Planning an Integrated Petrochemical Industry with ... - ACS Publications

Ind. Eng. Chem. Res. 2001, 40, 2103-2111

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Planning an Integrated Petrochemical Industry with an Environmental Objective G. K. Al-Sharrah, I. Alatiqi, A. Elkamel,* and E. Alper Chemical Engineering Department, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait

Production planning in the petrochemical industry requires a model that can account for the different interactions, needs, and features and provide at the same time suitable mathematical representation. In this work, a model with an environmental objective is presented. The system is formulated as a mixed-integer linear programming model where new value-added products are produced from the basic feedstock chemicals. From the superstructure of the technology alternatives, the optimal set of processes is selected with the objective function of sustainability. The quest for pollution prevention and increased pressure and demand for environmental considerations makes sustainability an important objective function. In this study, sustainability is quantified by a health index of the chemicals and increasing profit represented by processadded value. The model is applied to the case study of planning the development of the Kuwait petrochemical industry. Results give an optimal structure for the development and prove that simple indicators can represent sustainability, giving good results in selecting environmentally friendly processes and at the same time being profitable. Introduction The structure of the petrochemical industry is crosslinked and can be visualized as a network of chemical processes connecting basic feedstock chemicals to the desired final products. The objective of this study is to develop a model that translates the network into mathematical relations and plans for the projected development in the Kuwait petrochemical industry. In the model, it is assumed that the overall industry seeks to utilize its available resources in an optimal way with respect to the criteria of sustainability. Sustainability and how it applies to the petrochemical industry will be discussed in more detail in the next section. Many mathematical programming models have appeared over the years to plan the petrochemical industry. Stadtherr and Rudd1 defined the intermediate chemicals as a network and formulated the behavior of the petrochemical industry as a system of linear equations. The petrochemical industry was viewed as a system of chemical transformations that produce or consume different chemicals. To be general, they assumed that each of the chemicals is potentially a primary input or final product of the industry. Feedstock enters the industry from a limited exogenous supply, largely as byproducts from the energy industries. These feedstocks enter a network of chemical processes that constitute the petrochemical industry and are converted into intermediate and final chemicals. These chemicals then meet the demand for fibers, plastics, elastomers, and hundreds of other petrochemical products. Other different researchers presented variants to the linear programming model of Stadtherr and Rudd with different objective functions. Rudd,2 Al-Fadli et al.,3 and Fathi-Afshar et al.4 selected the minimization of the total production cost, and in other studies, Stokic and Stevancevic5 and Stadtherr and Rudd6 selected minimization of the feedstock consumption. * Correspondingauthor.E-mail: [email protected].

Multiobjective analysis in the modeling of the petrochemical industry was also considered. Fathi-Afshar and Yang7 considered a dual objective of minimization of cost and gross toxicity. Sophos et al.8 considered the minimization of entropy creation (lost work) and feedstock consumption. Optimality in multiobjective programming needs a “compromise strategy”; a solution which minimizes (or maximizes) one objective function will not in general minimize (or maximize) any other objective. To compromise or to reach a tradeoff between different objective functions, Fathi-Afshar and Yang7 and Sophos et al.8 casted all objective functions to a weighted sum, and one scalar objective was found. Fathi-Afshar and Yang7 also used a constrained method, whereby each design objective function was minimized in the presence of the constraint that the other objective cannot exceed a certain limit. Linear programming models showed their ability to identify the technological structure of the petrochemical industry that meets the needs of the economy, natural resources, or environment as well as to test different development scenarios. However, these models must be taken with care because their results may recommend the production of a single chemical using more than one technology or a process with a very low production rate. Using different technologies for one chemical is not recommended for small countries, and building a plant with small production is not economically feasible. To overcome this problem, many researchers cosidered mixed-integer linear programming (MILP) models. A MILP model was proposed by Jimenez et al.9 and Jimemez and Rudd10 to study the Mexican petrochemical industry with a fixed charge operating cost as an objective function. The model selects a process to be installed if the production cost of its product reaches a favorable level with respect to the cost of importing the chemical. The model permits the determination of the economic break-even point, and it can be recursively used to study the impact of different development policies.

10.1021/ie0007466 CCC: $20.00 © 2001 American Chemical Society Published on Web 04/03/2001

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The development of the petrochemical industry in the Kingdom of Saudi Arabia was also studied with a MILP model. Al-Amer et al.11 proposed a MILP model similar to the Jimenez and Rudd model with a small modification in process capacity constraints. The objective function was the maximization of the total annual profit. The sensitivity analysis on the model indicated that it was quite insensitive with respect to the overwhelming majority of given parameters. Thus, the solution can tolerate a wide range of change in selling price, production costs, and supply deficit data. A Mathematical Programming Model with Sustainability as the Objective Over the last 20 years, there has been a very rapid growth in environmentally related legislation affecting the petrochemical industry. Regulations now cover products, air and water quality, waste disposal, soil reclamation, noise abatement, and related matters. Looking ahead a further 20 years, it seems likely that the global petrochemical industry will face a major challenge in responding to the political and social imperative of continuous improvement in environmental performance while, at the same time, ensuring its economic and financial viability. The development of environmentalism in the industry has proceeded along two waves.12 The first wave was building during the 1960s and peaked in 1972 in which the industry responded in a protesting way. Protection of the environment was seen by industry as an extra and unnecessary cost in production. However, regulation regimes were introduced under the principle of “identify-and-repair” followed by a sanctioning approach toward the polluter. In the mid-1970s, the “PolluterPays-Principle” was introduced and broadly accepted by most countries. The second wave was building during the 1970s and began to take shape in the 1980s. This wave focused on the production philosophy that did not destroy the ecological basis to sustain economic development. With the first environmental wave primarily based on the nature declining capacity to provide essential raw materials such as fossil energy, metals, etc., the second wave was primarily concerned with nature’s capacity to absorb the waste from economic development.12 However, this second wave crested in 1987 and introduced the new well-known concept of sustainable development which adopted the “anticipated-and-prevent” approach instead of the old identify-and-repair approach. The quest for pollution prevention and increased pressure and demand for being environmentally friendly and sustainable processes and products have been creating new rules in the process industry. Sustainability is defined as “economic development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs”,13 and within the petrochemical industry, support for the concept of sustainable development is based on14 the following: (a) protection and improvement of the quality of the environment; (b) prudent management of available resources including the development of new, clean, and energy-efficient technology; (c) transition toward a cleaner and more sustainable mix of energy sources and consumption patterns (including a switch from high-carbon to low-carbon fuels).

The highly complex nature of environmental effects makes it difficult to link environmental and design calculations with either sufficient scope or detail. Also, the balance of resource use and benefit is an individual and social judgment and is clearly difficult to quantify.15 Some studies used the sustainable concept in the designing stage of a single process with relatively complex indicators. Young and Cabezas16 used the waste reduction algorithm (WAR), which is simply a tool to be used by design engineers to aid in the evaluation of the environmentally friendliness of a process. This algorithm determines the potential environmental impact (PEI) of a chemical process together with the PEI of the energy consumed within the process. Steffens et al.17 used a multicriteria function to represent the sustainable design. The work used two environmental impact indicators to rank flowsheets: life cycle analysis (LCA) and the sustainable process index (SPI). LCA is a method to identify and quantify the environmental performance of a process from “cradle to grave”. Its main potential in environmental decisionmaking lies in providing a quantitative basis for assessing potential improvement in the environmental performance of a system throughout the life cycle. This life cycle is an environmental cycle because it is mainly related to the wastes in the product life. The SPI does not focus on the impact of pollutant streams leaving the system but instead evaluates the sustainability of the whole system; the basic unit used by the SPI is the amount of area which is required to embed a process sustainability into the environment. The total area is made up of the area for raw materials production, the area required to provide process energy, the area for process installations, the area for staff, and the area required to accommodate products and byproducts (including wastes). More details on calculating SPI areas are available in Steffens et al.17 Environmental life cycle assessment was also used in the studies of Azapagic and Clift.18 The life cycle is represented as a muliobjective linear programming model with the objective functions of economic performance, environmental impact, and total production. The system considered in the study produces five boron products, and the disposal phase of the products is not considered, making this essentially a “cradle-to-gate” study. A cradle-to-gate LCA was done also for the assessment of two alternatives producing dimethyl carbonate in the studies of Aresta and Galatola.19 Culaba and Purvis20 used a simple sustainability index (SI) defined as

SI )

material used initially - recycled material material used initially

The SI is used together with expert systems and LCA to study pulp and paper manufacture. Expert systems operate on the basis of quantitative and qualitative information and may apply heuristic methods (rules based on practical experience). The studies of Dijkmans21 also described a methodology that allows expert judgment in a straightforward and transparent way to select the best available techniques (BAT) from industry alternatives. Another trend in sustainable development studies is the pure qualitative form, where the evaluation is done by questionnaires. A good example is the Green Management Assessment Tool (GMAT), which was a questionnaire on sustainable development and related to

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different levels of integration of environmental management systems into general business processes (see ref 22). It is noticed that an industry can measure the environmental performance in a number of different ways that range from compliance with existing internal standards and applicable regulations, to the amount of emissions, to ecoefficiency, to environmental cost. However, the industry must be allowed to preserve its profitability; otherwise, investment will not occur, and environmental protection will be eroded. Priorities must be established, based on a better understanding of the science, proper analysis of cost, benefits, alternatives, consequences, and any necessary tradeoffs. Key elements of such an approach will be partnership and dialogue between governments, nongovernmental organizations, academia, and the community. In this study, economics (represented by the added value) and health (represented by a health index) were used to represent the sustainability of the planned petrochemical industry. The next section will give more quantitative presentation to sustainability when applied as an objective function.

Let N be the number of chemicals involved in the operation of M processes, Xj the annual level of production of process j, Qi the total amount produced of chemical i, Fi the amount of chemical i as a feedstock, and aij the output coefficient of material i in process j. The main constraints that govern the operation of the petrochemical network are the material balance constraints: M

aijXj ) Qi ∑ j)1

i ) 1, 2, ..., N

(1)

These constraints ensure that the total quantity produced of each material i is equal to the sum of all amounts produced by all processes plus its quantity as a feedstock. For all of the intermediate chemicals, Qi will be set to zero because no output of these chemicals is required from the desired petrochemical network. The final products in the planned petrochemical industry will be governed by their demands in the petrochemical market, according to the country’s share in that market. Constraints on Qi for all final products are needed, and they are formulated as

DiL e Qi e DiU

i ∈ I1

(2)

where Di is the world demand for chemical i and is multiplied by the country’s share in the petrochemical market and L and U represent valid lower and upper limits on the country’s share, respectively. The above constraint is only applied for the set I1 of the final products. Introducing the binary variables Yj for each process j will help in the selection requirement of the planning procedure. Yj will be equal to 1 only if process Xj is selected and zero if process Xj is not selected. Also, if only process j is selected, the production level must be at least equal to the process minimum economic capacity Bj. For each process j, we can write the following constraint:

j ) 1, 2, ..., M

(3)

where K is a valid upper bound. The proposed improvement of Kuwait’s petrochemical industry is directed toward building new plants to produce petrochemicals, so it is logical that only one process should be selected to produce a single chemical. Then we should include the following constraints for intermediate chemicals:

∑Yj e 1

j∈J

(4)

j∈J

(5)

Similarly, for final products:

∑Yj ) 1

where J is the group of processes that produce a single chemical. The supply of feedstock limitations will impose additional constraints on the selection and planning, i.e.

Fi e Si

Model Development

Fi +

BjYj e Xj e KYj

i ∈ I2

(6)

where Si is the supply availability of feed chemical i. The above constraint only applies for the set I2 of some feedstock chemicals. Now we need to define our objective function, which should represent the sustainability concept. The petrochemical industry is facing a major challenge in responding to the political and social imperatives of continuous improvement in environmental performance while, at the same time, ensuring its economic and financial viability. Industry must be allowed to preserve its profitability; otherwise, investment will not occur and environmental protection will be eroded. Our proposed method to quantify the sustainability aspect is to define one objective function that represents the toxicity of the chemicals included in the petrochemical industry. The goals of environmental studies are classified by the mediasair, water, and soilsand by the corresponding effects on humans and ecology. Concentrating on air as the media and the corresponding effects on human health, Fathi-Afshar and Yang7 have selected the chemical threshold limit values (TLV) as an indicator for the health objective function and applied them only for the final products. Chemical 1 is considered to be more harmful than chemical 2 if TLV1 < TLV2, so the index is represented as 1/TLV. The threshold limit may give inaccurate relative toxicities of some chemicals; for example, the TLV for acrylonitrile is 2 ppm, while that for hydrogen peroxide is 1 ppm. So, according to their TLVs, hydrogen peroxide is relatively more toxic than acrylonitrile, which is not correct because acrylonitrile is one of the “top 100” hazardous substances.23 Also, the overall effect of all chemicals in the industry should be considered as they are handled in the operation, so another approach for defining the health objective function should be used. The National Fire Protection Association (NFPA)24 has developed a system for indicating the health, flammability, and reactivity hazards of chemicals. The system is based on giving a number (from 0 to 4) to the chemical, indicating its effect. For the objective function, the health index Hi from the NFPA is used for every

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chemical i with the following ratings: Hi ) 4 Hi ) 3 Hi ) 2 Hi ) 1 Hi ) 0

danger: may be fatal on short exposure; specialized protective equipment required warning: corrosive or toxic; avoid skin contact or inhalation warning: may be harmful if inhaled or absorbed caution: may be irritating no usual hazard

The toxicity objective function can be formulated as M N

min Z1 )

∑ ∑aijHiXj j)1 i)1

(7)

The other objective function that needs to be governing the results is a maximum economical gain in the selected processes. The economical profit in our model is represented by the added value; it is the price of products minus the cost of the feedstock for each process. If Ci is the price of chemical i, the added-value objective function will be represented by M N

max Z2 )

∑j ∑i aijCiXj

(8)

The selected objective functions to represent the sustainability aspect need simple indicators. The health index and the prices are simple and available for all of the chemicals in the petrochemical network. This complies with the requirements that the sustainable development indicators should be as small as possible and as large as necessary. Complex indicators are extremely difficult to apply for petrochemical networks because they consist of many processes and chemicals. The two components of the objective function were first normalized before being used in the optimization model. Illustrative Case Study Kuwaiti officials have expressed interest in accelerating the development of the country’s relatively small petrochemical industry. This would accomplish several goals: boosting the value of Kuwait’s crude oil reserves, helping to protect Kuwait’s revenues during periods of low crude prices, and boosting Kuwait’s revenues while adhering to OPEC crude oil quota limitation. The desired final products were defined under the criteria of their importance to the global petrochemical and its relevance to Kuwait. The proposed final products are acetic acid, vinyl acetate monomer, polystyrene, poly(vinyl chloride), acrylonitrile-butadiene-styrene (ABS), cumene, phenol, acetone, and acrylic fiber. Most of the proposed final products are plastic materials and synthesis resins or are used for the manufacturing of plastics and resins. Plastic materials and synthesis resins make up one of the newer but largest and most dynamic parts of the chemical industry. Starting with the billiard ball and the celluloid collar before the turn of the last century, these materials have mushroomed since the mid-1940s into a worldwide, multibillion-dollar industry.25 Kuwait petrochemical industries are mainly exportoriented because of the small, though growing, regional markets. With the implementation of the Gulf Cooperation Council (GCC) economic agreement, different GCC petrochemicals will be freely traded within the region;

the remainder will be marketed internationally. Kuwait, however, has had a little experience in marketing petrochemicals. Some observers26 believe that GCC countries, including Kuwait, will have great difficulties in marketing their products, for world over capacity in petrochemicals production is expected to characterize the market. Kuwait is classified among countries with abundant raw materials but limited markets.25 Also there is an increasing possibility that the industrial world will impose trade barriers to limit foreign competition. The involvement of large multinational chemical companies in Kuwait joint-venture petrochemical projects may assist in the marketing aspect. The routes from the final products to the basic feedstock chemicals were determined by selecting a number of manufacturing processes forming at the end a network of 83 processes linking the production and consumption of 65 chemicals. The selection of the chemicals came as an output of taking all possible alternatives of producing the desired products. Some processes included in the model may be old or not used in the current industry, but they were included to give a general model of the industry. Aresta and Galatola19 studied two alternatives for the environmental assessment of synthetic processes, with one not applied at the industrial level. Their justification was that, although the process is not implemented on an industrial scale, it is fascinating from the environmental point of view and can be extended to other feedstocks. So, in the model proposed, all process alternatives were taken into consideration. A simplified network of the process is illustrated in Figure 1. The chemicals that constitute the model are listed, numbered, and classified according to their potential function in Table 1. The potential function of a chemical is determined by assuming primary raw (PR) material, secondary raw (SR) material, intermediate (I), primary final (PF) product, and secondary final (SF) product. PR materials are chemicals derived from petroleum and natural gas and form the basic feedstock of the process, whereas the SR materials represent chemicals that are needed as additives or needed in small quantities. The I chemicals are the chemicals that are produced and then consumed in the petrochemical network. Finally, the PF and SF products are also classified as primary and secondary. The primary products are the selected final products produced for the country’s development, and the secondary products are byproducts associated with the processes in the network. Altogether, there are 65 chemicals included in the petrochemical model. Of these, 17 are only SR materials and SF products, which do not take part in material balance constraints. There are 24 I, being both produced and consumed by the model, and 13 end products (PF products). Primary feedstocks consist of 10 chemicals, of which 3 have limited supplies. The heart of the model is the material balance constraints. Hence, estimation of the output coefficient aij is a key step in constructing the model. For this purpose, yield data for each chemical transformation are required. In many cases, process yields are variable and depend on what product mix is desired or on what capital expenditure can be afforded. The model uses the average yields reported at commercial installations. Needed to complete the construction of the model constraint set are the supply of feedstock, demand of final products, and minimum capacity data. Because the


Figure 1. Simplified network of processes in the model.

industry must compete for its feedstock and markets, supply and demand data were taken from different sources (mainly from recent SRI reports27 and Kuwait’s Petrochemical Industries Company (PIC) annual reports). However, the supply and demand data are estimated using observed values of production and observed usage patterns. Table 2 shows the supply and demand data used in the model. Note that only three basic feedstock chemicals were assigned with a limited supply. These are naphtha, ammonia, and chlorine. Other feedstocks like methane and ethane are available in sufficient quantities in Kuwait.

The data needed for the objective function in the model are prices of all chemicals and their health indexes. The prices were taken from recent SRI reports, and the health indexes were obtained from the NFPA standards.24 Table 3 gives a comprehensive list of the objective function data. Model Solution The model was solved using the commercial optimization package GAMS.28 The solution indicated the selection of 23 processes out of the 83 processes that form

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Table 1. List of Chemicals Included in the Modela chemical

function

acetaldehyde acetic acid acetone acetylene acrylic fibers acrylonitrile acrylonitrile-butadiene styrene ammonia benzene butadiene butenes (mixed n-, iso-, -dienes, etc.) C-4 fraction (mixed butanes, -enes, etc.) carbon dioxide carbon monoxide chlorine chlorobenzene coke cumene ethane ethanol ethylbenzene ethylene ethylene dichloride formic acid fuel gas fuel oil gas oil gasoline hydrochloric acid hydrogen hydrogen chloride hydrogen cyanide hydrogen peroxide isopropyl alcohol

SF + I I + PF PF I PF I PF PR SF + I I SF + PR SF + PR SR I PR I PR I + PF PR I I SF + I I SF SF SF + PR PR SF SR SR + SF SR + SF I I I

chemical

function

methane methanol methyl acrylate methyl methacrylate naphtha n-butane n-butylenes (1- and 2-) pentane phenol polybutadiene rubber polystyrene (crystal grade) polystyrene (expandable beads) polystyrene (impact grade)

PR + SF I SR SR PR PR PR SR PF SR I + PF PF PF

poly(vinyl acetate) poly(vinyl alcohol) poly(vinyl chloride) propane propylene (chemical grade)

I SR PF SF + PR SF + I

propylene (refinery grade)

PR

propylene oxide sodium carbonate sodium hydroxide styrene sulfuric acid sulfur synthesis gas of 3:1 synthesis gas of 2:1 toluene vinyl acetate vinyl chloride xylene (mixed)

SF SR SR I I PR I SF PR + SF I + PF I SR + SF

a Also indicated is the potential function of each chemical: PR ) primary raw material, SR ) secondary raw material, I ) intermediate, PF ) primary final product, SF ) secondary final product.

Table 2. Supply and Demand Data feedstock chemical

production rate (1000 tons/year)

final product chemical

ammonia chlorine naphtha

575.765 16.371 2500

ABS acetic acid acetone acrylic fiber cumene phenol polystyrene (crystal grade) polystyrene (expandable beads) polystyrene (impact grade) poly(vinyl chloride) vinyl acetate

demand (1000 tons/ year) 60 37.888 28.255 41 72.165 61.4 21.36 17.151 77.09 68.039 15.276

the petrochemical network. The selected processes and their respective capacities are shown in Table 4, and the corresponding petrochemical network is shown in Figure 2. It is noticed from the results that heavy feedstocks such as naphtha are not selected. Chlorine is also not selected. The model did not recommend the production of carbon monoxide, chlorobenzene, ethanol, ethylene dichloride, hydrogen peroxide, methanol, sulfuric acid, synthesis gas of 3:1, and poly(vinyl acetate). Another very important notice on the model solution is that the model recommended the production of acrylonitrile using cyanation/oxidation of ethylene. This process needs hydrogen cyanide and hydrogen chloride as the

main feeds, and as a result hydrogen cyanide was selected also as an intermediate chemical. The other alternative for the production of acrylonitrile is the ammoxidation of propylene with ammonia and sulfuric acid as feeds. It is clear that the selected process leads to a path in the network that produces a very dangerous chemical and cannot be considered as a good choice for the development. To correct this situation, the process of producing acrylonitrile by cyanation/oxidation of propylene was removed, and the model was solved again. The solution was infeasible, meaning that no solution was found that satisfies the given constraints and/or type of variables. After inspection of the model, it was found that the infeasibility was due to the fact that if the ammoxidation of propylene was selected, sulfuric acid was needed as an intermediate chemical with a required production rate of less than the minimum economic production rate. The previous notice on the solution gives an indication that the production of acrylonitrile and any related chemical should be reconsidered for the development in Kuwait or that sulfuric acid needed should be imported. The model has proved to be flexible for studying any modification; this encouraged another test on the model’s solution. The production of vinyl chloride by the hydrochlorination of acetylene was considered to be an old technology; when it was removed from the model, another similar optimal solution was found. In the new solution, vinyl chloride was produced by dehyrochlorination of ethylene dichloride and, as a consequence, ethylene was the intermediate chemical instead of acetylene.

Ind. Eng. Chem. Res., Vol. 40, No. 9, 2001 2109 Table 3. Health Index and Price Data chemical i

Hi

Pi ($/ton)

chemical i

Hi

Pi ($/ton)

1. acetaldehyde 2. acetic acid 3. acetone 4. acetylene 5. acrylic fibers 6. acrylonitrile 7. ABS 8. ammonia 9. benzene 10. butadiene 11. butenes (mixed, n-, iso-dienes, etc.) 12. C-4 fraction (mixed butanes, -enes, etc.) 13. carbon dioxide 14. carbon monoxide 15. chlorine 16. chlorobenzene 17. coke 18. cumene 19. ethane 20. ethanol 21. ethylbenzene 22. ethylene 23. ethylene dichloride 24. formic acid 25. fuel gas 26. fuel oil 27. gas oil 28. gasoline 29. hydrochloric acid 30. hydrogen 31. hydrogen chloride 32. hydrogen cyanide 33. hydrogen peroxide

3 3 1 0 0 4 0 3 2 2 1 1 0 3 4 2 0 2 1 0 2 1 2 3 1 0 0 1 3 0 3 4 2

657 644 443 1820 744 822 2300 152 407 322 869 179 104 17.6 104 514 727 507 147 631 547 461 282 666 109 103 188 212 485 575 172 507 1330

34. isopropyl alcohol 35. methane 36. methanol 37. methyl acrylate 38. methyl methacrylate 39. naphtha 40. n-butane 41. n-butylenes (1- and 2-) 42. pentane 43. phenol 44. polybutadiene rubber 45. polystyrene (crystal grade) 46. polystyrene (expandable beads) 47. polystyrene (impact grade) 48. poly(vinyl acetate) 49. poly(vinyl alcohol) 50. poly(vinyl chloride) 51. propane 52. propylene (chemical grade) 53. propylene (refinery grade) 54. propylene oxide 55. sodium carbonate 56. sodium hydroxide 57. styrene 58. sulfuric acid 59. sulfur 60. synthesis gas of 3:1 61. synthesis gas of 2:1 62. toluene 63. vinyl acetate 64. vinyl chloride 65. xylene (mixed)

1 1 1 3 2 1 1 1 1 4 0 0 0 0 0 0 0 1 1 1 3 0 3 2 3 2 2 3 2 2 2 2

527 134 113 1450 1910 203 185 245 465 688 1900 1100 1650 1150 1060 3040 789 172 381 280 1020 192 368 697 60 27.9 80 80 321 1020 500 292

Table 4. Model Optimal Solution process selected

capacity (103 tons/year)

acetaldehyde by the two-step oxidation from ethylene acetic acid by air oxidation of acetaldehyde acetone by the dehydrogenation of 2-propanol acetylene by the pyrolysis of ethane (regenerative process) acrylic fibers by the batch suspension polymerization acrylonitrile by the cyanation/oxidation of ethylene ABS by suspention/emulsion polymerization benzene by the disproportion of toluene butadiene by extractive distillation cumene by the reaction of benzene and propylene ethylbenzene by the alkylation of benzene ethylene by the pyrolysis of ethane hydrogen cyanide by the ammoxidation of methane 2-propanol by the hydration of propylene phenol by the sulfonation of benzene polystyrene (crystal grade) by bulk polymerization polystyrene (expandable beads) by suspension polymerization polystyrene (impact grade) by suspension polymerization poly(vinyl chloride) by bulk polymerization propylene (chemical grade) from propylene refinery grade styrene from ethylene by hydroperoxide process vinyl acetate by the reaction of ethylene and acetic acid vinyl chloride by the hydrochlorination of acetylene

227.657 291.868 151.552 119.953 113.02 132.878 164 882.149 40.18 228.66 617.473 451.245 79.727 167.465 245.6 88.87 68.604 308.36 272.156 398.837 542.119 73.676 278.96

Comparison between Single-Objective and Multiobjective Functions To examine the selection criteria, the model was solved using a single-objective function. Both health and added-value objective functions were tested with the model separately. The results indicated in Figure 3 show that the processes selected by the health objective, added-value objective, and multiobjective are different. Based on Figure 3, 10 processes, out of the 22 selected, are shared by both objectives, which is about 45% of all of the processes accepted by either objective. However, by examining the situation more closely, one can observe that 5 out of the 10 processes have been selected merely because of the lack of alternatives. After eliminating 5 such cases, there remain only 5 processes which are favored by both objectives. These observations, however, suggest that if an environmentally safer industry is desired, about 70% of the process (with alternative

routes) which has been proven to be cost efficient will have to be abandoned. All of the processes favored by the two objectives were selected also by the multiobjective function. This proves the validity of the multiobjective function with its simple computation. Concluding Remarks A mixed-integer programming model for the development of the petrochemical industry has been formulated with an objective defined by sustainability and quantified by added value and the health index. Input data to the model included a list of petrochemicals, their production technologies, the corresponding minimum economical production rates, supply demand data, chemicals prices, and health indexes. The model was illustrated in the case of the Kuwait petrochemical industry. The model recommended some relatively profitable and environmentally friendly petrochemical

2110


Figure 2. Optimal development in Kuwait’s petrochemical industry.

products to be manufactured in Kuwait as intermediate chemicals to produce the desired final products. The recommended intermediate products include benzene, ethylbenzene, styrene, acetylene, vinyl chloride, propylene, ethylene, acetaldehyde, acetylene, acetic acid, butadiene, acrylonitrile-2-propanol, and hydrogen cyanide. The final products include acetic acid, acetone, acrylic fiber, ABS, cumene, phenol, polystyrene, poly(vinyl chloride), and vinyl acetate monomer. Also, as a result of the planning problem solution, the following improvements for Kuwait’s petrochemical industries are highly recommended: (a) light feedstocks should be used like propane and ethane instead of naphtha; (b) processes that use chlorine as a feedstock should be avoided; (c) an intermediate chemical can be imported if its production in the country is not economically recommended. The two objective functions representing sustainabilitysthe added value and the health indexshave been used with simple indexes, and they have proved to be efficient in modeling and planning. Also, the multiobjective treatment of the model provides the design engineer and industrial planner a powerful mathematical tool for designing and operating large-

scale chemical processing systems which are sound economically and at the same time responsive to the environmental constraints. There are several ways to develop and modify this study further. There is an assumption in the model that the prices taken for the chemicals will stay constant during the next development period. Obviously, in a world of uncertainty and change, it is not prudent to rely completely on the deterministic and constant value of any parameter. Thus, sensitivity analysis is required to find out how the solution would react to changes in parameters. The basic question is how much we can change each given value without affecting the current solution. Further modification from a mathematical point of view can be to add another part for the objective function, which is energy consumption of the processes. Energy consumption is a very important aspect in selecting industrial processes. Such development requires a great deal of accurate data. Acknowledgment The authors thank Kuwait University graduate school for funding this work.


Figure 3. Comparison between single-objective and multiobjective function solutions: (O) process selected by the health objective function; (0) process selected by the added-value objective function; (s) process selected by the multiobjective function.

Literature Cited (1) Stadtherr, M. A.; Rudd, D. F. Systems Study of the Petrochemical Industry. Chem. Eng. Sci. 1976, 31, 1019-1028. (2) Rudd, D. F. Modeling the Development of the International Chemical Industry. Chem. Eng. J. 1975, 9, 1-20. (3) Al-Fadli, A. M.; Soliman, M. A.; Wagialla, K. M.; Al-Mutaz, I. S. Optimal Resource Allocation and Processing for the Saudi Petrochemical Industry. Process Econ. Int. 1988, 7, 22-29. (4) Fathi-Afshar, S.; Maisel, D. S.; Rudd, D. F.; Terevino, A. A.; Yuan, W. W. Advances in Petrochemical Technology Assessment. Chem. Eng. Sci. 1981, 36 (No. 9), 1487-1511. (5) Sokic, M.; Stevancevic, D. The Optimal Structure of the Petrochemical Industry. Chem. Eng. Sci. 1983, 38 (No. 2), 265273. (6) Stadtherr, M. A.; Rudd, D. F. Resource Use by the Petrochemical Industry. Chem. Eng. Sci. 1978, 33, 923-933. (7) Fathi-Afshar, S.; Yang, J. Designing the Optimal Structure of the Petrochemical Industry for Minimum Cost and Least Gross Toxicity of Chemical Production. Chem. Eng. Sci. 1985, 40 (No. 5), 781-797. (8) Sophos, A.; Rotstein, E.; Stephanopoulos, G. Multiobjective Analysis in Modeling the Petrochemical Industry. Chem. Eng. Sci. 1980, 35, 2415-2426. (9) Jimenez, A.; Rudd, D. F.; Meyer, R. R. Study of the Development of Mexican Petrochemical Industry using MixedInteger Programming. Comput. Chem. Eng. 1982, 6 (No. 3), 219229.

(10) Jimenez, A.; Rudd, D. F. Use of a Recursive Mixed-Integer Programming Model to Detect an Optimal Integration Sequence for the Mexican Petrochemical Industry. Comput. Chem. Eng. 1987, 11 (No. 3), 291-301. (11) Al-Amir, A. M. J.; Al-Fares, H.; Rahman, F. Towards Modeling the Development of the Petrochemical Industry in the Kingdom of Saudi Arabia. Arabian J. Sci. Eng. 1998, 23 (No. 18), 113-126. (12) Ulhoi, J. P. Merging Economic Sense and Ecological Sensibility: Environmental-Driven Response to Corporate Change. Strategic Change 1998, 7, 31-41. (13) Engel, J. R., Engel, J. G., Eds. Ethics of Environmental Development: Global Challenge, International Response; University of Arizona Press: Tucson, AZ, 1990. (14) Kohlhase, K. R. The Oil Industry after Rio. Proceedings of the Fourteenth World Petroleum Congress; John Wiley & Sons: New York, 1994. (15) Sharrat, P. Environmental Criteria in Design. Comput. Chem. Eng. 1999, 23, 1469-1475. (16) Young, D. M.; Cabezas, H. Designing Sustainable Process with Simulation: The Waste Reduction (WAR) Algorithm. Comput. Chem. Eng. 1999, 23, 1477-1491. (17) Steffens, M. A.; Fraga, E. S.; Bugle, I. D. L. Multicriteria Process Synthesis for Generating Sustainable and Economic Bioprocess. Comput. Chem. Eng. 1999, 23, 1455-1467. (18) Azapagic, A.; Clift, R. Life Cycle Assessment and Muliobjective Optimization. J. Cleaner Prod. 1999, 7, 135-143. (19) Aresta, M.; Galatola, M. Life Cycle Analysis Applied to the Assessment of Environmental Impact of Alternative Synthesis Processes. The Dimethicarbonate Case: Part 1. J. Cleaner Prod. 1999, 7, 181-193. (20) Culaba, A. B.; Purvis, M. R. I. A Methodology for the Life Cycle and Sustainability Analysis of Manufacturing Processes. J. Cleaner Prod. 1999, 7, 435-455. (21) Dijkmans, R. Methodology for Selection of Best Available Technology (BAT) at the Sector Level. J. Cleaner Prod. 2000, 8, 11-21. (22) Eagan, P. D.; Joeres, E. Development of Facility-Based Environmental Performance Indicators Related to Sustainable Development. J. Cleaner Prod. 1997, 5, 269-278. (23) Sitting, M., Ed. Handbook of Toxic Hazardous Chemicals and Carcinogens, 3rd ed.; Noyes Publications: Park Ridge, NJ, 1990. (24) NFPA Standard 49, Hazardous Chemical Data, 1994. (25) Farouq Ali, S. M. Strategies for Petroleum Conservation and Utilization for the Production of Petrochemicals and LPG. Proceedings of the Conference on Chemistry and Technology of Petroleum, Kuwait, 1983. (26) Al-Sabban, M. S. Viability of Industrial Integration within the Gulf Cooperation Council: The case of Petrochemical Industries. Ph.D. Thesis, University of Colorado, Boulder, CO, 1983. (27) SRI PEP Report, 1992. (28) Brooke, A.; Kendrik, D.; Meeraus, A. GAMS User’s Guide; Boyd & Fraser Publishing Co.: Danvers, MA, 1992.

Received for review August 14, 2000 Accepted January 24, 2001 IE0007466

Planning an Integrated Petrochemical Industry with ... - ACS Publications

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