The waste reduction (WAR) algorithm: environmental impacts, energy consumption, and engineering...

The waste reduction (WAR) algorithm: environmental impacts,energy consumption, and engineering economics

Douglas Young *, Richard Scharp, Heriberto Cabezas

United States Environmental Protection Agency, National Risk Management Research Laboratory, Sustainable Technology Division,

Systems Analysis Branch, Cincinnati, OH 45268, USA

Received 14 February 2000; accepted 9 March 2000

Abstract

A general theory known as the waste reduction (WAR) algorithm has been developed to describe the ¯ow and the generation of

potential environmental impact through a chemical process. The theory de®nes indexes that characterize the generation and theoutput of potential environmental impact from a process. The existing theory has been extended to include the potential environ-mental impact of the energy consumed in a chemical process. Energy will have both an environmental impact as well as an eco-nomic impact on process design and analysis. Including energy into the analysis of environmental impact is done by re-writing the

system boundaries to include the power plant which supplies the energy being consumed by the process and incorporating theenvironmental e�ects of the power plant into the analysis. The e�ect of this addition on the original potential impact indexes will bediscussed. An extensive engineering economic evaluation has been included in the process analysis which inherently contains the

cost of the consumed energy as an operating cost. A case study is presented which includes a base process design and two mod-i®cations to the base design. Each design is analyzed from an economic perspective and an environmental impact perspective. Theenvironmental impact analysis is partitioned into the impacts of the non-product streams and the impacts of the energy generation/

consumption process. The comparisons of these analysis procedures illustrate the consequences for decision making in the design ofenvironmentally friendly processes. Published by Elsevier Science Ltd.

1. Introduction

Incorporating pollution prevention techniques intochemical process design has received more attention inrecent years. This concept entails taking into considera-tion the environmental concerns, beyond the demandsof regulation, of the chemical process in the designstage. Historically, the economics, operating and capi-tal, of the chemical process were the predominant issuesin the design stage [1±3] with little regard towardsenvironmental concerns or pollution prevention.The concept of pollution prevention was ®rst presented

in the 1970s via heat exchange networks (HENs). Thesewere employed to help reduce the energy consumption ofmanufacturing processes. Shenoy [4] and Gundersen andNaess [5] provide reviews on the research that has sprou-

ted within this area. Heat exchange networks led toinnovation known as mass exchange networks (MENs)which were ®rst introduced by El-Halwagi and Manou-siouthakis [6]. The idea behind MENs is to concentratepollutants in selected waste streams while purifyingother streams. This results in reducing the volume ofwaste generated within a process that would requirefurther treatment. Both of these techniques focus onreduction to accomplish pollution prevention. Reduc-tions in energy usage and waste treatment generallyimprove the economics of the process as well.Whereas reduction in energy usage has obvious con-

sequences, less pollution and lower operating costs,reduction in waste does not necessarily have the sameobvious result. It is quite conceivable that one design fora process has less waste than another design, but thatwaste could have a greater impact on the environmentthan the waste from the latter design. Evaluating theenvironmental impact of wastes from a chemical processcan be accomplished by the waste reduction (WAR)algorithm. The WAR Algorithm was ®rst developed by

0956-053X/00/$ - see front matter Published by Elsevier Science Ltd.

PI I : S0956-053X(00 )00047-7

Waste Management 20 (2000) 605±615

www.elsevier.nl/locate/wasman

* Corresponding author. Tel.: +1-513-569-7624; fax: +1-513-569-

7111.

E-mail address: [email protected] (D. Young).

Hilaly and Sikdar [7]. The original version of the WARalgorithm introduced the concept of a pollution balancewhich was strictly mass based. Cabezas et al. [8] intro-duced the generalized WAR algorithm with a potentialenvironmental impact (PEI) balance, which assignedenvironmental impact values to di�erent pollutants, asan improvement upon the original WAR algorithm.Young and Cabezas [9] extended the PEI balance toincorporate the consumption of energy by the chemicalprocess into the environmental evaluation. From thePEI balance, PEI indexes are calculated which provide arelative indication of the environmental friendliness orunfriendliness of the chemical process.Other researchers have devised di�erent methodolo-

gies for incorporating environmental considerationsinto chemical process design. These methodologies arereviewed extensively by Cano-Ruiz and McRae [10].Most commonly, environmental concerns are treated asconstraints in an economic optimization problem wherethe constraints are designated by regulations. Minimiz-ing the amount of waste or pollutants generated withina process is another common method to incorporateenvironmental considerations into process design [10].A number of index type methods have been imple-mented to evaluate the environmental impact of theemissions of chemical processes: Houghton et al. [11]proposed an index for global warming de®ned as theemissions rate multiplied by the global warming poten-tial of that chemical relative to CO2; Grossman et al.[12] proposed a toxicity index by multiplying the e�uent¯ow rate of a chemical by the inverse of its LD50 value;

Fathi-Afshar and Yang [13] proposed an index for gas-eous emissions by dividing the e�uent ¯ow rates of thechemicals by their threshold limit values as de®ned bythe ACGIH and then multiplied by their speci®c vaporpressures; Heinzle et al. [14] andKoller et al. [15] proposedecological indices based on a classi®cation approach toassess the environmental impact of a process. Pistiko-poulos et al. [16] proposed relative environmentalimpact indexes for multiple categories, i.e. air pollution,water pollution, global warming, ozone depletion, pho-tochemical oxidation, and solid wastes, and optimizedthe process for each impact category. The PEC/PNEC(predicted environmental concentation/predicted noe�ect concentration) ratio has also been used to evalu-ate the environmental impact of a process design [10].King et al. [17] used case base reasoning to evaluate theenvironmental impact of a process design which relieson past experience.This paper presents an illustrative case study that

demonstrates the use of the WAR algorithm in con-junction with a detailed economic evaluation to design achemical process that is environmentally friendly andeconomically bene®cial. The case study will present threedesigns: an original design and two modi®ed designs.ASPEN PLUS 10.1 [18] process simulator was used tosimulate the process designs. ICARUS Process Evaluator(IPE) [19] was used to evaluate the capital and operatingcosts of the process designs. [Use of ASPEN PLUS 10.1and ICARUS Process Evaluator in this research does notimply United States Environmental Protection Agency(USEPA) endorsement of these products.]

Nomenclature

PEI potential environmental impactIt the total PEI in the overall system (PEI)I:in�cp� the PEI entering the chemical process (PEI/

h)I:out�cp� the PEI leaving the chemical process (PEI/

h)I:in�ep� the PEI entering the energy generation

facility (PEI/h)I:out�ep� the PEI leaving the energy generation facil-

ity (PEI/h)I:we�cp� the PEI emitted from a chemical process

associated with the waste energy (PEI/h)I:we�ep� the PEI emitted from a energy generation

facility associated with the waste energy(PEI/h)

I:gen�t� the total PEI generated within the system

(PEI/h)I:in�t� the total PEI entering the system (PEI/h)

I:out�t� the total PEI leaving the system (PEI/h)

I:i;in�t� the total PEI entering the system associated

with impact category i (PEI/h)I:i;out�t� the total PEI leaving the system associated

with impact category i (PEI/h)Iout�t� the total PEI leaving the system normalized

to the production rate (PEI/kg products)Igen�t� the total PEI generated within the system

normalized to the production rate (PEI/kgproducts)

M:j;in the mass ¯ow rate of stream j into the sys-

tem (kg/hr)M:j;out the mass ¯ow rate of stream j out of the

system (kg/h)P:

p the mass ¯ow rate of product stream p (kg/h)

xkj the mass fraction of chemical k in stream j(dimensionless)

�i the weighting factor associated with impactcategory i (dimensionless)

ski the speci®c PEI of chemical k associated

with impact category i (PEI/kg chemical k)

606 D. Young et al. /Waste Management 20 (2000) 605±615

2. Theory

The WAR algorithm is a methodology used to evalu-ate the relative environmental impact of a chemicalprocess. It is not a life cycle analysis (LCA) tool. It onlyconsiders the manufacturing aspect of the product's lifecycle; it does not consider the other life cycle stages: rawmaterial acquisition, product distribution, product use,product disposal, and product recycle. This methodol-ogy can be used in either the design stage of a futureprocess or in the retro®tting of a current process.A detailed discussion of the WAR theory is presented

by Young and Cabezas [9]. For sake of clarity, a sum-mary of the theory will be presented here.

2.1. Potential environmental impact theory

The potential environmental impact of a given quan-tity of material and energy can be generally de®ned asthe e�ect that this material and energy would have onthe environment if they were to be emitted into theenvironment. This implies that a release has not occur-red, but if it did, it would have a quanti®able e�ect, thepotential environmental impact. This quantity is prob-abilistic in nature and will in reality deviate from theobserved values. Potential environmental impact is aconceptual quantity that cannot be measured directly.However, it can be estimated from measurable orestimable quantities [9].Cabezas et al. [8,20] have proposed that a potential

environmental impact balance is required to incorporateenvironmental e�ects into process design. In theirresearch, they included only the environmental e�ects ofthe materials entering and leaving a manufacturingprocess into their potential environmental impact (PEI)balance. In these papers, the system boundaries for thePEI balance were drawn around the manufacturingprocess. Young and Cabezas [9] improved on that bal-ance by incorporating the consumption of energy intothe PEI balance as depicted in Fig. 1. They were able todo this by drawing the system boundary around boththe manufacturing process and the energy generationfacility (electric power generating utility). The potentialenvironmental impact balance including energy isdescribed by the expression,

@It

@t� I:in�cp��I:in�ep�ÿI:out

�cp�ÿI:out�ep�ÿI:we

�cp�ÿI:we�ep��I:gen

�t� �1�

where It is the amount of potential environmentalimpact inside the system (chemical process plus energygeneration process), I

:in�cp� and I

:out�cp� are the input and

output rates of potential environmental impact to thechemical process, I

:in�ep� and I

:out�ep� are the input and

output rates of potential environmental impact to theenergy generation process, I

:we�cp� and I

:we�ep� are the out-

puts of potential environmental impact associated withwaste energy (denoted by the subscript we) lost from thechemical process and the energy generation process, andwhere I

:�t�gen is the rate of generation of potential envir-

onmental impact inside the system. I:�t�gen represents the

creation and consumption of potential environmentalimpact by chemical reactions inside the chemical pro-cess and the power plant.For steady state processes, Eq. (1) reduces to

0 � I:in�cp� � I

:in�ep�ÿI:out

�cp�ÿI:out�ep�ÿI:we

�cp�ÿI:we�ep� � I

:�t�gen �2�

The waste energy emissions of both the chemical pro-cess and the energy generation process will be neglectedsince these will have minor impacts when compared tothe amount of energy and materials that are consumedand produced through non-fugitive streams. Also, theimpact of the input streams (coal, air, and water) to theenergy process will be neglected for reasons discussed byYoung and Cabezas [9]. To summarize the explanation,the coal stream which contains a multitude of organicand metallic compounds is considered to have a negli-gible impact since these components are stabilized in asolid matrix so that they are not normally bioavailableto humans and animals.This reduces Eq. (2) to

0 � I:in�cp�ÿI:out

�cp�ÿI:out�ep� � I

:�t�gen �3�

This can be re-written yet again as

0 � I:�t�inÿI

:�t�out � I

:�t�gen �4�

where I:�t�in is de®ned as the total potential environmental

impact that resides in the mass inputs to the manu-facturing/energy generation process (see Fig. 1) and isapproximated solely by the impacts from the inputs tothe chemical manufacturing process, I

:in�cp�. I

:�t�out is

de®ned as the total potential environmental impact thatresides in the mass outputs from the manufacturing/

Fig. 1. The incorporation of energy into the potential environmental

impact calculations or the WAR algorithm.

D. Young et al. /Waste Management 20 (2000) 605±615 607

energy generation processes and is approximated solelyby the impacts from outputs from both the chemicalmanufacturing process, I

:out�cp�, and the energy genera-

tion facility, I:out�ep�.

2.2. Potential environmental impact indexes

The input potential environmental impact index, I:�t�in ,

can be approximated by known and measurable quan-tities by

I:�t�in �

XEnvCat

i

�iI:�t�i;in �

XEnvCat

i

�iXStreams

j

M:

j;in

XComps

k

xkj ski �5�

where �i is the weighting factor associated potentialenvironmental impact category i, I

:�t�i;in is the PEI input

index for category i, M:j;in is the mass ¯ow rate of input

stream j, xkj is the mass fraction of component k instream j, and s

ki is the speci®c potential environmentalimpact of component k associated with environmentalimpact category i.The output potential environmental impact index,

I:�t�out, can be approximated in a similar fashion.

I:�t�out �

XEnvCat

i

�iI:�t�i;out

�XEnvCat

i

�iXNPStreams

j

M:j;out

XComps

k

xkj ski �6�

where I:�t�i;out is the PEI output index for category i and

M:j;out is the mass ¯ow rate of the non-product output

stream j. In the calculation of I:�t�out only the non-product

streams (NP Streams) are taken into consideration. Thisinsures that the user or producer is not directly penalizedfor producing a chemical that has a high PEI value. Theuser or producer will be penalized though if this highPEI valued chemical is not completely recovered in oneof the products and/or in the by-products of this processalso have a high PEI value. The PEI generation index,I:�t�gen, can then be calculated from Eqs. (4)±(6).Two types of environmental impact indexes are used

to evaluate the environmental friendliness of a chemicalprocess: PEI output indexes and PEI generationindexes. The output indexes can be evaluated on a ratebasis, PEI/h, or on a production basis, PEI/kg of pro-duct. The indexes presented so far are in terms of rateevaluations, PEI/h. To evaluate the PEI output index ona production basis, a simple transformation can be made

I�t�out �

I:�t�outPProdStreams

pP:

p

�7�

where I�t�out is the PEI output index with units of PEI/kgof product and P

:p is the mass ¯ow rate of the product

streams.A similar transformation can be made to convert the

PEI generation index into terms of PEI/kg of product

I�t�gen �I:�t�genPProdStreams

pP:

p

�8�

where I�t�gen is the PEI generation index with units of PEI/kg of product.The intended use of the WAR algorithm is to provide

a means for comparing the potential environmentalimpact of alternative designs for a process. In the WAR

algorithm, the four indexes shown in Eqs. (7) and (8),

I:�t�out; I

�t�out; I

:�t�gen; and I�t�gen, are used to compare the envir-

onmental friendliness of the possible process designs.Designs with lower PEI index values represent moreenvironmentally desirable designs.

2.3. Speci®c chemical environmental impacts

The WAR algorithm uses eight environmental impactcategories in its evaluation. Those categories are

1. Human toxicity potential by ingestion (HTPI),2. Human toxicity potential by exposure both dermal

and inhalation (HTPE),3. Terrestrial toxicity potential (TTP),4. Aquatic toxicity potential (ATP),5. Global warming potential (GWP),6. Ozone depletion potential (ODP),7. Photochemical oxidation potential (PCOP), and8. Acidi®cation potential (AP).

The PEI evaluation of these categories is discussed indetail by Young and Cabezas [9].

2.4. Weighting factors

Weighting factors are used to combine PEI categoriesinto a single PEI index. They represent the relative orsite-speci®c concerns of the user. For instance, if theuser was evaluating a process that was located in theLos Angeles area the weighting factor for smog forma-tion (PCOP) would probably receive a high value.Whereas, if the user was evaluating a process in theNortheast the weighting factor for acid rain (AP) wouldprobably receive a high value. This paper discusses anillustrative case study with no speci®c site in mind. Forthis reason, the weighting factors for all categories inthis case study will be assigned equivalent values ofunity.


2.5. Economic evaluation

The economic portion of this investigation wasaccomplished with the IPE [19]. IPE is a state-of-the-artsoftware tool that allows the user to size and cost acomplete chemical manufacturing process. IPE usesstandard design codes to size the processing equipment,such as ASME, API, ANSI, TEMA, NEMA, BS5500,JIS, DIN, etc. IPE evaluates the capital costs of a pro-cess by using expert system databases; it does not usethe factor method. IPE will also evaluate the operatingcosts associated with a chemical process. Again, thesecosts are estimated through expert system databases.

3. Case study

The case study that will be investigated in this paper isan allyl chloride production facility [3]. The process ¯owdiagram (PFD) is shown in Fig. 2. A natural gas furnaceis used to heat the propylene feed stream which is mixedwith the chlorine feed stream and sent to a ¯uidized-bedreactor. The reactor operates at 511�C and about 3 bar.In the reactor, allyl chloride and hydrogen chloride arethe primary products [Eq. (9)]; however, 2-chlor-opropene [Eq. (10)] and 2,3-dichloropropene [Eq. (11)]are signi®cant by-products of the reaction. A series of

heat exchangers are employed to reduce the temperatureof the reactor e�uent to approximately ÿ50�C. The¯ash drum is used to remove approximately 80% of theHCl. The hydrogen chloride is absorbed by de-ionizedwater to produce 31.5% HCl. The vapor stream leavingthe absorber is primarily propylene which is compressedand heated before recycling. The liquid from the ¯ashdrum is sent through a series of distillation towers toseparate allyl chloride from the other chlorinated by-products. The ®rst two towers are used to recover anyHCl and propylene that was present in the liquid e�u-ent of the ¯ash drum. The primary product streams ofthis process are the allyl chloride stream and the 31.5%HCl product stream. The 2-chloropropene and the 2,3-dichloropropene by-product streams can either be sold,if the purity is >95 wt.%, or be treated as waste.

C3H6 � Cl2 ! CH2 � CHCH2Cl�HCl �9�

C3H6 � Cl2 ! CH2 � CClCH3 �HCl �10�

C3H6 � 2Cl2 ! CH2 � CClCH2Cl� 2HCl �11�

This process requires refrigeration. The third heatexchanger after the reactor and the condensers of the®rst three distillation towers all require e�uent stream

Fig. 2. The process ¯ow diagram of the allyl chloride production facility.


temperatures less than 0�C. Due to the amount ofrefrigeration required, this process is extremely energyintensive. Propylene is used as the refrigerant in thiscase study. The PFD for the refrigeration portion of theprocess is not shown, but is considered in the environ-mental and economic analysis.In this case study, three process designs are con-

sidered: the base design as shown in Fig. 2 (unit 600)and two alternative designs (units 601 and 602). The®rst alternative process design (unit 601) addresses theenergy consumption issue. For this design option, theoperating parameters have been adjusted to reduce theamount of refrigeration required for this process. Thiswas accomplished by increasing the operating pressure inthe distillation towers (see Table 1 for the pertinent oper-ating parameters of all three processes). By increasing thepressure in the system, the ¯ash drum, one heat exchanger(the third one after the reactor), and the propylene dis-tillation tower could be removed from the PFD. Onecompressor was added to increase the pressure of thereactor e�uents; this was added between the ®rst andsecond heat exchanger after the reactor.The second alternate process design (unit 602) inves-

tigates improving the yield of allyl chloride in the reac-tor while including the improvements used in unit 601.The kinetics of this system are such that the yield of allylchloride can be increased by lowering the reactor tem-perature. For this illustrative case study, a reactor tem-perature of 470�C was chosen. No other modi®cationswere made to unit 602.All three process design options were designed to

maintain the process speci®cations: (1) the allyl chlorideproduct stream must have a minimum purity of 99.9mol%, (2) the HCl product stream must be 31.5 wt%,(3) the 2-chloropropene byproduct stream must have aminimum purity of 95 mol%, and (4) the 2,3-dichlor-opropene byproduct stream must have a minimum pur-ity of 95 mol%. The revenue/expense for each of thefeed and product streams is listed in Table 2.For the WAR algorithm evaluation, the energy con-

sumed by the process was calculated by considering

both direct energy and indirect energy requirements.Direct energy is the energy required to operate pumpsand compressors. Indirect energy results from the use ofutility ¯uids (cooling water, low pressure steam, boilerfeed water, and natural gas). Utility ¯uids are broughtinto the process at non-standard conditions (25�C and 1bar). Indirect energy describes the energy that isrequired to transfer the ¯uid from standard conditionsto the conditions used in the process. In this illustrativecase study, heat integration has not been considered.Again, indirect energy calculations are used only for theenvironmental evaluations (WAR algorithm).

4. Results and discussion

A combined environmental and economic evaluationof the allyl chloride production process has been per-formed for the base design (unit 600) and two alter-native designs (units 601 and 602). The environmentalanalysis was completed using the WAR Algorithm, andthe economic analysis was completed using the com-mercial software ICARUS Process Evaluator.

Table 1

Pertinent operating parameters for the three possible designs of the allyl chloride production case study: the base design (unit 600), two alternative

designs (units 601 and 602)

Pertinent operating parameters Unit 600 Unit 601 Unit 602

Reactor temperature (�C) 511 511 470

Pressure in HCl tower (bar) 1.4±1.5 7.5±7.6 7.5±7.6

Pressure in 2-chloropropene tower (bar) 1.4±1.5 6.0±6.1 6.0±6.1

Molar re¯ux ratio in HCl tower 0.0667 0.458 0.478

Molar re¯ux ratio in 2-chloropropene tower 56.6 81.4 84.0

Direct energy consumed in refrigeration process (MW) 1.97 0.411 0.412

Direct energy consumed in the total process (MW) 2.14 0.698 0.696

Flow rate of allyl chloride product stream (kg/h) 1190 1190 1300

Flow rate of 2-chloropropene by-product stream (kg/h) 35.3 34.5 36.0

Flow rate of 2,3-dichloropropene by-product stream (kg/h) 203 206 121

Table 2

Revenue and expenses of the feed and product streams of the allyl

chloride production case study

Stream ($/kg)

Revenue

Allyl chloride product stream 1.72a

31.5 wt.% HCl product stream 0.08a

2-Chloropropene by-product stream 0.10b

2,3-Dichloropropene by-product stream 0.15b

Expense

Chlorine feed stream 0.28a

Propylene feed stream 0.31a

De-ionized water feed stream 0.001b

HCl waste stream 0.40b

a Values obtained from the 21 January 2000 edition of Chemical

Market Reporter.b Values obtained from Turton et al. [3].


The potential environmental impact (PEI) of each ofthe chemicals involved in the process is shown in Table3 with each of the impact categories listed. In this casestudy, none of the chemicals used contribute sig-ni®cantly to ozone depletion (ODP) so that category hasbeen omitted from this table. The PEI values for each ofthe chemicals is given in terms of PEI/kg; the PEI valuesfor the energy consumption is given in terms of PEI/MWh. Thus, the values are not directly comparable.The PEI values are presented to two signi®cant ®gures;this is the maximum amount of signi®cant ®gures thatshould be used in the WAR analysis. Due to inherentuncertainties in the environmental data, only one or twosigni®cant ®gures should be used in this analysis. FromTable 3, it can be seen that chlorine is the chemical thatwill have the greatest environmental impact. In all threedesigns chlorine is the limiting reagent and is consumedalmost entirely in the reactor.Now, let us examine the base design (unit 600). The

results of the economic evaluation for this design areshown in Table 4. This design generates $18 million/year of revenue at an operating cost of $9.9 million/year; the total operating pro®t of this design is $8.1million/year. The grass roots design of the process isestimated to have a capital cost of $23.5 million.The purpose of the WAR algorithm is to improve the

environmental performance of the process while main-taining the economic performance. Thus, any suggesteddesign improvements made by using the WAR algo-rithm must maintain, if not improve upon, the eco-nomics of the original or base design. Consider these tobe constraints of using the WAR algorithm.The WAR algorithm is used to evaluate the environ-

mental friendliness of the base design (unit 600). ThePEI output index for each of the categories, I

:�t�i;out, [from

Eq. (6)] for the base design is shown in Fig. 3. From this®gure, it is quite apparent that the major concerns ofthis design are human toxicity, terrestrial toxicity, andacid rain formation. The other impact categories pro-vide negligible impact compared to these three cate-gories. Remember, these are the result of our uniformweighting. If a di�erent set of weighting factors had

been chosen, these results could have possibly been dif-ferent. Also, since the feed streams for all three designsare very similar only the output indexes will be used inthe environmental evaluation of this case study. If thePEI input index, I

:�t�in , is consistent among all possible

designs, then by Eq. (4) I:�t�out and I

:�t�gen will reveal the same

information.From Table 3, it can be seen that allyl chloride, 2-

chloropropene, 2,3-dichloropropene, and hydrogenchloride are the signi®cant contributors to both HTPIand TTP. They all have approximately equal impact forthose categories. The power consumption also plays asigni®cant role in those categories. For acidi®cationpotential (AP), the power consumption is the most sig-ni®cant contributor and hydrogen chloride has only asecondary in¯uence on that category. Overall, the APgenerated by the consumption of electricity is the pri-mary concern of the base design from an environmentalperspective. As an aside, the chlorine has a very highPEI value for ATP, but it is never realized since chlorineis almost completely consumed within the reactor andonly minute amounts are seen in the e�uent streams.The power consumption of the base design is an

obvious area for improvement since it is a signi®cantconcern in both the environmental (Fig. 3) and the eco-nomic analyses (Table 4). The base design requires sig-ni®cant refrigeration to accomplish the separations in

Table 3

The potential environmental impact values for each category for the chemicals used in this case study. Values are in PEI/kg unless otherwise stateda

Compound HTPI HTPE ATP TTP GWP PCOP AP Total

Allyl chloride 0.51 5.4e-4 0.10 0.51 0 0 0 1.12

2-Chloropropene 0.61 0 0.0059 0.61 0 0 0 1.23

2,3-Dichloropropene 1.1 0 0.014 1.1 0 0 0 2.21

Hydrogen chloride 0.78 2.3e-4 4.6e-4 0.78 0 0 0.86 2.42

Propylene 0 0 3.1e-2 0 0 2.1 0 2.13

Chlorine 0 5.4e-4 22 0 0 0 0 22

Water 0 0 0 0 0 0 0 0

Power consumption (PEI/MWh) 0.25 0.0053 1.2 0.25 0.67 0 22 24

a The total was calculated using weighting factors of unity for all categories.

Table 4

The results of the economic analysis of the allyl chloride production case

study. Bold values represent sums of non-bold values (directly above)

(Values in million $/year) Unit 600 Unit 601 Unit 602

Revenue from allyl chloride stream 16.3 16.3 17.9

Revenue from other streams 1.7 1.7 1.6

Total operating revenue 18.0 18.0 19.5

Cost of feed stocks 5.0 5.0 5.1

Operating costs 4.8 4.2 4.3

Cost of waste treatment 0.1 0.1 0.1

Total operating expense 9.9 9.3 9.5

Net operating pro®t 8.1 8.7 10.0

Total capital costs 23.5 20.0 19.9


this process, and typically, refrigeration units requirelarge amounts of energy to operate. To reduce thisrefrigeration requirement, the pressure in the separationunits was increased. This was the primary improvementmade for the ®rst alternative process design (unit 601).To change the pressure in the system, some modi®ca-

tions had to be made to the overall process design. First,a compressor was added between the ®rst and secondheat exchanger in Fig. 2 to accommodate the increase insystem pressure. Next, the third heat exchanger, whichused refrigeration, was removed. The ¯ash drum wasalso removed; the preliminary separation achieved inthe ¯ash drum was combined into the HCl tower byincreasing the re¯ux in the tower's condenser. Theoperating pressure in the HCl tower was increased from1.4±1.5 bar to 7.5±7.6 bar. This allowed for the separa-tion of the HCl and the propylene to occur in the samevessel. The HCl tower still required refrigeration in thecondenser, but its duty had been reduced. The propy-lene tower was removed, since the propylene was beingseparated in the HCl tower. The pressure in the 2-Chloropropene Tower was increased from 1.4±1.5 barin Unit 600 to 6.0±6.1 bar in unit 601. To meet the samepurity speci®cations, the re¯ux ratio in this tower wasincreased by 44%. The allyl chloride tower was oper-ated under the same conditions as the base design.This process design (unit 601) only had one unit

which required refrigeration as opposed to four units inthe base design (unit 600). This signi®cantly reduced the

energy consumption of the process as seen in Table 1.unit 601 requires 67% less energy than Unit 600. Thishad signi®cant impacts on both the economic and theenvironmental evaluations.The reduction in energy consumption decreased the

annual operating costs by a modest $600,000. Thisreduction is almost entirely ($500,000) due to the amountof electricity required to operate the compressors in therefrigeration cycle. The remaining reductions in operat-ing costs are due to reductions in the amount of utility¯uids used and reductions in labor force due to fewerunit operations to monitor. Since nothing else was doneto this process design to change production character-istics, this $600,000 reduction in operating costs repre-sents the realized increase in operating pro®ts which isonly a 7.4% improvement.On the other hand, the process modi®cations had a

much more signi®cant e�ect on the capital cost of thedesign (Table 4). The simple process changes reducedcapital costs by $3.5 million or 15% of the base design.This reduction was a direct result of removing equip-ment from the design as well as decreasing the size ofexisting equipment. For instance, the compressors usedin the refrigeration cycle were considerably smaller inunit 601 as compared to unit 600.Figs. 3 and 4 display the environmental comparison

between the base design, unit 600, and the ®rst alternativedesign, unit 601. The I

:�t�out decreased from 700 PEI/h in

unit 600 to 620 PEI/hr in unit 601. This is an 11%

Fig. 3. The total potential environmental impact output index for each of the impact categories, I:�t�i;out, for the three process designs: the base design

(unit 600), and the two alternative designs (units 601 and 602). HTPE, human toxicity potential by exposure dermal and inhalation, PCOP, photo-

chemical oxidation potential, GWP, global warming potential, ATP, aquatic toxicity potential, AP, acidi®cation potential, HTPI, human toxicity

potential by ingestion, TTP, terrestrial toxicity potential.


improvement from an environmental perspective. Fig. 4distinguishes the potential environmental impact asso-ciated with the chemical process and the potential envir-onmental impact associated with the energy generationprocess. From this ®gure, it is quite apparent that allenvironmental advantages are due simply to reduction inenergy consumption by the chemical process. Fig. 3, breaksdown the PEI into the various impact categories. From this®gure, the environmental improvement is seen almostentirely in a reduction of the acidi®cation potential. Thismakes sense since the primary PEI concern of the powergeneration facility is the acidi®cation potential. Againthese results are directly related to the weighting factorscheme that was employed in this illustrative case study.The reduction in the PEI generated by the energy

facility is a noticeable improvement in the process; how-ever, as Fig. 4 clearly shows, to make a substantialimprovement (reduction) in the environmental perfor-mance of the overall process the PEI in the process e�u-ent streams needs to be reduced. As Table 3 shows, allylchloride, 2-chloropropene, 2,3-dichloropropene, hydro-gen chloride, and propylene all have similar PEI values.Thus, to reduce the PEI generated within the processitself those compounds have to be reduced in the by-product and waste e�uent streams.Unit 602 attempts to address the reduction of the by-

product streams by changing the reactor's operatingconditions. The kinetics of the reaction system are suchthat lower reactor temperatures favor a greater selec-tively towards allyl chloride. This was the primary

modi®cation in the second alternative process design,unit 602, from unit 601. Unit 602 used the modi®cationsthat were implemented in unit 601 as a basis.For the purposes of this illustrative case study, the

reactor temperature in unit 602 was decreased to 470�C.This increased the production rate of allyl chloride, by9.2%, and reduced the amount of the by-products, 2-chloropropene and 2,3-dichloropropene. The rate ofhydrogen chloride production remained constant sinceits production is directly related to amount of chlorineconsumed in the reactor which in all design options wasconsistent. A secondary result of this change in reactorconditions is that there was a greater conversion ofpropylene observed. This is due to the fact that more ofthe chlorine was used to make allyl chloride, whichcontains only one chlorine atom, and less of the chlorinewas used to make 2,3-dichloropropene, which containstwo chlorine atoms. The direct energy consumed by theprocess was almost identical to unit 601.The second alternative design, unit 602, also had sig-

ni®cant impacts on the economic and environmentalanalyses. The added production of allyl chloride incr-eased the annual operating revenue $1.5 million or 8.3%over the base design. The total operating expenses forunit 602 increased slightly as compared to unit 601. Thisprimarily results from increased feed rate of propylenerequired to balance that increased amount of propylenethat was converted in the reactor. However, the netoperating pro®t increased to $10 million annually. Thisis a 23% improvement over the initial design.

Fig. 4. The total potential environmental impact output index, I:�t�out, for the three process designs: the base design (unit 600), and the two alternative

designs (units 601 and 602). The contributions to the index attributable to the process e�uents and to the energy generation e�uents are indicated.

The values of the indexes are in units of PEI/h.


The capital costs for unit 602 are almost identical tothose seen in unit 601, which amount to a 15% reduc-tion over the capital costs required for the base case,unit 600.Figs. 3±5 show how unit 602 compares with the other

design options from an environmental viewpoint. Fig. 5displays values for the PEI output index that has beennormalized to production rate of the allyl chloridestream, I

�t�out. Figs. 4 and 5 clearly show that unit 602 has

a much more environmentally friendly design than boththe base design, unit 600, and the ®rst alternativedesign, unit 601. The I

:�t�out has been decreased to 440

PEI/hr from the 700 PEI/hr observed in the base design.This represents a 37% improvement in the environ-mental performance. If the change in production istaken into consideration (Fig. 5), a 43% improvementin environmental performance is observed. That repre-sents a decrease from 0.59 PEI/kg allyl chloride productstream in unit 600 to 0.34 PEI/kg of allyl chloride pro-duct stream in unit 602. Fig. 3 shows that the HTPI andthe TTP impact categories have been signi®cantlyreduced by the modi®cations made in unit 602.In this illustrative case study, the importance of con-

currently considering the economic performance ofchemical process as well as the environmental perfor-mance of the chemical process was shown. There weretwo primary areas of concern in this case study: therefrigeration cycle and the reactor performance. The ®rstconcern addressed the refrigeration cycle. This was aneconomic concern that led to improved environmentalperformance. The second concern, the reactor perfor-mance, addressed environmental concerns which also

improved the economic performance. In this case study,it can be easily concluded that the second alternativedesign, unit 602, was superior to both the originaldesign, unit 600, and the ®rst alternative design, unit601, in economic and environmental performance.

5. Conclusions

In designing chemical manufacturing processes, it isimperative that environmental impact analysis be per-formed in concert with the traditional economic analy-sis. This point has been demonstrated in great detailwith an illustrative case study. In this case study, a baseprocess design option was considered along with twoalternative design options. All three designs were eval-uated from an economic and an environmental view-point. The second design option, unit 602, was farsuperior in both analyses to the other two designs. TheWAR algorithm was used to evaluate the environmentalperformance of the process designs; the ICARUS Pro-cess Evaluator was used to evaluate the economic por-tion (operating and capital costs) of the case study.

Acknowledgements

The authors wish to acknowledge the encouragementgiven by Subhas K. Sikdar, the Director of the Sustain-able Technology Division, and the management at theNational Risk Management Research Laboratoryincluding E. Timothy Oppelt and Teresa Harten.

Fig. 5. The total potential environmental impact output index normalized to the allyl chloride production rate, I�t�out, for the three process designs:

the base design (unit 600), and the two alternative designs (units 601 and 602). The contributions to the index attributable to the process e�uents

and to the energy generation e�uents are indicated. The values of the indexes are in units of PEI/kg of allyl chloride product stream.


References

[1] Douglas JM. Conceptual design of chemical processes. New

York: McGraw Hill, 1988.

[2] Biegler LT, Grossman IE, Westerberg AW. Systematic methods

of chemical process design. NJ: Prentice Hall PTR: Upper Saddle

River, 1997.

[3] Turton R, Bailie RC, Whiting WB, Shaeiwitz JA. Analysis,

synthesis, and design of chemical processes. Upper Saddle River,

NJ: Prentice-Hall Inc, 1998.

[4] Shenoy UV. Heat exchange network synthesis: process optimiza-

tion by energy and resource analysis. Houston, TX: Gulf Pub-

lishing Company, 1995.

[5] Gundersen T, Naess L. The synthesis of cost optimal heat

exchanger networks: an industrial review of the state of the art.

Comp Che Eng 1988;12:503±30.

[6] El-Halwagi MM, Manousiouthakis V. Synthesis of mass

exchange networks. AIChE J 1989;35:1233±44.

[7] Hilaly AK, Sikdar SK. Pollution balance: a new method for

minimizing waste production in manufacturing processes. J Air &

Waste Manage Assoc 1994;44:1303±8.

[8] Cabezas H, Bare JC, Mallick SK. Pollution prevention with che-

mical process simulators: the generalized waste (WAR) algorithm

Ð full version. Comp Che Eng 1999;23:623±34.

[9] Young DM, Cabezas H. Designing sustainable processes with

simulation: The waste reduction (WAR) algorithm. Comp Che

Eng 1999;23:1477±91.

[10] Cano-Ruiz JA, McRae GJ. Environmentally conscious chemical

process design. Annu Rev Energy Environ 1998;23:499±536.

[11] Houghton JT, Filho LGM, Callander BA, Harris N, Kattenberg

A, Maskell K. eds. Climate change 1995: the science of cli-

mate change. Cambridge (UK): Cambridge University Press,

1996.

[12] Grossman IE, Drabbant R, Jain RK. Incorporating toxicology in

the synthesis of industrial chemical complexes. Chem Eng Com-

mun 1982;17:151±70.

[13] Fathi-Afshar S, Yang JC. Designing the optimal structure of the

petrochemical industry for minimum cost and least gross toxicity

of chemical production. Chem Eng Sci 1985;40:781±97.

[14] Heinzle E, et al. Ecological and economic objective functions for

screening in integrated development of ®ne chemical processes 1.

Flexible and expandable framework using indices. Ind Eng Chem

Res 1998;37:3395±407.

[15] Koller G, et al. Ecological and economic objective functions for

screening in integrated development of ®ne chemical processes. 2.

Stream allocation and case studies. Ind Eng Chem Res 1998;

37:3408±13.

[16] Pistikopoulos EN, Stefanis SK, Livingston AG. A methodology

for minimum environmental impact analysis. AICHE Sympo-

sium Series 1994;90:139±50.

[17] King JMP, Banares-Alcantara R, Manan ZA. Minimising envir-

onmental impact using CBR: an azeotropic distillation case

study. Environ Model Soft 1999;14:359±66.

[18] Aspen Technology, Aspen Plus 10.1, 1998.

[19] ICARUS Corporation, ICARUS Process Evaluator, 1998.

[20] Cabezas H, Bare JC, Mallick SK. Pollution prevention with che-

mical process simulators: the generalized waste reduction (WAR)

algorithm. Comp Che Eng 1997;21:S305±10.


The waste reduction (WAR) algorithm: environmental impacts, energy consumption, and engineering...

Documents

Transcript of The waste reduction (WAR) algorithm: environmental impacts, energy consumption, and engineering...