducts and wastes.

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Not for Resale, 03/30/2011 03:41:59 MDTreproduction or networking permitted without license from IHS

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Development of Emission FactorsFor Leaks in Refinery Components inHeavy Liquid ServiceHealth and Environmental Affairs Department

API PUBLICATION NUMBER 337PREPARED UNDER CONTRACT BY:

HALTABACK OMPANY378 PASEO ONRISAWALNUT, ALIFORNIA,1789

AUGUST 1996

AmericanPetroleumIns itutepyright American Petroleum Institute


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~

A P I PUBL* 337 9 b 0 7 3 2 2 9 0 0 5 5 8 3 8 8 8 2 7

FOREWORD

API PUBLICATIONS NECESSARILY ADDRESS PROBLEMS OF A GENERALNATURE. WITH RESPECT TO PARTICULAR CIRCUMSTANCES, LOCAL, STATE,AND FEDERAL LAWS AND REGULATIONS SHOULD BE REVIEWED.API IS NOT UNDERTAKING TO MEET THE DUTIES OFEMPLOYERS, MANUFAC-TURERS, OR SUPPLIERS TO WARN AND PROPERLYTRAINAND EQUIP THEIREMPLOYEES, AND OTHERS EXPOSED, CONCERNING HEALTH AND SAFETYRISKS AND PRECAUTIONS, NOR UNDERTAKING THEIR OBLIGATIONS UNDERLOCAL, STATE, OR FEDERAL LAWS.NOTHING CONTAINED IN ANY API PUBLICATION IS TO BE CONSTRUED ASGRANTING ANY RIGHT, BY IMPLICATION OR OTHERWISE, FOR TH E MANU-FACTURE, SALE, OR USE OF ANY METHOD, APPARATUS, OR PRODUCT COV-ERED BY LETTERS PATENT. NEITHER SHOULD ANYTHING CONTAINED INITY FOR INFRINGEMENTOFLETTERS PATENT.THE PUBLICATION BE CONSTRUED AS INSURINGANYONEAGAINST LIABIL-

Copyright O 1996 American Petroleum Instituteiii



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A P I P U B L x 3 3 7 96 0732290 0 5 5 8 3 8 9 7 6 3

ACKNOWLEDGMENTS

THE FOLLOWING PEOPLE ARE RECOGNIZED FOR THEIR CONTRIBUTIONSOFTIME AND EXPERTISE DURING THIS STUDY AND IN THE PREPARATIONOFTHIS REPORTAPI STAFF CONTACT

Karin Ritter, Health and Environmental Affairs Department

AIR TOXIC MULTI-YEAR STUDY WORKGROUPHL EMISSION FACTORS OVERSIGHT COMMITTEE

Minam Lev-On, Chairperson, ARCORobert D. Andrew, Mobil

Dan Isaacson, BP OilDan VanDerZanden, Chevron ,

API FINANCE. ACCOUNTING. AND STATISTICS DEPARTMENTGina PapushPaul Wakim

HAL TABAC K COMPANYMichael Godec, Data Analyst& Test Engineer

Steven A. Momll, EIT, Test DirectorH.J. Taback, PE, DEE, QEP, REA, Principal Investigator& Project Manager



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A P I P U B L * 3 3 7 96 = 0732290 0558L90 4 8 5 =ABSTRACT

The objective of this program w as to develop a set of emission factors (expressed inlb h k o m p o n en t) applicable to refinery components (valves, flanged connectors, non-flangedconnectors, pumps, open-ended lines, and others) in heavy liquid (HL) service. To accomplishthis, more than 2 11,000 existing HL screening values from Southern California refineries werecompiled and compared with 2,500 new HL screening measurements taken at two refineries inWashington State. Southern California is under stringent emission control regulations due toextreme non-attainment of the National Ambient Air Quality Standards (NA AQ S); thus, itsscreening data may not be representative of refineries w ithout stringent fugitive emissioncontrols. How ever, the S outhern California screening data were compared to screeningmeasurem ents made at refineries in Washington State, which is an area in attainment of theNA AQ S and therefore without fugitive emissions control. There was no significant statisticaldifference found in emission factors between the two areas; the results sugge st there is nodifference in emissions from heavy liquid components in areas with and w ithout leak detectionand repair (LDA R) programs. The new emission factors range from 65% to 86% less than thecurrent EPA emission factors.



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A P I P U B L * 3 3 7 96 0 7 3 2 2 9 0 0558L91 311

TABLE OF CONTENTS



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uuI.!22- 12-22-32-43- l a3 - lb3 - l c3 - ld3- le3-2a3-2b3 - 2 ~3-2d3-2e3-3

LIST OF FIGURE S

-L Component Leak Rate Distribution - Refinery C 1 .............................. 2-4HL Compo nent Leak Rate Distribution - Refinery C2 .............................. 2-4HL Compo nent Leak Rate Distribution - Refinery C3 .............................. 2-5HL Com ponent Leak Rate Distribution - Refinery C4 .............................. 2-5Leak Rate Distribution by Component for Refinery W 1 - Fittings ---------------- -4Leak Rate D istribution by C omponent for Refinery W1 - Flange -----------------3-4Leak Rate D istribution by C omponent for Refinery W1 - Pump ------------------ 3-4Leak Rate D istribution by Component for Refinery W1 - Valve ------------------3-5Leak Rate D istribution by C omponent for Refinery W1 - Aggregate -------------3-5Leak Rate D istribution by Component for Refinery W2 - Fittings ----------------3-6Leak Rate Distribution by Component for Refinery W2 - Flange -----------------3-6Leak Rate Distribution by Component for Refinery,W2 - Pump ------------------3-6Leak Rate Distribution by Component for Refinery W2 - Valve ------------------3-7Leak Rate D istribution by Component for Refinery W2 - Aggregate -------------3-7Com parison of the Aggregate Leak Rate Distributionfor S outhern California and W ashington Refineries ................................. 3-8

A-12-3 Illustration of the Combined Effect of Stream Type and T emperature----------



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A P I P U B L X 3 3 7 96 D 0 7 3 2 2 9 0 0558393 394

LIST OF TABLES

TableES- 12- 12-23-13-23-33-43-53-6

uEm ission Factors for Components in HL Service .................................. ES-3Correlation Equations, Default Zero E mission Rates, and Pegged EmissionRates Used for Em issions Calculations ................................................ 2-2Average Em ission Factors for Components in Heavy Liquid Service ------------2-3Washington S tate Test Ma trix---_--_-_-__-_-_______- -____-_____________________- - - --1Co mponen t Stream Counts (Washington Refineries)-------------------------------- 3-2Em ission Data by Stream Type (W ashington Refineries)--------------------------- 3-3Emissions Factors by Component S ize (Washington Refineries)------------------3-9Emissions Factors by Temperature (Washington Refineries) .................... 3-1OAggregate Emissions Factors by Component Type (Washington Refineries) - 3-1 1

A- 1A-2

Suggested Heavy Liquid Service Emission Factors--------------------------------- A-2Southern California Refineries - Simple Average Em ission Factors ------------- A-4

A-3A-4A-5A-6A-7A-8

Washington Refineries - Simple Average Emission Factors ...................... A-5Emission Factors for Reduced Refinery C 1 Data ................................... A-8Emission Factors for Reduced Refinery C2 D ata ................................... A-8Emission Factors for Washington Data by Stream Type .......................... A-13Aggregate Emission Factors for S outhe rn California Refineries----------------- A-16Aggregate Emission Factors for Washington Refineries .......................... A-16

A-9 Em ission Factors for Com bined Southern Californiaand Washington Data _ _ _ _ _ _ _ _ _ _ _ -_ _ _ _ _ _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ _ _ _ _ _ _ _~ ~ ~ ~ ~ ~ -- ------ - ------A-1 8



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A P I P U B L U 3 3 7 9 6 m 0 7 3 2 2 9 0 0 5 5 8 3 9 4 O 2 0

EXECUTIVE SUMM ARYThis report presents the results of a study to develop em ission factors applicable to refinerycomponents in heavy liquid (H L) service. It includes an analysis of whether the type of d istillateor residual hydrocarbon in the stream would influence the emission factors. The objectives wereaccomplished using existing screening data for components in HL service and confirming thosedata with new screening data obtained from refineries without Leak Detection and Repair(LDAR ) programs. These factors, expressed in pounds/hour/component, are such that they canbe m ultiplied by the number of individual components in HL service in a refinery to calculate thevolatile organic compound (VO C) emissions due to leaks from those components. Extensivestatistical analysis of the screening data and related process parameters was perform ed.TECHNICAL APPROACHRefineries in Southern California (SoCal) were solicited for available HL screening data. Fourrefineries responded , providing 2 11,290 discrete screening values. Leak Detection and Repairhas been practiced at these refineries for approximately ten years on components in gas (G ) andlight liquid (LL) service, but not for HL service. Nevertheless, because SoCal has tight emissioncontrols, it was decided to conduct additional HL component screenings in an area withoutLD AR to determine whether or not the Soc a1 HL screening values were representative. The sitechosen for the HL screenings was Washington State, which is in attainment for all of theNational Amb ient Air Q uality Standards for VOCs and where LD AR programs are not requiredexcept for new or modified facilities under the New Source Performance Standards (NSP S).More than 2,500 discrete values were recorded from which the average emission factors for eachcomponent category were computed. A sampling matrix was used for the Washington test whichwould cover a representative range of middle distillate and residual process streams. These datawere com pared to values from S oCal to assess whether they were representative of all refineries.CONCLUSIONSThe screening tests performed as part of the program showed that the emission factors for therefineries in Southern California, for which HL screening data were available, were similar to

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A P I P U B L * 3 3 7 96 0 7 3 2 2 9 0 0 5 5 8 1 9 5 T b 7

emission factors determined based on m easurements at the W ashington State refineries. Bothsets of em ission factors are lower than those published in EPAs Protocol for Equipment LeakEstimates, Contract 68-dl -0 117, for EPA by Radian Corp., June 1993 (EPA 1993). Th eWashington State factors are lower than those from Southern California for all components withthe excep tion of pumps. Th e higher pum p value in Washington State was influenced by on epum p leaking at a high rate.

The screening values for components in HL service were found to be independent of streamtemperature and hydrocarbon composition (as defined by the 10% Distillation Temperature,ASTM Method D86). At first this may seem odd. The heavy liquid streams range fromkerosene to asphalt. Intuitively, kerosene should leak more than asphalt. However, a reasonableexplanation of this observed phenomenon is that the heavier hydrocarbons, which are moreviscous and less volatile at am bient temperatures, inherently circulate in h igher temperatureprocess streams. Therefore, for the medium-w eight middle distillate, the heavy-weight middledistillate, and the heavy residual streams, it appears that the viscosity and vapor pressure(properties that affect leakage) have a similar effect on the screening values.

In this report, the SoCal data are presented and discussed along with the details of theWashington State tests. An extensive statistical analysis of the various data sets is presented inAppendix A discussing various methods of combining the data to derive emission factors. Theconclusions reached as a result of the statistical ana lysis follow:

. . . . . . .* e SoC al data have a s& distribution of e m s u n r a t e s from quarter to a-Therefore, in aggregating screening data from different refineries, where individualrefine ries have different amounts of data (Le., 2 quarters, 6 quarters and 7 quarters), theindividual refinery data sets were reduced by averaging repeated measurements for eachcomponent.. . . .reemng data indicates that there is a sienificanthe analysis of the W a s h w o n State scdifference in e w s i o n fac ors am on^ Co-nt tvDes ( e a valve. ~ u m p lHowever,the differen ces in emission factors among the various HL stream types (Le,, mediummiddle distillate, heavy middle distillate and heavy residual) are not considered to bestatistically significant.

. .

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The emission factors for the SoCai refineries and he WashinPtgn refiwries ared s t i c a l l y similar. (For all components except pump seals, the respective emissionfactors for the W ashington refineries are slightly lower than those for the SoCalrefineries.) This serves to demonstrate that there should be no significant differences inHL emission factors between areas with and without LDA R programs, indicating thatthe H L emission factors generated by these screening data should be universallyapplicable to refineries in the U.S.Table ES-1 presents two sets of factors derived using a two-step averaging method, as discu ssedin Appendix A , one based exclusively on SoCal data (where the quantity of data is large) and on ebased on a com bination of SoCal and Washington data. These are compared to the factorscurrently published in EPA s Protocol document. AP I recommends the use of the combinedemission factors. This data set is representative of refineries both with and without LDA Rprogram s. Th e statistical analysis shows no significant difference between the data sets.Table ES-1. Em ission Factors for Com ponents in HL Service

ES-3

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Section 1INTRODUCTION

BACKGROUNDEstimating air pollutants from stationary sources is necessary for com piling emission inventories,determining emission fees, and meeting the conditions of various permits and compliances.Extensive field measurements have been made over the last four years to develop more accurateemission correlation and emission factor data for estimating emissions from leaking pipelinecomponents. For any petroleum industry facility employing leak detection and repair (L DA R)procedures, the com ponent-leak emissions can be estimated using the recorded screening valuesexpressed in parts per million (ppmv) along with com ponent-specific correlation equationsapproved by EPA and found on its Technology Transfer Network (TTN) bulletin board (EPATTN , 1995). For certain types of facilities where LDAR is not practiced, such a s oil productionfields and bulk terminals, EPA emission factors are readily available for use in estimatingemissions.Pipeline com ponents in heavy liquid (HL) service are generally not included in LD AR prog ramsregardless of the facility. Since HL screen ing values are generally not available, it is necessary touse EPAs em issions factors. The HL emission factors published in EPAs 1993 ProtocolforEquipment Leak Estimates (EPA, 1993) were believed to be high, based o n available information.Therefore, API conducted a study to develop m ore accurate HL emission factors.OBJECTIVEThe o bjective of the program was to develop the emission factors and to determine whether or notthe type of distillate or residual hydrocarbon in the HL stream would influence the emissionfactors. Emission factor data were obtained and extensive statistical analysis of the screening dataand related process parameters was performed and discussed.

1-1pyright American Petroleum Institute


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~~-A P I P U B L X 3 3 7 b 0 7 3 2 2 9 0 0 5 5 8 1 9 8 7 7 6 W

APPROACHRefineries in Southern California (SoCal) were solicited for available HL screening data. Fourrefineries responded, providing 2 11,000discrete HL screening values. At those refineries,LDA R has been practiced for approximately ten years on com ponents in gas (G ) and light liquid(LL) service, but not generally for HL service. Nevertheless, because SoCal has tight emissioncontrols, additional HL component screenings were conducted in an area without LDA R todetermine whether or not the Soc a1 HL screening values were representative. Tw o refineries inWashington State were chosen for the HL screen ings. Washington is in attainment of the NationalAmbient Air Q uality S tandards and LDA R programs are not required except for new or m odifiedfacilities under the New Source Performance Standards (NSPS). More than 2,500 discrete valueswere recorded from which the average emission factors for each component category werecomputed. The W ashington test used a sampling matrix which covered a representative range ofmiddle distillate and residual process streams.

REPORT OR GANIZATIONSection 2 presents the SoCal data from four refineries showing leak rate d istribution and averageemission rates for various components. Section 3 presents the Washington State refinery dataincluding the screening values, leak rate distribution, average emissions and the effects of streamcomposition and temperature on leak rate. Section 4 presents the conclusions. The statisticaltreatment of the SoCal, Washington and aggregated data s presented in Appendix A and thediscre te screening and associated measurements, taken at the Washington refineries, are tabulatedin Appendix B.

1-2



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A P I P U B L * 3 3 7 96 0 7 3 2 2 9 0 0 5 5 8 3 9 9 602

Section 2DATA FROM SOUTHERN CALIFORNIA REFINERIES

Four sets of existing screening data, received from S oCal refineries, were used to derive therefinery HL component emission factors. The refineries are designated C1, C2, C3, and C4,respectively. Refinery C1 provided 165,852 values of HL screening data in electronic format.The data were taken over seven quarters, from 1992 to 1994. Refinery C1 has a full LDARprogram and had screened the HL com ponents along with G & LL service components inaccordance with South Coast Air Quality Managemen t Districts (SCAQ MD ) Rule 1173 eventhough the screening of HL components has never been required by the SCA QM D. The datawere taken with a Foxboro Model 108 Organic Vapor Analyzer (OV A) with strict QualityControl (QC) procedures in accordance with EPA Method 21, which stipulates that the OV Aprobe is maintained at the surface of the component being screened.Refinery C2 provided 2 1,41O HL screening values for six quarters also over 1992 to 1994. Thisrefinery had been screening HL components in addition to conducting its SCAQ MD Rule 1173program. Data from Refinery C3 and C 4 were originally obtained in electronic format during aW SPN AP I study in 1992 as part of the p lanning effort for the 1993 refinery screening andbagging study (Radian Corp., 1992). These screening values, which included various componenttypes and services, were measured in the initial two quarters of the Rule 1173 program in 1991.In reviewing the data, it was found that two of the refineries had included HL screening data.The C3 and C4 data set, when merged, comprised a total of 24,028 screening values.The screening values in the three data sets were converted to em ission rates using the latest set ofcorrelation equations and zero and pegged source emission factors accepted by EPA and postedon EPAs TTN (EPA TTN , 1 9 9 9 , which are presented in Table 2- 1.

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A P I P U B L * 3 3 7 96 0 7 3 2 2 9 0 0 5 5 8 2 0 0 1 5 4 =

EquipmentType/Service

Connector/A 1Flange/AllOpen-Ended Line/AllPump/AllValve/AllOtherc/All

Default Zero Pegged Emission Rates Correlation EquationEmission Rate (Ib/hr/comp)(I b/h r/com p)

10,000 ppmv 100,000 ppmv1.65E-05 0.062 0.066 E = 3.33E-06 x (SV)0.7356.84E-07 0.19 0.19 E = 9.79E-06 x (SV)0.7034.41E-06 0.66 0.17 E = 4.76E-06 x (SV)0.7045.29E-05 O. 16 0.35b E = 1.06E-04 x (SV)o.6o1.72E-05 O. 14 0.3 1 E = 5.03E-06 x (SV)0.7468.80E-06 O. 16 0.24 E = 2.91E-06 x (SV)0.89

Table 2-2 summ arizes the Soc a1 component-leak emission data, showing the total numb er ofcomponents of each type screened, the total estimated emissions using the Table 2-1 conversion,and the average em ission factor obtained by dividing the total emissions by the number ofcom ponents. Values are presented for each of the three data sets. In each case the averageemission factors computed are compared to the 1980 refinery HL emission factors as presentedin EPAs June 1993 Protocol document.

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Table 2-2. Average Em ission Factors for Components in Heavy Liquid Service(7 Qtrs. of Refinery CI Data, 6 Qtrs. of Refinery C2 data, and 2 Qtrs. of Refuienes C3 & C4 data)

~ ~~

Figures 2-1,2-2,2-3, and 2-4 show the d istribution of the screening values for the aggregate ofall components, for the four refineries, in screening ranges of less than 10 ppmv, 10 to 99 ppmv,1O0 to 999 ppmv, etc. As can be seen, the percentage of components screening greater than1,000 ppmv ranges from 0.02 to 0.7 percent.Refer to A ppendix A for further analysis of these data.

2-3



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A P I P U B L r 3 3 7 96 D 0 7 3 2 2 9 0 0 5 5 8 2 0 2 T 2 7

99.88%100%90%80%70%60%

20%10%0%

0.03% 0.06% 0.01% 0.01% 0.00%=

999 9,999 49.999 50,000

Screening Range (ppmv)Figure 2-1. HL Com ponen t Leak Rate Distribution - Refinery C l (165,852 values)

99.53%100%,QI

60%50%40%30%20 %~-10% --O% -

0.15% 0.04% 0.19% 0.07% 0.02%< = l o 11-99 100- 1,000- 10,000- >=

999 9,999 49,999 50,000

Screening Range (ppmv)

Figure 2-2. HL Component Leak Rate Distribution - Refinery C2 (21,410 values)

2-4



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A P I P U BL * 3 3 7 96 = 0732290 0558203 9 6 397.18%100%

90%40%70%oi 60%er 40%**5 50%30%20%10%0% 1.75% 0.76% 0.09% 0.22% 0.00%I,,,,,=999 9,999 49,999 50,000Screening Range (ppmv)

Figure 2-3. HL C ompone nt Leak Rate Distribution- Refinery C3 (7,767 values)

I 10%L

82.23%90%80%70%60%50%

-t)Oc) 40%BU 30%

15.36%.- . .1.72% 0.49% 0.14% 0.07%

:=lo 11-99 100- 1,000- 10,000- >=999 9,999 49,999 50,000

0%1Screening R ange (ppmv)

Figure 2-4. HL Com ponent Leak Ra te Distribution - Refinery C4 (16,261 values)2-5



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A P I PUBLW337 96 O 7 3 2 2 9 0 0 5 5 8 2 0 4 8 T T

ComponentType

ValvesFittings & FlangesPumps

Section 3NEW SCREENING DATA FROM WASHINGTON STATE REFINERIES

Type of Heavy Liquid ServiceMiddle Distillate, Middle Distillate, Heavy Residual - #6600 600 15600 600 1512 12 4

medium* heavy*

To obtain representative data, a m atrix w as prepared for the screening tests to be performed,asshow n in Table 3-1. The number in each square of the matrix represents the target number ofscreening tests to be performed at two Washington State refineries designated Refineries W l and W2. The basis for this matrix was 350 screenings per day. This is a relatively lowscreening rate because each component had to be properly identified (the components were notID tagged) and certain stream properties (1 0% distillation point and type) and process parameters(temperature and pressure) had to be recorded.

* A definition of midd le distillate medium & heavy serv ice is as follows:Middle Distillate - Medium is defined as streams with a 10% distillation temperatu re between300F and 500F including such products as:Comm ercial Jet FuelSR KeroseneHydro-Desulfurized JeK eroDiesel FuelHome H eating OilHydro-DesulfurizedDieselHydro-Desulfurized Heating OilLight Hydrocracked DistillatesMiddle Distillate - Heavy is defined as streams with a 10% distillation temperature between500 O F and 700F including such products and intermediatesas:

Cat. Cracker FeedHeavy Vacuum Gas OilHeavy Atm ospheric Gas OilHeavy H ydrocracked DistillatesHeavy Cat. Gas Oil

Coker Gas OilLight Atmospheric Gas OilLigh t Cat. Gas OilLight Vacuum Gas Oil

The test program was set up for a two-week screening exercise. Two W ashington Staterefineries were visited, one each week, screening 2,548 components. Table 3- 2 is a breakdown

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A P I P U B L r 3 3 7 76 0 7 3 2 2 7 0 0 5 5 8 2 0 5 7 3 b M

of the sp ecific process streams on which com ponents were screened and the number of each typeof com ponents screened. The streams are listed in order of their 10% distillation temperaturewhich is the temperature at which 10%of the organic would flash off (ASTM M ethod D-86).Table 3-2. Component Stream Counts (Washington Refineries)

Number of Comnonents S c r e e d

.................................................................500 - 699. Middle D istillate. Heavy

Table 3-3 summ arizes the emissions by compon ent type and stream type. Th e emissionstabulated were computed using the recorded screening values and the correlation equations, zero

3-2



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A P I P U B L * 3 3 7 9 b 0 7 3 2 2 9 0 0558206 b 2 =default values and pegged source values shown in Table 2-1. The raw test data are tabulated inAppendix B. An extensive statistical treatment of these data is presented in Appendix A inwhich confidence interval and standard error values are tabulated with the emission factor values.The em ission factors show n in Table 3-3 are the simple average emissions computed for therespective components and stream types.Table 3-3. Em ission Data by Stream Type (Washington Refineries)listillate Group & Total Emissions Emission FactorStream Temp. Comp onent Cou nt (Ib/hr) (Ib/hr/comp)kiddle Distillate. Medium (300- 499F)Fitting 53 5Flange 336

OEL 6Other 46h m P 18Valve 414Total 1,355Fitting 259Flange 290OE L 1Other 33Pump* 13Valve 281Total 877

v (500- 699F)

* One large leaker, pegged at 100,000 pp mResidual ( >700F) Fitting 68Flange 140Other 12Pump 4Valve 92Total 316

0.009390.008820.000130.000410.01615o.013900.0488O. 004270.003790.000000.000460.355800.005000.36930,001 120.000100.0001 10.00265o.001580.0056

Grand Total = 2,548 0.42

1.75E-052.62E-052.1OE-O58.82E-068.97E-043.36B-053.60E-051.65E-051.3 1E-054.41E-061.40E-052.74E-02 *1.78E-054.20E-041.65E-057.1 SE-O78.82E-066.64E-041.72E-051.80E-05

Figu res 3-1 a-e and 3-2 a-e present the leak rate distributions by com ponent and aggregated forRefineries W1 an d W2 respectively. The distributions at the two refineries on a percent ofcount basis are similar, especially if the 51O and 11-99 ppmv ranges are combined.

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vided by IHS under license with API Licensee=Shell Global Solutions International B.V. Main/5924979112, User=Lavende


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~ -~~~

A P I P U B L m 3 3 7 b m O732290 0 5 5 8 2 0 7 5 0 9

99.41%100% ,q

8 O % U I Ic

LcO 50 %8 4 0 % ,E!0.43% 0.14% 0.00% 0.00% 0.00%ins/.


Figure 3-l a. Leak Rate Distributionsby Component for Refinery W 1 - Fittings

95.01%100%90%F180 %8 70%3 50%40%pi 30%20 %10%0%'i; 60% 3.25% 1.74% 0.00% O:OO% 0.00%L< = l o 11-99 100- 1,000- 10,000- >=999 9,999 49,999 50,000

Screening Range (ppmv)Figure 3-lb. Leak Rate Distributions by Component for Refinery W 1- Flange

8.33% 8.33%0.00% 0.00% 4.17%lo%--

O%,rc= 10 11-99 loo- 1,Ooo- 10,Ooo- >=

999 9,999 49,999 50,oOoScreening Range ppmv)

Figure 3-lc. Leak Rate Distributions by Component for Refinery W1 - Pump

3-4


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A P I P U B L* 3 37 96 O 7 3 2 2 9 0 0 5 5 8 2 0 8 Y 4 5 D

7-97.27%

lo0%r0% +

2 60%a2 50%O t40%E 30%a>n

20%t10%t0%i .55% 0.18% 0.00% 0.00% 0.00% I' < = l o ' 11-99 ' 100- ' 1,000- ' 10,000- >=

999 9,999 49,999 50,000


Figure 3-ld . Leak Rate Distributions by Com ponent for Refinery W1 - Valve

97.33%100%90%80%70%0 60%50%

8 40%E 30%20%10%

0%

c

Oc


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1 0 0 % 490%--go%.-: 0%.-6 60%--

8 40%.-2 30%--

*

cc,0 50%.-

20%-~~2.37% 0.59% 0.00% 0.00% 0.00%10%

0%

~.100%490% --80% -- 70%--

o 60%--o 50%--8 40%--i 30%--

20%71

2c,


26/62

A P I P U B L * 3 3 7 96 0732270 0 5 5 8 2 1 0 O T 3 =90.30%100%

90%80%

ci= 70%ou 60%* 50%32 40%2(L1O

3 0 % ~I0%10%0% 5.06% 4.22% 0.42% 0.00% 0.00%I< = l o 11-99 100- 1,000 - 10,000 >=999 9,999 49,999 50,000


rigure 3-2d. Leak Rate D istributions by Compo nent for Refinery W2 - Valve

94.69%

f100%90%80%20%10%0%

3.32% 1.73% 0.00% 0.00% 0.09%

< = I O 11-99 100- 1,000- 10,000- >=999 9,999 49,999 50,000


Figure 3-2e. Leak R ate Distributions by Com ponent for Refinery W2 - Aggregate3-7



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API P U B L a 3 3 7 96 W 0732290 05582LL T I T

90%80%

E 7O%--u 60%--g SO%--og:

rrO

i 40%--30%20Yo1O%O%

The em ission factor for Heavy Middle Distillate Pump in Refinery W1 (given in Table 3-3) wasinfluenced by one large-leaking pump seal. There is no doubt that this pump seal was leakinggreater than 100,000 ppmv screening value. The screening team verified and re-verified thescreening value. Interestingly, the stream in which the large leak was discovered is one of theheavier hydrocarbon streams, a material that is dischargedat the bottom of the atmospheric still,called straight run esidue, with a 10% distillation temperature of 590F. However, this pumpwas handling this hydrocarbon at a temperatureof 617F, a suction pressure of 54 psi, and adischarge pressure of 80 psi. The seal failure was immediately repaired. Nevertheless, the pumps e d emission factor for the W ashington tests is high because the screening values were recordedprior to repair.

L----

------

100% 0 96.5e

01%n .35% 00.98% 00.00% 00.00% O 0.12%

=999 9,999 49,999 50,000

Scrcening Range (ppmv)

gWashIocal

Figure 3-3. Com parison of the Aggreg ate Leak Rate Distribution for Southern California andWashington Refineries

3-8



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Table 3-4 summarizes the new screening data by pipeline diameter categories,6 inches. No size dependency was noted.

> 6"Flange 156 0.0076 6.96E-05Valve 60 0.0025 4.93E-05

Total= 216 0.01 6.43-05.....................................................................................................................................................................No Size Given

Table 3-4. Emissions Factors by Component Size (Washington Refineries)Component Total Emissions Emission Factor< 2"

Size Component Count (Ibhr) (Ibhr/com p)Fittings 847 0.0145 1.91E-O5Flange 71 0.0005 2.94E-05OEL 7 0.0001 3.95E-05Other 68 0.0008 1.13E-04Valve 44 1 0.0091 2.96E-05

Total = 1,434 0.02 2.33-05I ..................................................................................................................................................................I 12" - 611

Fittings 15 0.0003 1.96E-05Flange 539 0.0047 3.01E-05Other 13 0.0001 8.82E-06Valve 2 84 0.0089 3.99E-05Iotal= 851 0.01 3.33-05

Otherh m PValve

103520.0001 8.82E-060.3746 1.07E-020.0000 2.73E-05

Total= 47 0.37 8.OE-03

Grand Total = 2,548 0.42Table 3-5 summarizes the new screening data by temperature ranges,


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~~~~~ ~

A P I P U B L U 3 3 7 b 0732290 0 5 5 8 2 3 3 8 0 2

0.008 2.OE-05................................................. ............... ........................... ...............otal= 389100 - 299 Fiaings 274 0.0046 1.67E-05Flange 174 0.0005 2.98E-06OEL 1 0.0000 4.4 1E-06Other 26 0.0002 8.82E-06W P 5 0.0003 5.53 E-04Valve 200 0.0039 1.93E-05Total= 680 0.009 1.4E-05...................................... ..........................................................................Fittings 233 0.0042 1.80E-05Flange 26 1 0.0020 7.57E-06OE L 6 0.000 1 2.10E-05Other 32 0.0005 1.42E-05Pump 12 0.0045 3.78E-04Valve 280 0.0087 3.1 1E-05Total= 824 0.020 2.43-05............................................................................................................................................................- 500 Fittings 151 0.0026 1.73E-05Flange 227 0.0093 4.1 1E-05Other 18 0.0002 8.82E-06Pump 13 0.3680 2.83E-02Valve 182 0.0056 3.06E-05

and less volatile. Therefore, the effects offset each other such that the leak rates are fairlyconstant.Table 3-5.Emissions Factors by Temperature (Washington Refineries)

Temperature Total Emissions Emission Factor@e! F) Component Count (Ibhr) (Ibh dco mp)< 100 FittingsFlangeOther

Valvef i m P

187 0.003 180 0.000612 0.00015 0.0018105 0.0020

1.66E-058.05E-068.82E-063.57E-041.89E-05

Total= 591Fittings 17Flange 24Other 3Valve 20

...................... , ..............................................................N'TemperatureGiven 0.386.....................................0.00030.00020.00000.0004

.........

........

........

6.5E-04............................1.65E-051.02E-058.82E-061.75E-05

Total = 64 0.001 1.4E-05Grand Total = 2,548 0.42

3-10



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A P I P U B L * 3 3 7 9 6 = 0 7 3 2 2 7 0 0 5 5 8 2 3 4 7 4 9Based on the statistical analysis in Appendix A, the best estimate of emission factors for theWashington refineries is obtained by averaging the individual measurements for each o f thecomponent types . These results are presented in Table 3-6.Table 3-6 . Aggregate Emissions Factors by Component Type (W ashington Refineries)r Washington Data 1980 DataI Total Emissions Emission Factor Emission FactorComponent Count (Ib/hr) (lb/hr/comp) (I b/hr/comp)Fittings 862 0.0148 1.71E-O5Flange 766 0.0127 1.66E-O5OEL 7 0.0001 1.86E-05Other 91 0.0010 1.07E-05h m P 35 0.3746 1.07E-02Valve 787 0.0205 2.60E-05

I To tal= 2,548 0.42

5.5 1E-O45.51E-04___-4.63E-025.07E-04

3-1 1



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A P I P U B L *3 3 7 9 6 W 0732290 0 5 5 B 2 1 5 6 8 5 W

Section 4CONCLUSION A ND RECOMMENDATION

Based on this programs source testing and data analysis, including the statistical analysis of thedata in Appen dix A, the primary conclusion is that emission factors for leaks from com ponen tsin HL service have been determined that are valid for any refinery in the U.S. Tw o sets offactors derived in Appendix A are presented in Table ES- 1 (Page ES-3), one set based on thedata available from SoCal refineries and the other based on a com bination of the SoCal andWashington data. The W ashington factors are lower than Southern Californias for allcomponen ts with the exception of pumps. The higher pump value in W ashington was influencedby one pum p leaking at a high rate. API recommends the use of the combined set. The newemission factors range from 14% to 35% of the current EPA emission factors (EPA 1993).The supporting conclusions reached as a result of the sta tistical analysis follow:. . . . . . .The SoC al data - distnbut on of e m o n ates from auarter to a uarter,Therefore, in aggregating screening data from different refineries, where individualrefine ries have different amounts of data (i.e., 2 quarters, 6 quarters and 7 quarters), theindividual-refinery data sets were reduced by averaging repeated measurem ents foreach component.

The analysis of the Washington State sc eenin? data indicates that t here is a si eni f c m.difference in emiss n fac o r m o n P commnent tvms (e.p . alve. p d owever,the d ifference in emission factor among the various HL stream types (Le., m ediummiddle distillate, heavy m iddle distillate, and heavy residual) are not statisticallysignificant.The emission factors for the SoCa1 refineries and the Washnpton r e f m s arestatistically similar. (For all components except pump seals the respective em issionfactors for the Washington refineries are slightly lower than those for the SoCalrefineries.) This serves to demonstrate that there should be no significant differences inHL emission factors between areas, with and without LDAR programs, which indicatesthat the HL emission factors generated by these screening data should be universallyapplicable to refineries in the U.S.

It was also concluded that for com ponents in HL service the screening values are independent ofstream temperature and hydrocarbon composition (as defined by the 10% Distillation

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A P I PUBL*33 96 W 0 7 3 2 2 9 0 0 5 5 8 2 3 6 511 =Temp erature, ASTM Method D86). At first this may seem odd. The heavy liquid streams rangefrom kerosene to asphalt. Intuitively, kerosene should leak more than asphalt. How ever, areasonable explanation of th is observed phenomenon is that the heavier hydrocarbons inherentlycirculate in higher temperature process streams. Therefore, for the medium-w eight middledistillate, the heavy-w eight middle distillate, and the heavy residual streams, it appears that theviscosity and vapor pressure (properties that affect leakage) have a similar effect on the screeningvalues.

4-2



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~ ~~~~

A P I Ph lBL*337 96 0732290 0 5 5 8 2 3 7 4 5 8

REFERENCE LIST

Radian Corporation, 1992. Development of Refinery Fugitive Emission Factors fo r VOCsan dToxics: Review Study Protocol with Phase I I Bagging & Speciation Results. Western StatesPetroleum Institute (W SPA) and American Petroleum Institute (MI).Glendale, C.A. &Washington, D.C.U.S. Environmental Protection Agency, 1995. Technology Transfer Network (TTN) CH IEFBulletin Board . Filename: Leaks-95.wps, (919) 541-5742.U.S. Environmental Protection Agency, 1993.Pro tocolfo r Equipment Leak Estimates, Contract68-dl-0117, for EPA by Radian Corporation, June 1993.

R- I



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~-A P I P U B L * 3 3 7 96 0732290 0 5 5 8 2 1 8 394

APPENDIX A

Statistical Analysis of Emission Factorsfo rLeaks in Refinery Com ponentsin Heavy L iquid Service

pyright American Petroleum Institutevided by IHS under license with API Licensee=Shell Global Solutions International B.V. Main/5924979112, User=Lavende


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A P I PUBLx337 96 = 0 7 3 2 2 9 0 0558239 220INTRODUCTION AND SUMM ARY OF RESULTS

This report complements Development of Emission Factors for Leaks in Refinery Com ponen tsin Heavy Liquid Service, a study conducted by H al Taback Company (HTC). APIs StatisticsDepartment ana lyzed existing screening data collected from Southern California and W ashingtonrefineries to p rovide statistical conclusions on several issues, including the following:

For Southe rn California refineries, are there quarterly changes in emission rates?For Washington State data, are there significant differences in emission rates forvarious components and different heavy liquid stream types (as categorized in HTCstudy)?Are emission factors for different refineries within Southern California andWashington States comparable?Are there significant differences in aggregate emission factors between SouthernCalifornia and W ashington States?Is there an appropriate procedure for com bining individual refineries data to obtainaggreg ate emission factors unaffected by the sizes of individual samples?Can confidence intervals be supplied for each of the emission factors computed?How do these emission factors compare to those currently recommended by E PA ?

The analysis described in this report provided the answers to the m ain questions listed abo veusing a variety of the advanced statistical modeling methods. It is necessary to em phasize thefollowing general conclusion s made in the course of the analysis:

The analysis of the data collected from Southern California Refineries C1 and C2 has shownthat there are no significant differences in the distribution of em ission rates by quarter forthose refineries (considering all seven quarters of data for Refinery C 1, and the last fourquarters for refinery C 2). Based on this conclusion, those two data sets were reduced byaveraging repeated measurem ents for each com ponent unit over qua rters prior to any furtheranalysis.The analysis of em ission rates for W ashington data has provided important information aboutfactors affecting emission rates. The effect of compon ent type has been found significant.The nature of the combined effect of heavy liquid stream type and stream temperature onemission rates has been analyzed, and emission factors for different stream types have beenproduced. Although we have found emission factors for Medium streams to be slightlyhigher than em ission factors for Heav yResidual streams, the data collected in the course of

A- I



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A P I PUBLX337 96 O 7 3 2 2 9 0 0 5 5 8 2 2 0 T 4 2

Component Type

FittingsFlangeOELOtherPumpValve

the H TC study do es not present enough evidence to consider this difference statisticallysignificant.

Em ission Factor Emission Factor Emission FactorSouthern California Combined South. California & 1980 EPAData Washington Data9.49E-05 7.93E-05 5.51E-049.66E-05 7.00E-05 5.5 1E-O4

1.86E-05 _-_1.14E-04 6.2 1E-O5 _-7.63E-03 8.25E-03 4.63E-022.13E-04 1.76E-04 5.07E-04

The analysis of the com bined Southern California and W ashington data has show n that thereare no significant differences in emission factors for these two states. This conclusionsupports the assumption of no differences in emission factors for states which are inattainment of the N ational Ambient Air Q uality Standards (represented by Washington data)and those that are non-attainment areas and have LDAR (Leak Detection and Repair)programs in effect (Southern Californiadata).Painvise com parisons of refineries within each of Southern California and Washington datasets indicate that em ission factors for W ashington State refineries are not significantlydifferent while all Southern California refineries show strong differences in average emissionrates. B ased on these results, a different method is suggested for com puting aggregateemission factors for Southern California and combined data sets referred to as a tw o-stepaveraging. Aggregate emission factors obtained by this method are shown in Table A-1.Notethat for both sets of emission factors presented in Table A-1, he values are significantlylower than HL emission factors currently recommended by EPA.The 95% confidence levels were com piled for each emission factor in order to facilitate thecomparison between these new HL emission factors and those in EPAs Protocol document.

A-2



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DATA DESCRIPTIONTo address the main points of interest, all the data sets used in the HTC study were obtained andused in the analysis. A brief description of those data sets is presented to highlight certaincharacteristics of the data.

SOUTHERN CALIFORNIA DATAAs described in HTCs report, the first two refineries HL screening data (designated refineriesC 1 and C2) were obtained by HTC directly from the refineries. Refinery C 1 supplied 165,852observations taken over the seven quarters during the period 1992 to 1994. Refinery C2 provided21,41O screening values collected over the six quarters during the same time period as RefineryC 1. The same se ts of components in each of the Refineries C1 an d C2 were screened in eachquarter, producing repeated measurements on the sam e units over the several quarters. Such aprocedure yielded data sets that could not be treated as a collection of independent observationsdue to the depen dencies between consecutive measurements on the same component units.Hen ce, further analysis was conducted to detect changes in em issions over time for R efineries C1and C2. Based on the results of this analysis, the data sets for both refineries were sum marizedfirst, and then em ission factors were com puted. The details and results of this ana lysis arepresented in a subsequent section of this Appendix, Analysis of Emissions by Quarterfo rSouthern California Data.

The second set of Southern California data collected by HTC was obtained from a prev ious studyand includes the da ta from two refineries, designated Refineries C3 and C4, taken over the twoquarters in 1 991. A total of 24,028 screening values w ere reported: 7,767 for Refinery C3, an d16,261 for Refinery C4.The data sets provided by HTC for each of the refineries were in sum marized form and assumedno repeated m easurements on the same component unit during the two consecutive quarters.

A-3pyright American Petroleum Institute

vided by IHS under license with API Licensee=Shell Global Solutions International B.V. Main/5924979112, User=LavendeNot for Resale, 03/30/2011 03:41:59 MDTreproduction or networking permitted without license fr om IHS

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A P I P U B L * 3 3 7 96 0732290 0 5 5 8 2 2 2 8 1 5 =

100,48223,370--12,077

78728,265

871

WASHINGTON DATAThe second set of data was measured by HTC at two refineries (designated Refineries W1 andW2) located in Washington State . The data set contained 2,548 screening values: 1,795observations from Refinery W 1, and 753 observations from Refinery W2. The screen ing testswere performed as described in Section 3 of the HTC report, each screening value representing asingle independent measurement on a component unit.

1.82E-055.01E-05_ _1.13E-045.54E-043.1 1E-051.13E-04

As explained in the H TC report, the purpose of this com ponent screening effort was mainly toveri@ that the Southern California data was representative of all refinery emission factorsapplicable to com ponents in HL service. On the other hand, advance planing provided anopportunity to collect more information about the components being screened. In addition tocomponent type and screening value, information extracted included type of heavy liquid stream,size of the component, and process parameters (temperature and pressure). This additionalinformation was used in the analysis to id ent ifj the main factors affecting emission levels. Theanalysis is discussed in the section of this A ppendix, AnaZysis of Emissions by Stream TypeforWashington D ata.All Southern California and W ashington data sets analyzed used emission rates computed fromscreening values provided by HTC (for details about conversion procedure, refer to Sectio n 2 ofthe HTC report). Based on these emission rates, emission factors (in lb/hr/component) wereobtained for each of the da ta sets by taking simple averages over ail available observ ations foreach component type (see Tables A-2 and A-3).Table A-2. Sou thern California Refineries - Simple Average Emission Factors

Component I Refinery C1 Refinery C2 Refinery C3 Refinery C4I I I IType

FittingsFlangeOELOtherPumpValvePRD

Count I EFI Ib/hr/comp

I

1.49E-035,468 1S5E-O4

8.7 1E-06

Ib/hr/comp Ib/hr/comp

-- _ _ __ --_ _ -- -- _ _58 3.86E-04 58 2.81E-02

2,669 3.08E-04 7,468 3.59E-0438 9.90E-05 425 1.09E-04

A-4pyright American Petroleum Instituteovided by IHS under license with API Licensee=Shell Global Solutions International B.V. Main/5924979112, User=Lavende


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Table A-3. Washington Refineries - Simple Average Emission FactorsI I Iomponent Refinery W 1 Refinery W 2 1Type Count EF, Ib/hr/comp Coun t EF, Ib/hr/compI I I

Analysis Of Emissions by Ouarte for Southern Ca ifornia DateThe data collected in Southern California from Refineries C1 and C2 deserved special attentionbecause they contained repeated measurements on the same component units over the period ofseveral consecutive quarters. Repeated-measures analysis of variance was used to test whetherthe differences in em ission rates between the quarters are statistically significant.A multivariatemodel used fo r both Refineries C1 and C2 was applied to the logarithmic transformation ofemission values (used to achieve normality and satis@ the usual linear model assum ptions).Figures A-1 and A-2 show multiple box plots constructed by quarter for Refineries C1 and C2respectively (in logarithmic scale). A v isual examination of F igure A-1 suggests that there are nostrong differences in average emissions by quarter for Refinery C1. This suggestion is supportedby the results of repeated-measures analysis: the test has shown that the differences betweenquarters are not statistically significant (p-value for null hypothesis of no quarter effect is 0.61 ).As Figure A-2 indicates, Refinery C2 does not show such a stable performance. The variabilityof emission rates and the average level of emissions are changing from quarter to qua rter. Theresults of repeated-m easures analysis indicate that the observed difference between quarters isstatistically significant (the null hypothesis of no quarter effect is rejected w ith p-value


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~ ~

A P I P U B L S 3 3 7 96 0732290 0 5 5 8 2 2 4 698

than the others). The differences in emissions during the last four quarters of the data collectionare not statistically significant (at significance level 0.05).

The results of repeated-measures analysis described above not only provide an insight onchanges in em issions over time for Refineries C1 and C2, but also suggest a way of dealing withrepeated quarterly measurements. Since significant differences in emissions by quarter inRefinery C1 data have not been found, the size of this data set can be significantly reducedwithout losing any important information by computing the average emission rate over allavailab le quarterly observa tions for each of the component units. This procedure will generateexactly one em ission rate for each unit screened. The size of Refinery C 1 data set is therebyreduced from 165,852 to 15,296 observations. Emission factors computed from this reduced dataset are shown in Table A-4. For each emission factor, the standard error and 95% confidenceinterval are computed to allow for comparison to em ission factors for original data given inTable A-2. Note that the differences between the two. sets of em ission factors are minor (o riginalemission factors are within confidence limits for em ission factors of the reduced data set).By applying the above procedure of averaging over quarters before com puting emission factors,two major improvements are achieved in the quality of the data set used for the analysis. First, acollection of independent observations is obtained, not a data set with unknown structure ofdependencies between quarterly observations on the sam e units. Second, the size of the reduceddata set is no w com parable to the size of some other data sets in consideration, which helps todiminish the dominating effect Refinery C1 would have if the original data sets were used tocom pute aggregate emission factors. Therefore, the reduced Refinery C 1 data are more suitablefor fu rther analysis of emission rates than the originaldata set.

A-6



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~ ~~ ~

A P I P U B L * 3 3 7 Yb W 0 7 3 2 2 9 0 0 5 5 8 2 2 5 5 2 4 W

1

Log (Emissions)

*c

**et

c

t

cc**

ctecctet

*ce

c

t

*

Oc

+ _ _ _ _ _ _93Ql +- - - -93Q3

Figure A-1. Distribution of Em issions by Quarter - Refinery C 1, Southern C alifornia Data

tt

t*t**

t *O O O

I T- - + - - + + - - + - - + + - - + - - +O

Figure A-2. Distribution of Em issions by Q uarter - Refinery C 2 S outhern California DataA-7



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~~-

A P I P U B L X 3 3 7 96 0732290 0558226 460

Table A-4. Emission Factors for Reduced Refinery C1 Data (total of 15,296 observations)

Table A-5 shows emission factors computed after Refinery C2 data is reduced by the sam emethod as described above. Although the overall significance of quarter effect on em ission ratesfor Refinery C2 has been established, the pairwise com parisons between the q uarters have shownthat only the first two quarters are significantly different from the others. Since em ission rates forthe last four quar ters did not differ significantly, it was decided that the averages over quarterscould still be taken for each of the screened component units without causing substan tial changesin em ission rates. The results in Table A-5 support this point: emission factors for reducedRefinery C2 data are very close to emission factors for original data set shown in T able A-2 (notethat all of the original emission factors arewithin the confidence intervals for emission factors inTable A-5). Hence, further analysis of Refinery C2 data was based on the reduced d ata set whichcontains only 4,274 observations (compared to 21,41O observations originally collected).Table A-5. Emission Factors for Reduced Refinery C2 D ata (total of 4,274 observations)*

Interval forEF

~~ ~~~ ~~

Confidence intervals are cut off at zero if the lower bound is negative.A-8



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~~

A P I P U B L m 3 3 7 9 b 0732290 0558227 3 T 7 W

Analysis of Stream TVDe Effect fo Washington DataThe data collected fi-om two refineries in Washington State were analyzed to assess the effects ofdifferent component and process parameters on emission rates (Southern Ca lifornia data did nothave eno ugh information to conduct this type of analysis). Although the influence of heavyliquid stream type o n emissions was the main interest, other parameters such as refineryidentification, comp onent type and size, stream temperature, and line pressure were also includedin the m odel. The same breakout into categories as in the HTC report was employed for streamtype, component size, and stream temperature in our analysis. The breakout for pressure wasderived in a simila r manner. Also, an artificial effect was introduced which indicates whether thecom ponent is actually leaking or not into our modeling process to improve the fit (non-leakerswere identified as components with the corresponding zero default emission rate). This effectwill be referred to as a le an on -l ea k category in the following discussion.The analysis consisted of several consecutive steps. First, the general linear model containing thefactors described above (with interactions) was fit to the data. Then, based on the results, aseparate analysis was conducted which was restricted to the subset of leakers that were found tobe a main source of variability in the data. This smaller data set (98 observations out of 2,548)was analyzed more closely to veri@ the conclusions suggested by the analysis of the completedata set. As an additional step, the complete data set was also analyzed as a contingency table tosupport to the conclusions drawn from the first two steps.Analysis of Com plete Washinpton Data SeA linear model with interactions was fit to the logarithmic transformation of the originalemission values (the data was transformed in order to satis@ the usual general linear modelassumptions). All the factors listed in the beginning of the section were originally included in themodel. A stepwise elimination of the insignificant terms yielded the following set of effectsincluded in the final model: component type and size, heavy liquid stream type, streamtemperature, le an o n -l eak category, and the first-order interactions between the lealnon-leakcategory an d the o ther factors in the model. All the main effects (component type and size,



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A P I P U B L * 3 3 7 96 0732290 0558228 233 m

stream type and temperature, and leaknon-leak category) were found to have a significant effecton em ission rates (p-value


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~~

A P I P U B L * 3 3 7 b = 0 7 3 2 2 9 0 0 5 5 8 2 2 9 L 7 TA linear model similar to the one used for the complete Washington data set was fit to the logtransformation of em ission rates for the subset of leakers. Here, again, the observation Pumpwith an unusually high emission rate was excluded when constructing the model. The modelaccounted for 54% of the variation in em ission rates (R2=0.54). he stepwise eliminationprocedure yielded the fo llowing set of significant factors for the subset of leakers: componenttype, temperature, and the first-order interaction term between stream type and tem perature (atsignificance level 0.05). No effects associated with com ponent size were found to be significant.Although the main effect associated with stream type was not statistically significant(p-value=0.20), this fact alone could not be considered as an ndicator of no overall stream typeeffect in the presence of the interaction. The significance of the interaction between stream typeand tem perature reflects the joint effect of those tw o factors which may be different for differentcomb inations of the factor levels.The nature of this combined effect is illustrated in Figure A-3. The solid line in Figure A -3show s the changes in emission rates for Medium stream s (emission rates are given as averagescomputed o ver the set of leakers in the corresponding temperature group regardless of theircomponent type); the dashed line provides the same type of information for Hea vymesidualstreams. Note that although some of the categories do not have emission rate estimates (due tounavailability of leakers in the corresponding subgroups), the general pattern is that thecombined effect of stream type and temperature is such that emission rates for Medium streamsrise with increasing temperature while emission rates for HeaydResidual streams are high at lowtemperature levels and low (with a small increase over temperature) at higher levels. The switchpoint on the temp erature scale is located somewhere between 100and 300 degrees Fahrenheit.

Visual exam ination of Figure A-3 clearly explains how emission rates are affected by ch anges intemperature and stream type, and why the m ain effect for stream type (averaged over allinteraction subgrou ps) is not significant: the interchange in stream-temperature effect addressed

A-1 1



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~~-A P I P U BL * 3 37 96 07321-190 0 5 5 8 2 3 0 9 9 1

1_I

o - 100 100 - 300 300 - 500 50 0 - ... NATemperature, O F

-e -Heavy/Residual Streams -Med ium Streams

Figure A-3. Illustration of the Com bined Effect of Stream Type and Temperature(Subset of Leakers, Washington Data). [Observation with unusually highemission rate (component Pump) is excluded.]in the above discussion causes the effects on average to cancel out, yielding an insignificant testresult. S imilar patterns in combined stream-temperature effect were also observed when em issionrates for the two stream types were plotted against temperature for each component typeseparately.Therefore, it may be concluded that, in general, emission rates are different for thetwo streamtypes being compared (Medium and HeavyResidual streams) when considering differenttemperatu re ranges separately. However, if emission factors are computed across all temperaturegroups available for any particular component type, the differences between the Medium andHea vyR esidual streams are not significant. Table A-6 shows emission factors for Washingtonrefineries computed for one complete data set (all available observations are included) and bystream type.

A-12



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A P I P U B L X 3 3 7 b 0732290 0 5 5 8 2 3 1 828 W

ComponentTable A-6. Em ission Factors for W ashington Data by S tream Type

Full Data Set Medium Streams Heavy/Residual Streams

h m P I 35 I 1.07E-02 I 1.01E-02 I 18 I 8.97E-04 I 4.49E-04 I 17 I 2.11E-02 I 2.07E-02Valve I 78 7 I 2.60E-05 I 2.31E-06 I 414 I 3.36E-05 I 4.35E-06 I 373 I 3.76E-05 I 2.49E-07Catego ica1 Analysis of Leaks for W ashington DataTo provide an alternative to the above analysis based o n the generalized linear modelingapproa ch, the Washington data were also analyzed from the categorical point of view. For eachof the six compo nent types, a 2x2 contingency table of counts by stream type (Medium orHe avyRe sidual) and by lealchon-leak category was constructed, and the tests for associationbetween stream type and leu no n -l ea k category were performed. (A leaker is any com ponentwith a screening value above the zero default value for that com ponent.) The results indicate thatthe chan ce of leak occurrence for different stream types depends on com ponent type.Specifically, for Fittings and Valves, the count of leakers is significantly higher fo r M ediumstreams than for H eavy Res idua l streams (at significance level 0.05), while for OEL, Other,Pumps and Flanges, the differences in the counts of leakers for Medium and H eavyR esidualstreams are not statistically significant. Since about two thirds of the total num ber ofobservations for W ashington data are either Fittings or Valves, it is not surprising that theanalysis of con tingency table constructed from the com plete data set by stream type andlealnon-leak category also yielded the test results indicating the significance of the generalassociation between stream type and leak occurrence after controlling for com ponent type. Inother words, even after the differences in component types are taken into account, the chancesofleak for M edium streams and H eavyResidual streams are still significantly different (p-value


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A P I PUBL*337 96 0732290 0558232 7b4

ion Factors for Southern California and Washineton DaQAs a final step in the analysis, the Southern California and W ashington data (reduced data setswere used for R efineries C1 and C2) were combined and the resulting complete data set wa sevaluated for differences in emission rates among refineries within each of the statesas well asfor differences in emissions between the states. To achieve this goal, the generalized linear modelfor log-transformed em ission rates was constructed with the individual refinery factor nestedwithin the state factor (several atypical observations with high emission rates were excludedfrom the modeling process). Both individual refinery and state factors were considered to berandom. T his means tha t available refineries within each of the states were viewed as typ icalrepresentatives of refinery population for that state (thus, Refinery C1 was considered to be arepresentative of the population of new er refineries with the full LDA R program while RefineriesC3 and C4 represented older refineries with a LDA R program in the beginning stage). Similarly,the test fo r difference s in emission rates between the stateswas perceived as a comparisonbetween the typical representatives of two populations: states where LDAR (Leak Detection andRepair) programs are required (southern California data), and states which are in attainmen t ofthe National Am bient Air Quality Standards and where LDAR programs are mostly not requiredwith a few ex ceptions (Washington data). This approach to model interpretation and test resultsprovided a more general understanding of the d ifferences in emission factors among therefineries and between the states.In addition to state and refinery w ithin-the-state factors, the model also included component typeand lealnon-leak category (described in the previous section) along with all the correspondingfirst-order interactions. The model accounted for 76% of the variation in emission rates for thecombined data set (R2=0.76).Under the assumption that state and refinery factors were random ,the tests showed that all the interaction terms in the model were significant (at significance level0.05). The main effects for refinery within the state factor and com ponent type facto r werealsofound to be significant. However, the model did not yield a significant difference in the m aineffects for both state factor and lealnon-leak category factor.This means that although we havea significant interaction between state and the other factors in the model, once we average



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emission rates across each state, the difference is no longer significant. Therefore, there is noneed to compute separate emission factors for the states with differentLDAR programrequirements - we may use either Southern California data alone or combined SouthernCalifornia and Washington data to compute aggregate emission factors.Since refinery w ithin-the-state factor was found to be significant in the model described above,several additional tests were performed to identiy which refineries within each of the states aresignificantly different from the others. The test results provided a basis for the final decision onhow to combine individual refinery data to obtain the m ost objective emission factor estimatesfor each of the states.Pairwise com parisons between refineries within each of the states showed that R efineries W 1 andW2 in W ashington State did not have a significant difference in emission rates (p-value=0.8).Thus, emission factors for this state could be computed directly from the comp lete data set.How ever, ail refineries in Southern California were found to be significantly different(p-value


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for the purpose of com parison. Note that new em ission factors obtained from Southern Californiadata are substantially lower than em ission factors currently recommended by E PA (none of theEPA 's emission factors fail within the confidence limits derived for the Southern Californiadata).

ComponentType

Fittings

Table A-7. Aggregate Emission Factors for Southern California Refineries*

Count Em ission Factor 95% Confidence Interval 1980 EPA Emissionlb/hr/compon ent for Em ission Factor, FactorIb/hr/comp lb/hr/component862 1.71E-O5 [1.66E-05, 1.76E-051 5.5 1E-O4

Component Count Emission FactorType Ib/hr/component

FlangeOEL

Fittings 25,161 9.49E-05Flange 3,141 9.66E-05

~~~766 1.7OE-O5 [2.67E -05, 3.13E-051 5.51E -047 1.86E-O5 [O.OO* 4.65E-051 --

1.14E-04Pump 7.63E-03

OtherPump

I Valve 13,869 2.13E-04

91 1.07E-05 [6.94E-06, 1.45E-051 -_35 1.07E-02 [O.OO* , .04E-021 4.63E-02

95% C onfidenceInterval for E missionFactor[5.23E-05, 1.38E-041[O.OO* ,2.09E-04]

[1.13E-04, 1.14E-041[O.OO* ,2.11E-02][1.24E-04,3.02E-04]

1980 EPAEm ission FactorIb/hr/component5.5 1E-045.5 1E-04

4.63E-025.07104

Confidence intervals are cut off at zero if the lower bound is negative.

Table A-8 provides emission factors for Washington State obtained by direct averaging over allavailable observations for each of the component types (all available observations are included).The 95% confidence inte rvals are computed in order to facilitate the comparison between thesenew em ission factors an hose in EPA's Protocol document.

I Valve 787 2.60E-05 [2.15E-05,3.05E-05] 5.07E-04 IConfidence intervals are cut off at zero if the lower bound is negative.



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Note that em ission factors for Washington data (TableA-8) are even lower than those forSouthern California data (Table A-7) for all of the component types except Pump (wh ich isinfluenced by one leaking component with a very high emission rate). Lower emission factors forWash ington refineries may be explained by the specifics of data collection and com biningprocess. First, note that Southern California data include the results of some older data collectionefforts (Refineries C3 and C4) characterized by higher average emission rates, while W ashingtondata were m easures only recently and have a low proportion of leaks compared to the older datasets. Second , the two-step procedure adopted for S outhern California data assigns equal weightsto all of the combined refineries, regardless of the age of an individual refineries data. Thisallows the data to be aggregated under different conditions on an equal basis but it may also yieldhigher em ission factors than straightforward averaging due to the influence of the older data sets.In other words, if the emission factors for Southern California are computed using sim pleaveraging by component type, the resulting estimates would be lower and more in agreem entwith emission factors for Washington data shown in Table A-8. However, EPA may prefer to useemission factors given in Table A-7 for S outhern California data because of the advantagesassociated with the two-step averaging procedure and also because they are more conservative.

Finally, an alternative set of emission factors is suggested based on combined S outhernCalifornia and W ashington data (the results of our analysis indicate that we can com bine thesedata sets because there is no significant difference between the states). Taking in considerationdifferent sizes of individual data sets, we have used the two-step averaging procedure to obtainemission factors for combined data as shown in Table A-9 (Washington refineries provided oneaggregate emission factor for each component type; all available observations were included).How ever, when applied to the combined data, the two-step averaging method assigns equalweigh ts to each of the individual emission factors; thus,Washington refineries have a stronginfluence on aggregate emission factors regardless of their relatively small data set size.Thiscauses a noticeable decrease in emission factors for som e components in Table A-9 whencompared to Southern California emission factors in Table A-7. The increase in em ission factor

api 337 emission factors for refinery equipment

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