Pollution prevention with chemical process simulators: the generalized waste reduction (WAR) algorithm full version

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1 Computer and Chemical Engineering 23 (1999) Pollution prevention with chemical proce imulator: the generalized wate reduction (WAR) algorithm full verion Heriberto Cabeza *, Jane C. Bare, Subir K. Mallick 1 U.S. En ironmental Protection Agency, National Rik Management Reearch Laboratory, Sutainable Technology Di iion, Sytem Analyi Branch, 26 Wet Martin Luther King Dri e, Cincinnati, OH 45268, USA Received 11 February 1998; received in revied form 3 July 1998 Abtract A general theory for the flow and the generation of potential environmental impact through a chemical proce ha been developed. The theory define ix potential environmental impact indexe that characterize the generation of potential impact within a proce, and the output of potential impact from a proce. The indexe are ued to quantify pollution reduction and to develop pollution reducing change to proce flow heet uing proce imulator. The potential environmental impact are calculated from tream ma flow rate, tream compoition, and a relative potential environmental impact core for each chemical preent. The chemical impact core include a comprehenive et of nine effect ranging from ozone depletion potential to human toxicity and ecotoxicity. The reulting wate reduction methodology or WAR algorithm i illutrated with two cae tudie uing the chemical proce imulator Chemcad III (Ue doe not imply USEPA endorement or approval of Chemcad III) Elevier Science Ltd. All right reerved. 1. Introduction There i currently a great deal of interet in the development of method that can be ued to prevent or at leat minimize the generation of pollution; and there are numerou effort underway in thi area (Lederman & Weaver, 1991; El-Halwagi, El-Halwagi & Manouiouthaki, 1992; Fonyo, Kurum & Rippen, 1994; Mallick, Cabeza, Bare and Sikdar, 1996; Manouiouthaki and Allen, 1995; Roiter, 1995). Thi interet tem from the belief that pollution prevention i likely to lead to the creation of technologie that have a much more benign impact on human health and the environment. Becaue thi technology i inherently le polluting, it i likely to be more robut and economical than imply adding pollution control device to conventional deign. In chemical manufacturing, thee pollution prevention method take the form of an effort to deign proce plant that generate a little pollution a * Correponding author. Fax: addre: cabeza.herberto@epamail.epa.gov (H. Cabeza) 1 Pot Doctoral Reearch Fellow, Oak Ridge Intitute for Science and Education. Preent addre: Simulation Science Inc., 61 South Valencia Avenue, Brea, CA 92621, USA. poible. Since chemical proce imulator are widely ued in the deign and operation of chemical manufacturing plant, the development of a pollution prevention methodology for chemical proce imulator i likely to have a ignificant impact on the pollution generated by the chemical indutry. At the National Rik Management Reearch Laboratory, reearch effort are underway to develop a methodology for commercial chemical proce imulator. The reearch effort i called the wate reduction or WAR algorithm after Hilaly and Sikdar (1994) who performed ome of the early work in thi area. Thi paper preent a generalization of the WAR algorithm, dicue the methodology for evaluating potential environmental impact, and illutrate the ue of the method in the deign or modification of chemical procee with two cae tudie. 2. Potential environmental impact theory Potential environmental impact i the unrealized effect or impact that the emiion of ma and energy would have on the environment on average. It i, /99/$ - ee front matter 1999 Elevier Science Ltd. All right reerved. PII: S (98)298-1

2 624 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) therefore, eentially a probability function for the realization of a potential effect. Thu, the potential environmental impact of chemical manufacturing procee are generally caued by the energy and material that the proce take from or emit to the environment. Potential environmental impact i a conceptual quantity that can not be directly meaured, i.e. there are no potential environmental impact meter. However, one can calculate potential environmental impact from related meaurable quantitie uing functional relation between the two. Thi ituation i common in cience and engineering. For example, the energy of a fluid can not be directly meaured, but it can be calculated from temperature and preure by the ue of heat capacitie and equation of tate. Exactly how to perform a calculation for potential environmental impact will be dicued later in thi paper Coner ation equation Traditionally, chemical proce deign ha been baed on the creative application of ma and energy balance along with thermodynamic, chemical reaction engineering, and engineering economic. Our methodology propoe to add a conervation relation over potential environmental impact to the aforementioned two balance equation. The conervation equation for impact i baed on an accounting of the flow of potential environmental impact in and out of the procee. Thi flow of impact i related to the ma and energy flow but it i not equivalent to them. The impact conervation equation i di yt =I in I out +I gen (1) dt where I yt i the potential environmental impact content inide a proce, I in i the input rate of impact, I out i the output rate of impact, and I gen i the rate at which impact i generated in the ytem by chemical reaction or other mean. Note that procee can alo conume potential environmental impact o that I gen can, in fact, be negative. For teady tate procee, the conervation equation reduce to, =I in I out +I gen (2) which implie that no potential environmental impact accumulate in the ytem. Alo note that Eq. (1) and (2) erve a definition of the function I gen. The ignificance of potential environmental impact can be better undertood by conidering the following definition. If one were to dump into the environment all of the ma and energy flow entering a proce, the reulting impact on the environment would equal to I in ; if one were to alo dump into the environment all of the ma and energy flow exiting a proce the reulting impact on the environment would be equal to I out. However, due to chemical tranformation and change in tate condition (temperature and preure), I in i never exactly equal to I out, and conequently I gen i never exactly equal to zero for teady tate procee Chemical procee Application of either Eq. (1) or Eq. (2) to chemical manufacturing procee require an expreion that relate the conceptual potential environmental impact to meaurable quantitie. Potential environmental impact are caued by energy and material input and output to or from the environment. But, a a firt approach, thi treatment i retricted to potential impact due to material flow while neglecting any impact due to energy. Effect due to energy flow can be incorporated into the analyi by extending the boundary over which the impact balance i done to include the energy generation proce. Effect due to reource depletion are alo neglected mainly becaue there i no effective methodology for meauring them. Thi i conitent with the focu of thi work which i the chemical proce plant rather than a global life-cycle type of analyi. The expreion relating potential chemical environmental impact to meaurable i I i = j I j (i) = j (i) M j x kj j + (3) k where the um over j i taken over the tream of input i or output i, the um over k i taken over all chemical k, I i i the rate of potential environmental impact either in (i=in) or out of the proce (i=out), I j (i) i the rate of potential environmental impact for tream j which may be an input or an output, M j (i) i the ma flow rate of tream j which may again be either an input or an output, x kj i the ma fraction of chemical k in tream j, and j i the overall potential environmental impact of chemical j. Note that Eq. (3) i a firt order approximation that doe not include the ynergitic effect that can occur when multiple chemical are preent Impact indexe For teady tate procee one can ue Eq. (2) to define two categorie of indexe for the environmental impact of chemical manufacturing. The firt category of indexe meaure the generation of potential environmental impact within procee, and the econd one meaure the potential environmental impact emitted by procee. There are variou indexe that can be defined within each category. However, only the ix indexe, three from each category, that eem mot ueful for wate reduction will be treated here. Following Hilaly and Sikdar (1994), all non-product are conidered to be pollutant and the potential environmental impact of all product i et to zero, i.e.

3 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) j = for all product j. Thee aumption are conitent with the objective of thi paper which i to preent a methodology for wate reduction, i.e. the primary concern i reducing the impact and the amount of the non-product. The broader implication of Eq. (1), including other impact indexe for which j for product j and further conjecture on the implication for utainability, will the ubject of future publication.the firt index of the firt category of indexe (impact generation) i obtained by olving Eq. (2) for I gen and adding the upercript for non-product to give, I gen =I out I in (4) where I out and I in are the potential environmental impact due to non-product, i.e. pollutant in the output and input, repectively. Eq. (3) i ued to give explicit expreion for I out and I in where all component, product and non-product, are included in the ummation, but where j = for all product j which effectively remove all product from the ummation. The index, I gen, meaure the total rate at which the proce generate potential environmental impact due to non-product (). I gen ha unit of potential environmental impact generated per time. The econd index, I gen, of the firt category i obtained by dividing Eq. (4) by the rate at which the proce generate product to give a pecific impact generation, I gen = I gen = P p p I out I in (5) P p p where the um over p i taken over all the product p, and P p i the ma flowrate of product p. I gen meaure the potential impact created by all non-product in manufacturing a unit ma of all the product p. The index, I gen, ha unit of potential environmental impact per ma of product. The third index of the firt category, M gen, i obtained from Eq. (5) by etting the potential environmental impact ( j ) of all product to zero and that of all non-product to one. Thi ha the effect of aigning the ame potential environmental impact to all nonproduct. The index, M gen, i a meaure of the ma inefficiency of the proce, i.e. it give the ratio of ma converted to an undeirable form to ma converted to a deirable form. The expreion for M gen i M gen = j (out) M j x kj k j (in) M j x kj k (6) P p p (out) where the ummation over M j i taken only over (in) output tream, the ummation over M j i taken only over input tream, and the ummation over x kj i taken only over all non-product k in tream j. The unit of M gen are ma of non-product per ma of product. The firt index of the econd category of indexe (impact emiion) I out i imply the total rate of potential environmental impact output due to non-product. I out i calculated from Eq. (3) ubject to j = for all product j. The index, I out, i a meaure of the rate at which the proce emit potential environmental impact, and it ha unit of potential environmental impact per time. The econd index of the econd category, I out, i obtained by dividing the rate of potential environmental impact output due to non-product by the output rate of product to give, I out = I out (7) P p p The index, I out, ha unit of potential environmental impact per ma of product. Thi expreion give the pollution index of Mallick et al. (1996) which meaure the potential environmental impact emitted in manufacturing a unit ma of product. The third index of the econd category, M out, i obtained from Eq. (7) by etting the potential environmental impact ( j ) of all product to zero and that of all non-product to one. The reulting expreion i, M out = j (out) M j x kj k (8) P p p which i related to the pollution index n of Hilaly and Sikdar (1994) by, M out = n n (9) where the ummation i taken over all product n. M out meaure the amount of non-product or pollutant ma emitted in manufacturing a unit ma of product, and it ha unit of non-product ma per ma of product. It i alo a ma inefficiency meaure Significance of impact indexe The firt category of indexe, i.e. I gen, I gen, and M gen, characterize ome apect of the generation of potential environmental impact within a manufacturing proce. They are mot ueful in addreing quetion related to the internal environmental efficiency of the proce plant, i.e. the ability of the plant to produce deired product while creating a minimum of new, undeired potential environmental impact. It i important to note that once new potential environmental impact i created, reource uch a potentially cotly remediation

4 626 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) effort will likely be required to prevent the potential impact from being realized. Obviouly, the maller the value of I gen, I gen, and M gen the more environmentally efficient the proce, and, all other factor uch a economic being equal, the more deirable. I gen would be ueful in comparing different deign on an abolute bai, while I gen and M gen would be ueful in comparing different deign independently of manufacturing plant ize. The econd category of indexe, i.e. I out, I out, and M out, characterize ome apect of the emiion of potential environmental impact from a manufacturing proce. Their principal ue i in addreing quetion related to the external environmental efficiency of the proce plant, i.e. the ability of the plant to produce deired product while inflicting on the environment a minimum of undeired potential environmental impact. It i again obviou that the maller the value of I out, I out, and M out, the more environmentally efficient the proce, and all other factor uch a economic being equal, the more deirable it i. Since I out i a total rate of impact output, it could be ueful in deciding whether a given plant i compatible with a particular ite. For example, it would be unwie to locate a plant with a high I out in an ecologically enitive area. I out could alo be ued in matching the ize of a plant to the capacity of the urrounding environment to diipate environmental impact. I out, and M out are more ueful in com- paring the potential environmental impact of alternative procee independently of plant ize. 3. Chemical environmental impact Application of the methodology o far developed require that the potential environmental impact of chemical be etimated. Further, it i required that a relative impact number j be given for each chemical j over a wide range of different chemical environmental impact. Thi i, unfortunately, not a trivial matter becaue chemical impact are meaured on different relative cale that can not be imply added without ome form of normalization Chemical impact expreion To apply the WAR methodology to chemical procee, the following expreion for j ha been developed (Mallick et al. 1996), j = l j,l (1) l where the um i taken over categorie of potential chemical environmental impact, e.g. ozone depletion potential, human health, etc. lited below under Section 3.2. l i a relative weighting factor for impact of type l independent of chemical j, and j,l i the pecific potential environmental impact of chemical j for an impact of type l. l ha unit of potential environmental impact per ma. The relative weighting factor l allow Eq. (1) to be cutomized to pecific or local condition. The uggeted procedure i to initially et all the l to ame value of ay one, and to allow uer to vary individual l from to 1 according to local need and policie. Pleae note that for a relative comparion, the abolute value i not critical. For example, photochemical oxidation potential would be weighted more heavily than other impact in an area that uffer from mog. There i an effort underway in our reearch group to develop more ophiticated method of determining value for the l. The value for the j,l were obtained from the relative ranking or core for chemical by normalizing according to, i, j = (Score) i, j (11) (Score) i j +2 j where (Score) i,j i the relative core of chemical i on ome arbitrary cale within impact category j, (Score) i j i the arithmetic average of the core of all chemical i within impact category j, and j i the tandard deviation of all the chemical core in impact category j. The normalizing factor (Score) i j +2 j aure that about 75% of the normalized chemical core number j,l will be between and 1 irrepective of the tatitical ditribution of the initial core a expected from Chebyhev theorem (Lapin, 1975). If the chemical core happen to follow a normal ditribution, then the normalization range extend to 95% of the core Claification of impact The claification of chemical environmental impact and the value for the (Score) i, j were adopted from the tudy of Heijung, Guinée, Huppe, Lankreijer, Udo de Hae and Wegener Sleewijk (1992) 2 and normalized according to Eq. (11) to obtain the j,l. In the calculation of (Score) i j and j, the chemical core for dioxin, chromium VI, and vinyl chloride were excluded. The reaon i that the core number for thee three chemical were everal order of magnitude higher that thoe for all other chemical, and including them in the normalization proce would have made the normalization meaningle. Therefore, the normalized j,l for thee three chemical would appear a outlier which they, in fact, are. 2 Ue by the author doe not imply endorement or approval by the U.S. Environmental Protection Agency.

5 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) There are nine different impact categorie. Thee can be ubdivided into four environmental phyical potential effect (acidification, greenhoue enhancement, ozone depletion, and photochemical oxidant formation), three human toxicity effect (air, water, and oil), and two ecotoxicity effect (aquatic and terretrial). The normalized chemical core ued in the two cae tudie preented in the next ection are given in Table 1 below where H 2 i hydrogen, MEK i methyl ethyl ketone, SBA i econdary butyl alcohol, H 2 O i water, N 2 i molecular nitrogen, Ar i argon, CH 4 i methane, and NH 3 i ammonia. Thee acronym will be ued throughout the ret of the paper. 4. Cae tudy c1: MEK production To illutrate the ue of the generalized WAR algorithm, a cae tudy from the production of methyl ethyl ketone (MEK) from econdary butyl alcohol (SBA) i preented. The cae tudy wa adopted from the Chemcad III (Chemtation, 1997) chemical proce imulator, and all the material and energy balance were performed uing Chemcad III. However, any commercial proce imulator could have been ued. Thi cae tudy preent a typical chemical engineering proce for the production of a commodity chemical that involve everal unit procee uch a reactor, eparator, mixer, divider, and heat exchanger. It i, therefore, ufficiently complex to illutrate the WAR algorithm but till treatable within the pace of thi paper. Eentially, the chemical proce conit of a SBA dehydrogenation reactor followed by a MEK purification train and aociated equipment Bae flowheet Fig. 1 how the bae proce flow diagram for the production of MEK from SBA. SBA i fed to a hydrogen crubber where the feed SBA crub reidual MEK from the hydrogen tream. The SBA feed i then pumped up to reaction preure and heated to reaction temperature with a heat exchanger and a heater. The heated SBA i fed to the reactor where the chemical tranformation occur. The reactor output tream i ent to a heat exchanger where it i partially condened. The mixture of MEK, hydrogen, and unconverted SBA Table 1 Normalized chemical impact core H2 MEK SBA H2O E-4 N2 Ar CH4 NH3 7.4E-3.93 Fig. 1. Bae proce flow diagram for the production of methyl ethyl ketone from econdary butyl alcohol. i cooled further and ent to a eparator where the hydrogen i flahed off. The hydrogen i then crubbed and the liquid phae fed to a MEK purification ytem. The ma flow rate and the tate of the variou input and output tream a calculated by Chemcad III are lited in Table 2 above Modified flowheet Examination of the bae proce indicate that wate tream 13 contain large amount of unreacted SBA and mall amount of MEK. It i, therefore, logical to try to recover the SBA and MEK a the firt tep in a wate reduction trategy. Conequently, the proce flow diagram wa modified by the addition of a recycle from tream 13 to the feed, tream 1. Two cae were tudied with thi modification, recycling 5 and 1% of tream 13. Recycling 1% of tream 13, i.e. cloing off tream 18, i appropriate for thi illutration. But, it i clearly not practical becaue tream 18 i the only liquid wate tream in the modified proce and the only mean of purging the ytem of liquid impuritie. Without thi purge, impuritie would build up inide the proce cauing it to eventually ceae to function. Fig. 2 below how the flow diagram for the modified proce. The ma flow rate and the tate of the variou input and output tream for the modified proce with 5% recycle are lited in Table 3 The proce modification increaed the amount of product by 73% while reducing the amount of wate SBA in tream 18 by 2%. It i important to note that an examination of Table 2 4 will indicate that wate wa generally reduced, and that environmental impact wa probably alo reduced. However, the information o far conidered i not ufficient to allow a quantitative comparion of the overall wate and environmental impact reduction aociated with each of the three cae tudied here. For thi

6 628 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) Table 2 MEK production flow ummary (kg/hr) input & output: bae proce Stream (tate) c 1 (L) c 2 (G) c 12 (L) c 13 (L) c 14 (G) SBA MEK H2O 8 8 H2 18 comparion one mut calculate the impact indexe already decribed. For the modified proce with 1% recycle, the ma flow rate and the tate of the variou input and output tream are lited in Table 4 below. Note that increaing the recycle increaed the amount of product by 269% while imultaneouly reducing the amount of wate SBA in tream 13 to zero Impact index calculation Six different impact indexe were calculated for the bae cae and the two modified procee each. The indexe were obtained uing Eq. (3) (8), the flow rate from Table 2 4, Eq. (1), and the normalized chemical impact core of Table 1. The relative weighting factor, l, were all et to one for thee calculation. The firt category indexe, i.e. the impact generation indexe, I gen, I gen, and M gen are hown on Fig. 3 below. It hould be noted that M gen i a negative number ince ome of the input ma i alway converted to product, and the product are not included in the ummation of the output. The pecific indexe, I gen, and M gen were multiplied by a factor of 1 o that they could be hown on the ame cale a the rate index. The econd category indexe, i.e. the impact output indexe I out, I out, and M out, are hown in Fig. 4. The pecific index I out wa multiplied by a factor of 1, and the pecific index M out wa multiplied by a factor of 1 o that they could both be hown on the ame cale a I out. The larget ource of uncertainty in the calculation of the impact indexe i the environmental impact core. Thee meaurement are probably accurate to no more than one ignificant figure or an order of magnitude. It i, therefore, prudent to aume that impact index calculation are alo accurate to no more than one ignificant figure. Two ignificant figure are ued in Fig. 3 and 4 in order to help the reader to reproduce the calculation, if neceary. The impact generation indexe of Fig. 3 how that: (1) the rate of impact generation by non-product decreae by 13% for 5% recycle and by 2% for 1% recycle, (2) the impact generated by non-product kg 1 of product decreae by 48% for 5% recycle and by 77% for 1% recycle, and (3) the kilogram of nonproduct generated kg 1 of product remain nearly contant at 1. The mot ignificant index in thi cae i the impact generated kg 1 of product. The decreae of thi index reflect the increae in the productivity of the plant, i.e. the increae in product flow rate. The impact output indexe of Fig. 4 how that: (1) the rate of impact output from non-product decreae by 11% for 5% recycle and by 17% for 1% recycle, (2) the impact output from non-product kg 1 of product decreae by 48% for 5% recycle and by 78% for 1% recycle, and (3) the output of kilogram of non-product kg 1 of product decreae by 6% for 5% recycle and by 88% for 1% recycle. It i worth noting that the output of impact and wate decreaed a meaured by all the indexe. The mot ignificant index in thi cae i the impact output kg 1 of product. The decreae in thi index i conitent with the decreae in the impact generation per ma of product index dicued in the paragraph above. Thi decreae i alo a reflection of the increaed productivity of the plant. The decreae in the indexe are ufficiently large uch that they repreent ignificant reduction in pollution. The conitent decreae in the impact generation per ma of product (48 77%) and the impact output 5. Dicuion of cae tudy c1 Fig. 2. Modified proce flow diagram for the production of methyl ethyl ketone from econdary butyl alcohol.

7 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) Table 3 MEK production flow ummary (kg/hr) input & output: modified proce (5% recycle) Stream (tate) c 1 (L) c 2 (G) c 12 (L) c 14 (G) c 18 (L) SBA MEK H2O H2 29 per ma of product (48 78%), imply mean that the modified plant can meet the need of a much larger market without increaing it generation or it output of potential environmental impact. It alo mean that a modified plant that i 48 77% maller than the bae cae can meet the need of the ame market that the bae plant wa deigned for. 6. Cae tudyc2: ammonia production To further illutrate the ue of the generalized WAR Algorithm, a econd cae tudy from the production of ammonia from ynthei ga i preented. The cae tudy wa alo adopted from the Chemcad III (Chemtation, 1997) chemical proce imulator, and all the material and energy balance were performed uing Chemcad III. However, any commercial proce imulator could again have been ued. Jut a cae tudy c1, thi cae tudy alo preent a typical chemical engineering proce that involve everal unit procee uch a reactor, eparator, mixer, divider, and heat exchanger. It i alo ufficiently complex to illutrate the WAR algorithm but till treatable within the pace of thi paper Bae flowheet Fig. 5 how the bae proce flow diagram for the proce. Eentially, the overall proce i baed on the reaction of nitrogen and hydrogen to produce ammonia. The mixture of hydrogen and nitrogen i compreed and cooled and feed to a erie of three reactor through a flah drum. Several reactor are normally ued to maximize the converion of feed to product which for thi proce i difficult to do. Thi flah drum alo erve to eparate the ammonia product from the unreacted gae which are feedback into the ytem. The ammonia i recovered a an anhydrou liquid under modet preure. Thema flow rate and the tate of the input and output tream a calculated by Chemcad III are all lited in Table Modified flowheet Examination of the bae proce indicate that wate tream 17 contain ammonia and ome unreacted feed. It i, therefore, logical to try to recover the ammonia and the unreacted feed a an obviou firt tep in a wate reduction trategy. Conequently, the proce flow diagram wa modified in two way. Firt, the purge ratio wa reduced five fold from.1 to.2, i.e. the flow of tream 17 wa reduced 5-fold. Second, in addition to reducing the purge, a flah drum wa added with tream 17 a the feed to recover ome of the ammonia. Fig. 5 how the configuration of the flow diagram for the reduced purge modified proce which i identical to that of the bae proce. The ma flow rate and the tate of the input and output tream for the reduced purge modified proce are lited in Table 6. The proce modification increaed the amount of product by 25% while reducing the amount of wate ammonia in tream 17 by 77%. It i worth noting, however, that there wa an mall increae in the impuritie preent in the product tream. Thi changed from about 2% impuritie in the bae cae to 3% for the reduced purge modified cae. Fig. 6 how the flow diagram for the modified proce with reduced purge and addition of a flah drum with tream 17 a the feed. Under thi configuration, tream 25 i ued to purge impuritie from the ytem. Without thi purge, impuritie would again build up inide the proce, and it would eventually ceae to function. Stream 24 which conit of eentially pure ammonia i mixed with tream 19 to form a new product tream, tream 26. The ma flow rate and the tate of the variou input and output tream are lited in Table 7. Note that adding the flah drum in addition to reducing the purge 5-fold increaed the amount of product by 26% a compared to the bae cae. Thi i very cloe to the 25% increae in product that wa obtained by imply reducing the purge. However, the amount of wate ammonia in tream 25 wa reduced by 91%, and the amount of total wate in tream 25 wa reduced by 78%, both Table 4 MEK production flow ummary (kg/hr) input & output: modified proce (1% recycle) Stream (tate) c 1 (L) c 2 (G) c 12 (L) SBA MEK H2O H2 6 c 14 (G)

8 63 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) Fig. 3. Impact generation indice for the production of methyl ethyl ketone from econdary butyl alcohol I out in impact unit of pollutant h 1, I out in impact unit of pollutant kg 1 of product, and M out in kg of pollutant kg 1 of product. compared to the bae cae. A compared to the reduced purge proce, the addition of the flah drum increaed the amount of product by a meager 1%, but it reduced the amount of wate ammonia by 61% and total amount of wate by 18%, both in tream 25. Therefore, the principal effect of adding the flah drum wa the reduction of wate. An examination of Table 5 7 will again how that wate wa generally reduced, that the amount of product made wa increaed, and that environmental impact of the proce wa probably alo reduced. However, one find that it i not ufficient to allow a quantitative comparion of the overall wate and environmental impact aociated with each of the three Fig. 5. Bae proce flow diagram for the production of ammonia from ynthei ga. cae. In a imple example uch a thi one, the tak i confuing, but for complex chemical procee it can become impoible. For thi comparion one mut calculate the impact indexe Impact index calculation Again, ix different impact indexe were calculated for the bae and the two modified procee. The indexe were alo obtained uing Eq. (3) (8), the flow rate from Table 5 7, Eq. (1), and the normalized chemical impact core of Table 1. The relative weighting factor were all et to one for thee calculation. The firt category indexe, i.e. the impact generation indexe, I gen, I gen, and M gen, are hown on Fig. 7. It hould be noted that M gen i a negative number ince ome of the input ma i alway converted to product, and the product are not included in the ummation of the output. The pecific index, I gen, wa multiplied by a factor of 1, and the index, M gen, wa multiplied by a factor of 1 o that they could both be hown on the ame cale a the rate index. The econd category indexe, i.e. the impact output indexe, I out, I out, and M out, are hown in Fig. 8. The pecific index I out and the pecific index M out were both multiplied by a factor of 1 o that they could be hown on the ame cale a I out. Table 5 Ammonia production flow ummary (kg/hr) input & output: bae proce (purge ratio=.1) Stream (tate) c 1 (G) c17 (G) c 19 (L) Fig. 4. Impact output indexe for the production of methyl ethyl ketone from econdary butyl alcohol: I out in impact unit of kg h 1, I out in impact unit of pollutant kg 1 of product, and M out in kg of pollutant kg 1 of product. N Ar H CH NH

9 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) Table 6 Ammonia production flow ummary (kg/hr) input & output: modified proce (purge ratio=.2) Table 7 Ammonia production flow ummary (kg/hr) input & output: modified proce (purge ratio=.2 & flah drum) Stream (tate) c 1 (G) c17 (G) c 19 (L) Stream (tate) c 1 (G) c25 (G) c 26 (L) N Ar H CH NH N Ar H CH NH The larget ource of uncertainty in the calculation of the impact indexe are again the environmental impact core. Thee meaurement are probably accurate to no more than one ignificant figure or an order of magnitude, and it i, therefore, prudent to aume that impact index calculation are alo accurate to no more than one ignificant figure. Two ignificant figure are preented in Fig. 7 and 8 in order to allow reader to reproduce the calculation if neceary. 7. Dicuion of cae tudy c2 The impact generation indexe of Fig. 7 how that: (1) the rate of impact generation by non-product decreae by 77% when the purge ratio i decreaed from.1 to.2 and by 91% when the purge ratio i decreaed a above and a flah drum i added to recover wate ammonia; (2) the impact generated by non-product kg 1 of product decreae by 81% when the purge ratio i reduced from.1 to.5 and by 93% when the purge ratio i decreaed a above and a flah drum i added; and (3) the kilogram of non-product generated kg 1 of product remained nearly contant at 1 for all cae. The mot ignificant index in thi cae i the impact generated kg 1 of product. The decreae of thi index reflect primarily the recovery of the wate product and to a maller extent the increae in the productivity of the plant, i.e. the increae in product flow rate. The impact output indexe of Fig. 8 how that: (1) the rate of impact output from non-product decreae by 76% when the purge ratio i decreaed from.1 to.2 and by 91% when the purge ratio i decreaed a above and a flah drum i added to recover wate ammonia; (2) the impact output from non-product kg 1 of product decreae by 81% when the purge ratio i reduced from.1 to.5 and by 93% when the purge ratio i decreaed a above and a flah drum i added; and (3) the output of kilogram of non-product kg 1 of product decreae by 73% when the purge ratio i reduced from.1 to.5 and by 76% when the purge ratio i decreaed a above and a flah drum i added. It i again worth noting that the output of impact and wate decreaed a meaured by all the indexe. The mot ignificant index in thi cae i the impact output kg 1 of product. The decreae in thi index wa the ame a that for the equivalent generation index. Thi i a reflection of the change in the index being primarily driven by the recovery of wate product rather than increaed productivity. Fig. 6. Modified proce flow diagram for the production of ammonia from ynthei ga with reduced purge ratio and added fluh drum. Fig. 7. Impact generation indexe for the production of ammonia from ynthei ga: I gen in impact unit h 1, I gen in impact unit kg 1 of product, and M gen in kg of pollutant kg 1 of product. PR i the purge ratio.

10 632 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) Fig. 8. Impact output indexe for the production of ammonia from ynthei ga: I out in impact unit of pollutant h 1, I out in impact unit of pollutant kg 1 of product, and M out in kg of pollutant kg 1 of product. PR i the purge ratio. The decreae in the indexe are again ufficiently large that they repreent ignificant reduction in pollution. The conitent decreae in the impact generation per ma of product and impact output per ma of product (81% to 93%) implie that the modified plant can meet the need of a moderately larger market with much le impact on the environment. The decreae in the rate of impact generation and impact output (76% to 91%) mean that the modified proce ha an environmental impact that i roughly one tenth that of the bae plant. Latly, the decreae in the output of wate ma per ma of product indicate that the modified plant will loe le valuable material in it operation. 8. Future work In addition to the topic already mentioned, there are two other iue that need to be further mentioned in relation to the WAR algorithm: engineering economic and computer aided proce deign. However, both of thee are beyond the cope of thi paper which i to preent the baic generalized wate reduction or WAR algorithm and to illutrate it ue. Engineering economic and computer aided proce deign are the ubject of preent and future reearch, and they are included here only for completene and to aid intereted reader applying the method and furthering the work Engineering economic Whenever a proce i modified to reduce wate, there i a conequent change in the economic aociated with it. Economic change are generally due to: (1) the addition or removal of capital equipment; (2) an increae or decreae in energy conumption; (3) a change in the rate of conumption of feed material; and (4) a change in the rate of product generation. When a proce i modified, all of the above are frequently affected. There are well etablihed method for etimating the economic of chemical procee either manually (Peter & Timmerhou, 1968; Richardon Engineering Service, 1997) or with the aid of a computer (Apen Technology, 1997; ICARUS Corporation, 1997). Modification of a chemical proce uing the WAR algorithm need to be done along with an evaluation of the economic conequence of any propoed proce modification, i.e. one need to imultaneouly compare both the potential environmental impact and the cot of alternate proce flowheet. The reaon i that the ultimate objective i alway that of developing cot effective reduction in pollution. Unfortunately, there are no conenu criteria for cot effective wate reduction. Although one poibility, conitent with traditional proce deign procedure, i to require that the um of the capital and operating cot hould not increae with propoed proce modification from the bae configuration. Thi can be expreed a, [C o +C c ] Bae [C o +C c ] Modified (12) where C o i the operating cot and C c i the capital cot that can be etimated by one of the aforementioned method or ome another proprietary method. Eq. (12) can then be ued jointly with Eq. (4) (8) to evaluate alternative proce flowheet Computer aided proce deign While it i often poible to devie pollution reducing modification from an inpection of the proce flowheet, there are many ituation where a more ytematic approach uch a computer aided proce deign and optimization may be required. Thi i particularly important with very complex procee that are difficult to analyze by inpection. The WAR algorithm can be ued in computer aided proce deign and optimization. Thi can be done by employing the indexe of Eq. (4) (8) a objective function in a mathematical optimization ubject to a cot contraint uch a Eq. (12). For example, one could minimize the output of potential environmental impact per ma of product given by Eq. (7) ubject to keeping the total cot from increaing. Thi can be expreed a, Minimize I out =I out (M (out), x 1, x 2,, x n, P ) (13) ubject to Eq. (12) where M out i the vector of ma flowrate for all the output tream, x i i the vector of ma fraction for non-product component i in all

11 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) output tream, and P i the vector of ma flowrate for all product. The optimization could involve the variation of operating variable and modification of the flowheet configuration both. In thi way one can ytematically reduce the pollution indexe even in very complex procee. There are everal robut algorithm uch a imulated annealing (Kirkpatrick et al., 1983; van Laarhoven & Aart, 1987) that can be uccefully ued along with mixed integer programing (Gromann, 1985, 199) here. 9. Concluion A general theory and a methodology for incorporating pollution reduction into chemical proce deign ha been preented. The work i till at an early tage of development particularly with repect to it application. However, the fundamental bae along which future work will proceed have been etablihed. When ued in conjunction with chemical proce imulator, the WAR algorithm offer a powerful methodology for evaluating the potential environmental impact of alternative proce flow heet. Although, the WAR algorithm i intended for ue a part of a good faith effort to reduce the environmental foot print of proce plant, and it doe not obviate the need to make judiciou engineering and environmental deciion. For example, there i no completely unambiguou way of etting value for the impact weighting factor or l. The reaon i that the l repreent the value that ociety place on particular type of environmental impact, and thi will vary acro location, culture, and even time. One hould point out, however, that engineering deign practice ha alway ued human judgement in determining any number of deign parameter like afety factor. and, therefore, thi dilemma i not new. Neverthele, there i a need to further improve the methodology for etimating potential environmental impact and the weighting factor, there i a need to incorporate economic into the analyi, and there i a need to include computer aided proce deign and optimization. Future work will addre thee iue. The cae tudie, however, do illutrate that even in it preent tate the methodology i a ueful proce deign tool. Acknowledgement The author wih to acknowledge the upport of Chemtation, Inc., Houton, Texa under a Cooperative Reearch and Development Agreement with the U.S. Environmental Protection Agency. The author alo thank Dr Dougla Young at the U.S. Environmental Protection Agency for critiquing the manucript. Appendix A. Notation C c C o I yt I in I out I gen (i) I in I gen I out I in I gen I out M j (i) M out M gen M out P p P x kj x kj Potential environmental impact output rate due to non-product, impact h 1 Potential environmental impact input rate due to non-product, impact h 1 Specific potential environmental impact generation from non-product, impact kg 1 Specific output of potential environmental impact due to non-product, impact kg 1 Ma flow rate of tream j, input (i= in) or output (i=out), kg h 1 Vector of ma flowrate for all output tream, kg r h 1 Specific generation of non-product ma, kg kg 1 Specific output of non-product ma, kg kg 1 Ma output rate of product p, kgh 1 Vector of ma flowrate for all product, kg h 1 Ma fraction of chemical k in tream j Ma fraction of non-product k in tream j Vector of ma fraction for non- product component i in all output tream, kg kg 1 Chemical independent relative weighting factor for impact of type l, impact kg 1 Pollution index, kg kg 1 Standard deviation of all chemical core in impact category j, no unit Overall potential environmental impact of chemical j, impact kg 1 x i l n j j Capital cot aociated with a chemical proce, monetary Operating cot aociated with a chemical proce, monetary Potential environmental impact content of ytem, impact Potential environmental impact input rate, impact h 1 Potential environmental impact output rate, impact h 1 Potential environmental impact generation rate, impact h 1 Potential environmental impact flow of tream j input (i=in) or output (i= out), impact h 1 Potential environmental impact generation rate by non-product, impact h 1

12 634 H. Cabeza et al. / Computer and Chemical Engineering 23 (1999) j,l (Score) j, j (Score) j j Reference Specific () potential environmental impact of chemical j for impact of type l Relative potential impact core of chemical i for impact of type j, no unit Arithmetic average of the core for all chemical i in impact category j, no unit Apen Technology. (1997). Apen Plu. Apen Technology, Cambridge, Maachuett. Chemtation. (1997). Chemcad III, Ver Chemtation, Houton, Texa. El-Halwagi, M. M., El-Halwagi, A. M., & Manouiouthaki, V. (1992). Optimal deign of dephenolization network for petroleum-refinery wate. Tran. I. Chem. E., 7b, 131. Fonyo, Z., Kurum, S., & Rippin, D. W. T. (1994). Proce development for wate minimization: the retrofitting problem. Computer and Chemical Engineering, 18, S591. Gromann, I. E. (1985). Mixed-integer programming approach for the ynthei of integrated proce flowheet. Computer and Chemical Engineering, 9, 463. Gromann, I. E. (199). Mixed-integer non-linear programming technique for the ynthei of engineering ytem. Reearch in Engineering Deign, 1, 25. Heijung, R. (Final Ed.), Guinée, J. B., Huppe, G., Lankreijer, R. M., Udo de Hae, H. A. & Wegener Sleewijk, A. (1992). En ironmental life cycle aement of product guide October Centre of Environmental Science, Leiden. Hilaly, A. K., & Sikdar, S. K. (1994). Pollution balance: new methodology for minimizing wate production in manufacturing procee. Journal of the Air and Wate Management Aociation, 44, 133. ICARUS Corporation (1997). ICARUS Software. ICARUS Corporation, Rockville, Maryland. Lapin, L. L. (1975). Statitic Meaning and Method. Harcourt- Brace-Jovanovich, New York. Kirkpatrick, S., Gelatt, C., & Vecchi, M. (1983). Optimization by imulated annealing. Science, 22, 67. Lederman, P. B., & Weaver, J. (1991). Pollution prevention through proce modification: the challenge of the decade. AIChE Annual Meeting, LA, Paper 244a. Mallick, S. K., Cabeza, H., Bare, J. C., & Sikdar, S. K. (1996). A pollution reduction methodology for chemical proce imulator. Indutrial Engineering and Chemitry Reearch, 35, Manouiouthaki, V., & Allen, D. (1995). Proce ynthei for wate minimization. AIChE Sympoium Serie, 34, 72. Peter, M. S., Timmerhou, K. D. (1968). Plant deign and economic for chemical engineer. McGraw-Hill, New York. Richardon Engineering Service. (1997). Proce Plant Contruction Etimating Standard. Richardon Engineering Service, Mea, Arizona. Roiter, A. P., 1995, Wate minimization through proce deign. McGraw-Hill, New York. Van Laarhoven, P. J. M., & Aart, E. H. L. (1987). Simulated annealing: theory and application. Holland: Reidel...

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