Systematic Framework for Environmentally Conscious Chemical

Ind. Eng. Chem. Res. 2004, 43, 535-552

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PROCESS DESIGN AND CONTROL Systematic Framework for Environmentally Conscious Chemical Process Design: Early and Detailed Design Stages Hui Chen and David R. Shonnard* Department of Chemical Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, Michigan 49931

This paper presents a systematic and hierarchical approach for incorporating environmental considerations into all stages of chemical process design. The complexity of the environmental and economic assessments increases as the design proceeds. The design method integrates computer-aided process simulation with a suite of environmental impact, economic, and decision analysis software tools. At the earliest design stage, the environmental assessment includes emission estimates from major process equipment, considers pollution control efficiency, and generates nine risk-based environmental impact indices. The economic assessment is based on the cost of raw materials and reaction stoichiometry. This assessment method was applied to the selection of alternative feedstocks for a reaction step: benzene versus n-butane for maleic anhydride (MA) production. Using known yields and selectivities, the n-butane route was shown to be superior to the benzene route in both the raw material cost and environmental impacts. After synthesis of a process flowsheet and simulation, a series of “process diagnostic summary” tables was generated to identify early process improvement strategies. An “improved” basecase flowsheet was developed, simulated, and assessed for profitability and environmental impacts. The improved n-butane process flowsheet after implementation of heat integration yielded an 80% reduction in utility costs and a 13% reduction in the aggregate environmental index compared to the base-case flowsheet. Then, several design tasks, such as equipment sizing, scaled gradient analysis, and multiobjective optimization using a genetic algorithm, were applied. After optimization, the economic performance of both the n-butane and benzene flowsheets was substantially bettered over the previously improved base-case flowsheets. The aggregate environmental index for the n-butane process was decreased to the greatest extent as a result of early design activities and negligibly as a result of detailed design and optimization. The opposite was observed for the benzene process. The reasons for this difference in system behavior are explained. The early screening environmental and economic assessment methods were shown to be accurate when compared to the more rigorous assessment methods. Optimum operating conditions of the processes (reaction temperature and pressure, mass separating agent flow rate, etc.) change significantly when the objective function for optimization is either an aggregate environmental index or a profitability index. 1. Introduction Process design is performed when implementing new technologies, when creating new facilities, or when retrofitting existing processes. It normally involves several design activities performed in sequence, from data gathering, process synthesis, and base-case development, to detailed design and optimization.1 The traditional design approach incorporates economic objectives. Recently, however, process systems design and analysis has come to include more performance measures in the design activities, such as safety, environment, controllability, and flexibility. An environmentally conscious design (ECD) of a chemical process should have a number of defining characteristics. First, the purpose of the design should * To whom correspondence should be addressed. E-mail: [email protected]. Tel.: 906-487-3468.

be to improve the environmental performance of the process, even to the extent that regulatory compliance is exceeded. The environmental assessment should include multiple categories of impacts to provide the engineer with a broad view of the environmental effects. Realistic pollution generation and release scenarios should be used, and when available, measured data should form the basis for the environmental assessment. Computer-aided design (CAD) tools should also be used to facilitate the design evaluation. Environmental assessment should be conducted in a tiered fashion, starting with a simplified screening method in early design and progressing to a more rigorous method during detailed design. Finally, the environmental assessment should always be conducted in parallel with the economic evaluation and might include other performance measures. The framework presented in this paper demonstrates how to include these defining

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Ind. Eng. Chem. Res., Vol. 43, No. 2, 2004

characteristics in the ECD of chemical processes for both early and detailed design. A chemical product might be produced by several reaction routes and processed through various types of equipment. Most design projects employ some screening steps to eliminate unfavorable choices early in the effort. The importance of early design activities has been addressed in several recent studies. Sharratt2 mentioned that considering environmental improvement at early design stages provides great opportunities for identifying clean processes, whereas modifications made at later design stages have limited potential for improving environmental performance. Heinzle et al.3 also mentioned that most of the economic expense and environmental burden are determined in early design stages. Some attempts have been made to incorporate environmental concerns in early design. One of the earliest approaches conducted qualitative analyses such as environmental reviews by a 10-step procedure,4 a question list or checklist,5 or a hierarchical decision procedure.6 Another qualitative approach applies a knowledgebased expert system7 and presents a material-centric methodology embedded within an expert system to integrate safety and environmental considerations in early design. These approaches address environmental issues and identify opportunities for waste reduction. However, because each lacks quantitative measures, they are limited when it comes to comparing alternatives. Other approaches used to evaluate environmental impacts during early design are more quantitative. For example, a variety of environmental indicators have been used in the environmental assessment of chemical processes, from simple mass-based metrics to more complicated methods based on multiple media, multiple exposure pathways, and multiple categories of impacts.8 During the early stages of design, simple evaluation methods are often preferred, such as mass-based indices and toxicity-weighted mass indices.9 Hoffmann et al.10 developed a systematic methodology to generate and screen process alternatives on the basis of economic and environmental performance in the early development stages. The process alternatives are evaluated using total annualized profit per service as the economic indicator and material intensity per service (MIPS) as the environmental indicator. The results are organized using Pareto plots. However, in this approach, MIPS is an input-based index and not necessarily related to the output of environmental impacts from the process. As these authors suggested, additional analyses should be performed to account for the toxicity of releases. Allen and Shonnard11 suggested using toxicity and stoichiometric coefficients of reactions to approximate toxicity impacts for reaction pathway and raw materials selection. Heinzle et al.3 proposed a general methodology for ecological and economic assessment to accompany early process design. Three types of screening indices are used in this procedure based on simple mass balances, including mass-loss indices (MLIs), environmental indices that are generated by combining MLIs and ecological weightings, and economic indices that are generated by combining MLIs and economic weightings. The set of early design environmental impact assessment methods discussed above has characteristics that limit their applicability and accuracy. First, the environmental impacts of the design are caused by the

actual emissions from the process and not necessarily the flow of material through the process. Not all of the input materials pose impacts to the environment because such materials are often consumed in the reactions. Similarly, not all of the outputs from the process are released directly to the environment and cause environmental damage. Therefore, emission-based environmental indicators are more accurate in representing actual impacts. Some of these previously described methods use toxicity-weighted mass indicators to quantify impact potentials of the process while neglecting the effects of environmental fate. In addition, different chemicals can cause different kinds of impacts. For example, CO2 contributes to global warming, whereas NOx contributes to acid rain, and a given chemical often contributes to several categories of environmental impacts. The early design environmental impact assessment approach used in this paper is meant to improve on those methods discussed above because multiple impact categories are used, process emission estimates are incorporated, and the effects of environmental fate and transport are included. In addition, because software is used to calculate environmental indices, data availability and computational effort are not limitations. As part of this study, it will also be shown that the early screening economic and environmental assessments are accurate when compared to later detailed assessments. Detailed design tasks involve process simulation, evaluation, and optimization of a process flowsheet. In industrial practice, the detailed design is often performed after the layout of the flowsheet is fixed. The size of the equipment is then determined by the desired capacity, and then the operating conditions are optimized. Whereas early design stages focus on selecting materials, determining reaction pathways, and establishing the process flowsheet structure, detailed design seeks the optimum in equipment size and operating conditions to further reduce costs, waste production, and pollution release from the process. In detailed design, optimization is a most important step. Some approaches reported in the literature take economic incentives as objectives in optimization while requiring the satisfaction of environmental constraints.12-15 More recent approaches treat environmental requirements as objectives together with other objectives, that is, multiobjective optimization.8,16-21 Several systematic methodologies are available for detailed characterization of the environmental impacts of chemicals, products, and processes. These methods include life cycle assessment (LCA),22 the waste reduction (WAR) algorithm,23 the methodology for environmental impact minimization (MEIM),24 and the environmental fate and risk assessment tool (EFRAT).25 All of these methodologies have been incorporated into the design and optimization of chemical processes. It is often difficult to find an optimum for a process that satisfies both economic and environmental objectives. Normally, a range of optimal solutions is obtained, which forms a noninferior set. Identifying the optimum from this noninferior set is subjective, depending on the preference of decision makers.8 Techniques for identifying the set of optimal alternatives can be classified as: (1) weighting method approaches, such as simple additive weighting, weighted product, median ranking method,26 analytic hierarchy process,27 multiattribute utility theory,28 and simple multiattribute rating tech-

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Figure 1. Framework for environmentally conscious process design.

nique,29 and (2) nonweighting methods, such as goal programming and the -constraint technique.8 Both of these types of approaches have been applied to process design problems with both economic and environmental objectives.17,24,30,31 The advantage of a method without weighting is that “it does not require a priori articulation of preferences, so that the whole noninferior set of solutions can be explored”.32 However, such methods require the generation of all alternatives before a decision can be made. In weighting methods, decision makers assign weights to different objectives to transform the problem into single objective optimization problem. Thereafter, automated search algorithms can be applied to minimize this single objective function. In the preceding discussion, some methodologies were reviewed for detailed design and optimization of environmentally conscious chemical processes. However, there is a lack of design guidance and heuristics to assist environmentally conscious process design. Such questions as whether early or later detailed design tasks are more effective in reducing environmental impacts and improving profitability, which categories of economic and environmental impacts should dominate the assessment and why, and how accurate the screening economic and environmental assessments are and how can they can be improved are important design issues to address in ECD research. This paper presents a systematic framework for conducting the ECD of chemical processes and applications of software tools for their evaluation at several stages in the design task. A case study is presented of maleic anhydride production using either n-butane or benzene as the feedstock. Several environmentally conscious design guidance results and heuristics are obtained from the analysis of this case study. 2. Framework for Environmentally Conscious Process Design The ECD framework proceeds in several steps as shown in Figure 1. The goal of the early design tasks is to screen a large number of reaction pathways and raw

materials, to simulate a base-case flowsheet, and then to create an improved flowsheet. In detailed design, the goal is to optimize a small number of flowsheets starting with the improved base-case flowsheet for each remaining process. 2.1. Initial Design Task. The first step in the framework of Figure 1 is to understand the design task, gather information, and propose possible alternatives. For example, in the manufacturing of a specific chemical, literature and patents are searched for possible reaction pathways and transformation technologies. Data are collected; for example, thermodynamic and kinetic data can be found from journal articles, patents, or handbooks, and current chemical prices can be obtained from market reports (e.g., Chemical Market Reporter). In addition to those sources, some data related to quantification of environmental impacts are also collected so that an impact assessment can be performed in subsequent design steps. 2.2. Process Synthesis and Development of a Base-Case Flowsheet. In the second step, many design alternatives are generated. Various methodologies are available for generating design alternatives, such as use of technical documents, hierarchical design approaches, mathematical programming, and expert systems.8 A common method for generating design alternatives is to search patents and the technical literature for possible reaction routes, raw materials, process equipment, and pollution prevention strategies. Screening-level economic and environmental assessments are performed to assist in the selection among these alternatives. The screening economic indicator used in this paper is the total raw material cost, which is often the dominant cost element in a process. As shown in the case study in section 3, the screening economic assessment uses reaction stoichiometry and raw material price to generate the economic metric. Unlike the economic evaluation, which is different at the early design stages compared to detailed stages, the environmental impact characterization method is the

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same at all design stages. However, only the emissions from major pieces of equipment are estimated at the early design stages. As shown in the case study in section 3, the EFRAT method is applied in this hierarchical design methodology. The details of this method are provided in section 2.9. 2.3. Process Simulation. The third step is to simulate the base-case flowsheet for each remaining alternative. The operating conditions for each piece of equipment and input stream are specified, including temperature, pressure, and composition. Economicbased design heuristics, such as those found in design textbooks,1,33 guide the specification of the major pieces of equipment and their initial operating conditions. The steady-state material and energy balances are then obtained. 2.4. Process Diagnostic Summary Tables. Process diagnostic summary (PDS) tables are then created on the basis of the simulation and evaluation of the basecase flowsheet to obtain an overview of the process operation and to identify early process improvement opportunities. The economic and environmental assessments are conducted using SCENE software (section 2.9) “linked” with the process simulation software, HYSYS. These summary tables are described next. 2.4.1. Mass Input/Output Table. A mass input/ output analysis is performed on a base-case flowsheet to generate the process stream information, and an input/output table is generated. The objectives for creating this table are to verify the material balance calculations of the entire process and to identify the magnitudes of raw material streams being processed. 2.4.2. Energy Input/Output Table. The energy input/output table is developed according to the first law of thermodynamics. The motivations for making this table are to identify the amount of energy consumed in the process and to seek opportunities to reduce energy consumption by applying heat integration. 2.4.3. Utility Annual Expense Summary. This summary presents the expense of each type of utility for each piece of equipment in the process. Examples are reboilers, compressors, pumps, and condensers. This summary defines the sources of major utility expense. 2.4.4. Manufacturing Revenue and Expense Statement. This analysis presents an overview of economic information for the entire process (e.g., sales revenue, raw material cost, utility cost, etc.). It also allows the designer to see the main contributors to the economic performance, for example, which process units, inputs, and outputs are the major revenues and expenses in the process. Equipment costs are not included at this stage. 2.4.5. Environmental Impact Summary. The environmental impact summary quantifies the emission and impact of each chemical from each piece of equipment. This table enables the engineer to identify the major emission sources and major pollutants in the process. It is utilized to seek opportunities to reduce the environmental impacts by process modifications, material substitution, or pollution control. Overall, PDS helps to identify early opportunities for process improvement and environmental impact reduction. 2.5. Development of an Improved Base-Case Flowsheet. Process modifications identified from these PDS tables are then applied to the process, such as heat or mass integration, pollution prevention, or pollution

control. The process flowsheet is modified to accommodate these improvements, and a modified base case is simulated. This modified base-case flowsheet is then evaluated and optimized in the detailed design stages, covered in the following sections. 2.6. Equipment Sizing and Economic and Environmental Assessments. Once the modified base-case flowsheet is established, each piece of equipment is sized. Equipment sizing routines were obtained for this work from a number of standard references.1,33,35 The economic assessment here applies a more comprehensive indicator considering not only raw material cost (as in the early design) and utility costs (as in the PDS), but also capital costs of equipment. The environmental assessment method is the same as that applied in early design stages, but it includes all emissions of chemicals from every source in the process. 2.7. Scaled Gradient Analysis. To reduce the computational expense in later optimization steps, a scaled gradient analysis (SGA) is performed to evaluate the importance of each design variable, to identify chances for any structural alterations to the process flowsheet (changes to equipment), and to reduce the parameter set prior to optimization. The economic index is the UAW (uniform annual worth), instead of the TAC (total annualized cost), which was used by Douglas.33 The traditional economic-based SGA is extended in this research to include environmental impacts. First, scaled gradient analysis ranks all of these variables according to a rank-order parameter (r) that is used to identify significant process parameters and omit insignificant variables. Those design variables having rank-order parameters 1 order of magnitude less than the highest ranked parameter are neglected. Furthermore, the rank-order parameter is used to identify the opportunities for structural optimization. The design variables with higher rank-order parameters are included in the structural optimization as these variables can exert a large influence on the structure of the process, e.g., equipment sequence or size. Then, the relevance of each remaining variable is investigated using a proximity parameter (p). This parameter indicates the distance from optimum for the corresponding design variable. Those variables with proximity parameters less than 0.3 are assumed to be near optimum and are not included in the optimization. The detailed procedure is provided in Chen et al.34 2.8. Optimization Incorporating Multiple Objectives. Changing some continuous variables while keeping others constant can cause structural changes to the design. Examples of structural changes would be repositioning of a heat exchanger network or changing the size of some pieces of equipment. Structural optimization is performed manually in this study on those variables having substantial influences on the structure of the process. The next step is to set up a multiobjective function including economic and environmental indicators and perform optimization using the reduced set of key design variables identified in the SGA. These key design variables are considered manipulated variables prior to the performance of parametric optimization. The objective function combines two different types of performance measures, an economic index, net present value (NPV), and an environmental index, process composite environmental index (IPC) generated by the EFRAT method, using the analytic hierarchy process (AHP). The AHP score is based on a pairwise compari-


Figure 2. Structure of the software tool (SCENE).

son of design alternatives. It is nonconvex, discontinuous, and nondifferentiable, and it might have multiple local optima. A genetic algorithm is used in this work to perform the optimization because it provides a flexible, relatively efficient, and effective method for handling the black-box, discontinuous, and nondifferentiable objective functions and can often find the global optimum.36 Although genetic algorithms use stochastic search procedures, the possibility of remaining in a local optimum can be lessened if the optimization parameters are set properly. A more detailed explanation of implementing genetic-algorithm-based optimization is provided by Chen et al.34 A process design with optimum structure and operating conditions will be obtained after this step. 2.9. Integrated Software Tool for Environmentally Conscious Design. To aid in the economic and environmental evaluation of the processes during the various design stages shown in the framework of Figure 1, a suite of integrated software, SCENE, is used, as shown in Figure 2. SCENE (simultaneous comparison of environmental and nonenvironmental process criteria) is linked with a commercial process simulator, HYSYS, using OLE (object linking and embedding) programming. SCENE can operate in two modes, assessment mode and optimization mode. In both modes, HYSYS performs process simulation. SCENE extracts process information from the simulation and performs assessments using DORT and EFRAT or optimization using the optimizer. Descriptions of each tool embedded within SCENE in Figure 2 are presented in greater detail in another publication;34 however, a brief summary of each tool is provided here. DORT (design option ranking tool) calculates several cost elements, including capital costs of various pieces of equipment provided with user-supplied or simulatorinput size information. Using the mass and energy balances from the process simulator, DORT calculates the operating cost (working capital), income, and other expenses. All of these calculations lead to three economic indices: net present value (NPV), payback period (PP), and fixed capital investment (FCI). The environmental impact assessment methodology in SCENE is the environmental fate and risk assessment tool (EFRAT).25 This tool integrates various impact assessment steps including (1) process release estimation using U.S. EPA emission factors and models,37 (2) multimedia pollutant fate and transport modeling, (3) relative risk assessment, and (4) normalization and valuation. Nine environmental categories are char-

acterized, including indices for fish toxicity (IFT), human inhalation toxicity (IINH), human ingestion toxicity (IING), carcinogenic human inhalation toxicity (ICINH), carcinogenic human ingestion toxicity (ICING), global warming (IGW), ozone depletion (IOD), smog formation (ISF), and acid rain (IAR). A single process composite index (IPC) is formulated to aggregate from these nine by applying normalization for each impact category, followed by a valuation step based on the Eco-indicator 9538 method. A detailed description of this method for calculating IPC is found in Shonnard and Hiew.25 Embedded in the tool is a database of chemicals with environmental properties to support impact calculations. This feature allows for complex evaluations even at the early stages of design. The economic and environmental objectives are aggregated into a single objective function using the analytic hierarchy process (AHP).27,39 At the bottom of the hierarchy are an economic index (net present value, NPV) and an environmental index, IPC. These indices are normalized by their respective maximum values over the number of alternative designs being compared, and the results are converted to quantitative scores using a lookup table. The qualitative weightings of economic (0.82) and environmental (0.18) attributes are provided by a survey of experts.40 Process diagnostic summary (PDS) tables organize mass input/output, energy consumption, operating costs, and environmental impacts of the process. Data for these tables originate from DORT, EFRAT, and process simulation. PDS tables are useful for recognizing process improvement/optimization opportunities in early design. The purpose of SGA (scaled gradient analysis) is to reduce the set of process parameters prior to optimization. The optimizer performs multiobjective optimization based on a single objective provided by AHP that combines economic and environmental aspects and outputs new sets of manipulated valuables to HYSYS until it converges. 3. Case Study: Maleic Anhydride Production from Benzene or n-Butane The methodology described above is applied in the following case study, maleic anhydride production from two alternative feedstocks, benzene or n-butane. Maleic anhydride (MA) is involved in many chemical manufacturing processes. It can be used in unsaturated polyester resin, in fumaric and malic acid production, in lube oils as an additive, and in maleic copolymers.41 3.1. Initial Design Task. Maleic anhydride can be produced from hydrocarbons in fixed-bed reactors. Only benzene and n-butane are regarded as industrially important feedstocks.42 Benzene or n-butane is partially oxidized in the vapor phase in the presence of a catalyst. The catalyst for the production of MA from n-butane is vanadium phosphorus oxide (VPO),41 whereas the catalyst is V2O5-MoO3 for benzene oxidation.43 Raw materials not only are partially oxidized to form MA, but also form CO and CO2. MA can also undergo oxidation to CO and CO2. Using the kinetic model of Bielaski and Najbar43 for benzene oxidation and the Sharma et al. model44 for n-butane oxidation, it has been shown that equal molar amounts of CO and CO2 are formed in the side reactions (raw material and MA oxidation to carbon oxides). The main reaction of benzene to MA produces additional CO2, whereas that of n-butane to MA does not. The reactor offgas, which is composed mainly of product (MA), byproducts (CO, CO2, and H2O), unre-

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acted raw materials (benzene or n-butane), and inert gas (nitrogen), is sent to a recovery system. The recovery system can be categorized as either water-based or solvent-based. The latter has a higher recovery of MA and saves more energy; therefore, it is selected as the separation method in this case study. In this process, the cooled reactor offgas enters into an absorber column with absorption solvent flowing countercurrently. The solvent from the bottom of the absorber, which is rich in MA, is preheated and then distilled in a vacuum distillation column to separate MA from the solvent. The solvent is then recycled back to the absorber. A small stream of makeup solvent is added to compensate for losses from the absorber, distillation column, and product stream. The solvent candidates can be obtained from patents. 3.2. Process Synthesis, Early Design Assessments, and Development of Base-Case Flowsheet. 3.2.1. Reaction Pathway Screening. Each reaction route is evaluated on the basis of a screening economic and environmental evaluation, as indicated in Figure 1 and explained in section 2.2. Typically, the conversion of benzene is about 95%, with a molar yield of about 70%, whereas the conversion of n-butane is approximately 85%, with a molar yield of 60%.41 The definitions of conversion (X) and molar yield (Y) are provided in eqs 1 and 2. In these two equations, F is the molar flow rate (moles/hour), subscript B denotes raw material (benzene or n-butane), subscript M denotes maleic anhydride, and FB0 is the molar flow rate of raw material feed (moles/hour).

X)

FB0 - FB FB0

(1)

FM FB0

(2)

Y)

Raw material cost is chosen as the economic screening criteria, because it is often the dominant cost element in a process. Also, other costs, such as utility cost, solvent cost, or capital cost, are not included because, at this early stage, such information is not available. This assumption will be justified later after an evaluation of the process using the PDS tables. The unit prices of benzene and n-butane were obtained from standard references.46 Average prices are taken over a one-year period and are 0.280 $/kg for benzene and 0.214 $/kg for n-butane. Assuming 1 mol of maleic anhydride produced, at least 1/0.70 mol of benzene or 1/0.60 mol of n-butane is needed. The raw material costs are

benzene: (1 mol/0.70 mol) × (78 g/mol) × (0.000 280 $/g) ) 0.0312 $/mol of MA n-butane: (1 mol/0.60 mol) × (58 g/mol) × (0.000 214 $/g) ) 0.0207 $/mol of MA This result shows that the n-butane route costs less for the raw material than the benzene route because of the lower price of n-butane, even though the molar yield of maleic anhydride from n-butane is lower. The screening environmental evaluation at this earliest design stage includes air release estimates from

major pieces of equipment (reactor, separation units, and pollution control) for unreacted raw materials, byproducts, and product. These release estimates are manually input into EFRAT and yield a set of environmental impact indices. The emission from the reactor is estimated using an average emission factor (EFav, 1.50 kg emitted/103 kg throughput) calculated from data compiled by the U.S. EPA37 using the equation

E ) MvocEFav

(3)

In this equation, Mvoc is the mass flow rate (kilograms per unit time) of the volatile organic compound in the reactor, and it is taken as the average mass flow rate through the reactor. The emissions from the absorber and distillation column originate from the offgas vents and contain a large amount of unreacted raw material, byproducts, and unrecovered product. Raw material, especially benzene, and one of the byproducts, CO, are toxic. To include the effects of pollution control at this early design stage, it is assumed that 99% of the unreacted raw material, byproducts, and unrecovered product are incinerated to CO2 and only 1% of each is released to the environment. The recovery of MA in the separation system is assumed to be 99%. The amount of unreacted raw materials can be calculated using the conversions, and those of byproducts (CO + CO2) can be determined using the molar balance of carbon. The results are shown below.

Benzene route unreacted benzene: (1 mol/0.70 mol) × (1 - 0.95) ) 0.0714 mol/mol of MA CO:

(1 mol/0.70 mol) × (0.95 - 0.7) × 6/2 ) 1.071 mol/mol of MA

CO2: 1 mol × (2 mol/mol) + (1 mol/0.70 mol) × (0.95 0.7) × 6/2 ) 3.071 mol/mol of MA The emission of unrecovered MA from the incinerator is 1(0.01)(0.01) ) 1 × 10-4 mol. The estimated emission of benzene from the incinerator is (0.01)(0.0714 mol/mol of MA) ) 7.14 × 10-4 mol benzene/mol of MA, and those of of CO and CO2 from the incinerator are (1.071)(0.01) ) 1.071 × 10-2 mol CO/mol of MA and 3.071 + (1.071)(0.99) + (0.0714)(0.99)(6) + 1(0.01)(0.99)(4) ) 4.595 mol CO2/mol of MA, respectively.

n-Butane route unreacted n-butane: (1 mol/0.60 mol) × (1 - 0.85) ) 0.25 mol/mol of MA CO:

(1 mol/0.60 mol) × (0.85 - 0.6) × 4/2 ) 0.833 mol/mol of MA

CO2: (1 mol/0.60 mol) × (0.85-0.6) × 4/2 ) 0.833 mol/mol of MA The estimated emission of n-butane from the inciner-

Ind. Eng. Chem. Res., Vol. 43, No. 2, 2004 541 Table 1. Environmental Indices of the Two Routesa chemical benzene n-butane a

IFT 10-6

6.83 × 3.03 × 10-6

IING 10-3

3.32 × 3.11 × 10-3

IINH 10-2

4.57 × 3.78 × 10-2

ICING 1.43 × 0.00

ICINH

10-4

1.43 × 0.00

10-4

IOD 0.00 0.00

IGW 10-1

2.04 × 1.20 × 10-1

ISF 10-5

2.51 × 4.37 × 10-6

IAR 0.00 0.00

All values are in units of kg/mol of MA.

ator is (0.01)(0.25 mol/mol MA) ) 2.5 × 10-3 mol n-butane/mol of MA, and those of CO and CO2 from the incinerator are (0.833)(0.01) ) 8.33 × 10-3 mol CO/ mol of MA and 0.833 + (0.833)(0.99) + (0.25)(0.99)(4) + 1(0.01)(0.99)(4) ) 2.688 mol CO2/mol of MA, respectively. Table 1 shows the environmental indices calculated by the EFRAT model. All of the individual indices in the n-butane process are less than those in the benzene process. This table reveals that the benzene route is estimated to have greater environmental impacts than the n-butane route even though the molar emission of benzene is less than that of n-butane. A main reason for this outcome is that the n-butane process has no carcinogenic impacts, whereas the benzene process does. Also, the benzene route emits more CO2 (global warming index) and CO, and the greater environmental partitioning of benzene into water dominates IFT and IING. This analysis demonstrates the importance of making comparisons on the basis of risk indices that include multiple impacts, environmental fate modeling, and toxicity effects instead of merely emission rates. Both screening environmental and economic assessments confirm that the n-butane route is more desirable. 3.2.2. Mass-Separating Agent Selection. Up to this point in the design, the best reaction route has been identified. The next step is to select the mass-separating agent for the MA recovery system. Dibutyl phthalate was chosen from 26 candidate solvents using a method, described in detail in prior publications,45,47 that is based on process simulation and impact assessment of each solvent in the intended process. 3.3. Process Simulation. The next design task is process simulation of an initial process flowsheet. The n-butane process will be the basis for this case study for the evaluation of an initial flowsheet, the PDS tables, and the modifications to achieve an improved flowsheet. The reactor design is based on heuristics and constraints and is accomplished using the process simulator. Typically, the reactor would be operated to maximize the yield of MA48 such that the usage and cost of raw materials are minimized. A series of analyses and HYSYS simulations was performed in this study to determine the operating conditions (feed ratio of air to n-butane, reaction temperature, and pressure) to achieve the maximum MA yield and to determine the length of the packed-bed reactor. It is assumed that the design basis is a 50 × 106 lb of MA/year (22.68 × 106 kg/year) production rate of MA. Operating the reactor at higher pressure will require a smaller reactor but will diminish the selectivity for MA49 and increase the utility consumption for the air compressor. Therefore, it is preferred that the reactor be operated at lower pressure, yet at a reactor outlet pressure that is high enough to provide sufficient flow of the product through the downstream recovery system; 130 kPa is sufficient for this purpose. Using less air will reduce the utility consumption of the compressor and the pressure drop across the reactor. Therefore, a lower ratio of air to n-butane is desired. However, the ratio of

Figure 3. Maximum molar yield of MA, conversion, and reactor length as a function of reaction temperature for the n-butane process.

n-butane to air is constrained to be less than n-butane’s lower flammable limit, which is 1.7 mol %.41 Therefore, for safety reasons, the base-case molar ratio of air to n-butane is chosen to be 60:1. The reactor was simulated at several reaction temperatures using HYSYS. The kinetic model of Sharma et al.44 was used for the n-butane pathway, and the results are shown in Figure 3. When the reaction temperature is 410 °C, this reactor achieves the highest yield of MA. For the initial process design, the reaction temperature is chosen to be 410 °C. The reactor length is 6 m, and three parallel reactors are needed. The reaction and separation systems are coupled together to establish the base-case process flowsheet (Figure 4). 3.4. Process Diagnostic Summary Tables. Process diagnostic summaries (PDSs) were developed for the process flowsheet following process simulation to identify early opportunities for process improvement. 3.4.1. Mass Input/Output Table. Some useful information can be obtained from this table, such as conversion, yield, and selectivity (table not shown). There are three input streams (air, n-butane, and makeup solvent) and three output streams (MA product, absorber offgas, and distillation offgas). Air components of N2 and O2 are the dominant component flows. The predicted conversion of n-butane is approximately 98% (higher than the assumed 85%), and the yield of maleic anhydride is about 55% (lower than the assumed 60%). Nonetheless, the small differences in conversion and yield do not alter the results of the screening economic and environmental comparisons of the benzene and n-butane pathways shown in section 3.2.1. 3.4.2. Energy Input/Output Table. Table 2 shows the energy input and output from the process. The energy flow of each input or output material stream, such as air, n-butane, and makeup solvent, is calculated by assuming a reference temperature and using the equation

E ) MCp(T - Tref)

(4)

where M is the mass flow rate of the stream (kilograms per hour), Cp is the heat capacity (kilojoules per

542


Figure 4. Base-case process flowsheet of MA production from n-butane. Table 2. Energy Input/Output Table for the n-Butane Process as Shown in Figure 4

stream

available temperature (in, out) (°C)

air n-butane makeup solvent solvent pump air compressor n-butane vaporizer reactor feed heater reboiler total

25 10 35 244.93-244.98 35-75.1 10 71.46-410 244.93

absorber offgas distillation offgas crude MA reactor 1 reactor 2 reactor 3 reactor offgas cooler solvent subcooler condenser total

49.18 35.02 35.02 410 410 410 410-110 112.75-35 35.02

available pressure (in, out) (kPa) Input 101.33 153.6 125 8.62-125 101.33-153.6 153.6 153.6 8.62 Output 126 2.69 2.69

kilogram per degree Celsius), T is the temperature of the stream (degrees Celsius), and Tref is the reference temperature (degrees Celsius). The majority of the input energy is used to preheat the reactor feed. The next largest energy inputs are the reboiler and compressor duties. From the energy flow out of the processes, the heat produced from the reaction accounts for approximately 64%. The reactor offgas cooler consumes around 24.23% of the total outlet energy. It is apparent that the heat required to elevate the reactor feed temperature is very close to that removed from the reactor offgas, and the temperature ranges for these streams are very compatible. This implies that heat integration between the reactor feed and reactor offgas can reduce the energy consumption,

130.61 125 2.69

energy flow (MM kJ/h)

fraction of total energy (%)

0 -0.0447 0.0004 0.0113 4.1768 1.0613 31.5251 5.3569 42.0871

0.00 -0.11 0.00 0.03 9.92 2.52 74.90 12.73 100.00

2.1136 0.0002 0.0388 24.9352 24.9352 24.9352 28.3747 7.5529 4.2415 117.1273

1.80 0.00 0.03 21.29 21.29 21.29 24.23 6.45 3.62 100.00

but the added risk of reaction runaway in real applications requires a risk analysis with potentially costly safety add-ons. Another possible opportunity is to utilize the heat generated by the reaction to produce highpressure steam for the distillation reboiler and raw material vaporizer and to generate electricity. 3.4.3. Utility Annual Expense Summary. Table 3 shows the utility annual expense summary. The reactor feed heater is 58.49% of the utility expense, with the reboiler at 19.17% and air compressor at 15.10%. If the heat integration schemes described in the energy input/ output analysis are implemented, the majority of the utility expense can be eliminated. This table also shows that the utility cost of the air compressor is among the highest after heat integration is implemented. In the

Ind. Eng. Chem. Res., Vol. 43, No. 2, 2004 543 Table 3. Utility Annual Expense Summary for the n-Butane Process as Shown in Figure 4 consumption rate (units)

equipment

unit cost

cooling water (tower) reactor offgas cooler solvent subcooler condenser

$0.16/1000 U.S. gal

steam (50 psig) n-butane vaporizer

112,924 30,101 16,887 159,913

3.73 0.99 0.56 5.28

subtotal

(kW‚h) 1159.6 3.1 1162.7

457,097 1,238 458,335

15.10 0.04 15.14

$6/1000 lb

(lb) 1103.8 1103.8

58,014 58,014

1.92 1.92

6973.1 6973.1

580,303 580,303

19.17 19.17

(STP ft3) 28 868.8

1,770,237

58.49

1,770,237 3,026,802

58.49 100.00

$0.045/kW‚h

subtotal steam (600 psig) reboiler

$9.5/1000 lb

(lb)

subtotal natural gas reactor feed heater

$7/1000 STP ft3 subtotal annual utility cost

Table 4. Manufacturing Revenue and Expense Statement for the n-Butane Process as Shown in Figure 4 name maleic anhydride total sales revenue

fraction of total cost (%)

(U.S. gal) 80 568.2 21 476.3 12 048.5 114 093.0

subtotal electricity (on site) air compressor solvent pump

annual cost ($/year)

total ($/year) Revenue 21,258,236 21,258,836

Manufacturing Expenses raw materials n-butane cost 4,760,866 makeup solvent 81,343 utilities cooling water (tower) 159,913 electricity (on site) 679,014 steam (50 psig) 58,014 steam (600 psig) 580,303 natural gas 2,212,796 total manufacturing expenses 8,532,249

fraction of total cost (%) 100.00 100.00

55.80 0.95 1.87 7.96 0.68 6.80 25.93 100.00

design of the reactor, the operating cost of air compressor should be considered in addition to the raw material cost. The reactor design will be reevaluated after this section. 3.4.4. Manufacturing Revenue and Expense Statement. Table 4 is the manufacturing revenue and expense table for the n-butane process. The utility expenses in this table reflect the efficiency of each type of utility. The major contributor to the manufacturing expense is the raw material cost (55.80%); therefore, it is desired to design the reactor at maximum yield of MA. Also, the assumption made in section 3.2.1 of comparing only raw material costs when selecting reaction pathways is a sound strategy. This table also suggests the opportunity to minimize utility expenses, as stated in the previous summaries, by applying heat integration. To calculate the revenue, the price of maleic anhydride was obtained from Chemical Week.46 The average price taken over a 1-year period is 0.93 $/kg. 3.4.5. Environmental Impact Summary. There are several types of emission sources, including emissions from consumption of utilities (due to the air compressor, raw material vaporizer, reactor feed heater, reboiler, and solvent pump), emissions from the incinerator for treating absorber and distillation column offgas, emis-

28 868.8

sions from the storage tanks (solvent, maleic anhydride, and raw material tanks), and emissions from the reactors. Tables 5 and 6 display the environmental impact summary. Table 5 shows the risk index of each chemical, and Table 6 shows the risk index from each emission source. The major contributor to IPC is the human inhalation toxicity index, IINH, which comprises 86.63% of the composite index, followed by IAR (6.50%) and IGW (4.85%). According to Table 6, the emissions from the absorber offgas (via the incinerator), reactors, and one major utility (the reactor feed heater) dominate the value of IINH, and from Table 5, the emission of CO is the major contributor to IINH. This suggests that overall impacts can be decreased by reducing the utility usage and production of CO from the reactors. IAR is dominated by emission of nitrogen and sulfur oxides from utility consumption. IGW is mainly caused by emission of CO2 from utility consumption and incineration of offgas from absorber (Tables 5 and 6). Therefore, by reducing the utility usage, these two indices can be greatly reduced. 3.4.6 Conclusions from the Process Diagnostic Summary Tables. Several process improvement strategies are identified and summarized here. 1. Heat integration should be applied between the reactor offgas and reactor feed to reduce utility consumption and the costs/environmental impacts associated with these utilities. It can be shown that this heat integration procedure reduces the utility cost of the process by 63%, reduces the global warming impact by 32%, and decreases the overall environmental impact (IPC) by more than 11%. 2. The reaction heat can be used to generate highpressure steam to supply heat for the reboiler and raw material vaporizer. This heat integration procedure reduces the utility cost of the process by 17%, reduces the global warming impact by 7%, and decreases the overall environmental impact (IPC) by 2%. 3. To reduce total environmental impact (IPC via IINH), two major items should be considered: utility consumption and CO production from the reactions. The former can be reduced by heat integration, and the latter can be implemented by adjusting the operating conditions of the reactors to produce less byproduct.

544


Table 5. Risk Index of Each Chemical for the n-Butane Process as Shown in Figure 4a chemical sulfur dioxide TOC carbon dioxide carbon monoxide dibutyl phthalate maleic anhydride n-butane nitrogen dioxide total contribution to IPC (%) IPC a

IFT 0.00 1.36 × 10-2 4.36 × 102 1.90 × 10-1 7.70 × 101 5.10 × 102 6.98 × 10-2 2.10 × 10-1 1.02 × 103 1.55 6.13 × 10-4

IING

IINH

0.00 1.49 × 10-2 0.00 0.00 1.00 × 102 7.27 × 105 0.00 0.00 7.27 × 105 0.34

1.49 × 6.62 × 101 8.91 × 101 1.65 × 107 3.01 0.00 2.38 × 105 2.89 × 103 1.67 × 107 86.63 101

ICING

ICINH

IOD

IGW

ISF

IAR

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 4.11 × 103 6.09 × 107 2.33 × 105 2.56 × 102 3.49 × 104 6.97 × 104 4.09 × 106 6.54 × 107 4.85

0.00 4.24 × 102 0.00 2.03 × 103 0.00 0.00 0.00 0.00 2.46 × 103 0.14

1.35 × 102 0.00 0.00 0.00 0.00 0.00 0.00 7.16 × 104 7.17 × 104 6.50

All values are in units of kg/year, excluding IPC, which is unitless.

Figure 5. Process composite environmental index versus total cost of raw materials and utility for various reaction temperatures in the n-butane process.

In addition to these improvement opportunities, the process diagnostic summary tables also verify some heuristics and assumptions that were applied in the early design stages. 1. From the manufacturing revenue and expense statement table, the raw material cost is dominant compared to utility costs, especially after heat integration is applied. Therefore, it is a sound strategy that, during an early design screening analysis, only raw material costs are used in comparing reaction pathways. This observation confirms the simplified approach used in the reaction pathway screening step shown previously in this case study. 2. From the environmental impact summary tables, the dominant contribution to IPC is due to the emission of CO generated from the reactors and emitted from the absorber via the pollution control device. This observation confirms the need to use reasonable assumptions regarding pollution control at the early design stage evaluation to achieve a better estimate of the actual impacts during early design. The assumptions and early design heuristics generated in this study must be viewed as specific to this case study. Additional case study analyses should be performed before generalizing these results to other processes. The PDS indicates that the operating cost for the air compressor is very important. In the initial reactor design shown in Figure 3, only the raw material cost is considered to determine the reactor length and operating conditions. It is a good idea to reevaluate the reactor design by including the compressor utility cost and environmental and economic assessments. The objective is to achieve the minimum expense based on raw materials and utilities. Figure 5 shows the total cost of raw material and of the air compressor utility and process composite environmental index from emissions of reactor, utility consumption, and from the incinerator (assuming 99% control efficiency) at each reaction temperature. The economic optimum is still at 410 °C.

The environmental optimum is located at a slightly lower temperature than the economic optimum. This more detailed analysis shown in Figure 5 validates the previous reactor length calculation method for this case study. 3.5. Development of a Modified Base-Case Flowsheet. One of the process improvement schemes identified earlier, heat integration between reactor outlet and feed, is applied to the base case. The other heat integration step, use of reaction heat to produce highpressure steam and electricity, could be implemented but is not considered in this analysis because its implementation is beyond the boundary of the MA process being investigated, involving other processes in the facility. The offgas from the absorber and distillation column are incinerated (99% efficiency), and it is assumed that all of the resulting CO2 is emitted into the atmosphere. The modified base-case flowsheet for the n-butane process is shown in Figure 6. The flowsheet for the benzene process is similar, except that the benzene process requires only two reactors to meet the production requirements (50 × 106 lb of MA/year) and there is only one heat integration exchanger between the reactor feed and offgas. These flowsheets are now ready for detailed evaluation and optimization using economic and environmental objective functions, as described next. 3.6. Equipment Sizing and Economic and Environmental Assessments. Equipment sizing in this study follows the procedures in process design textbooks1,33,35 making use of economic-based heuristics (e.g., 10 °C approach temperature difference on heat exchangers, etc.). The economic assessment of the resulting improved flowsheet includes capital, installation, and operating costs of major equipment; raw material costs (benzene or n-butane and solvent); and product revenue. The minimum acceptable return is assumed to be 20%, and the yearly inflation rate is 3.4%, which is the average inflation rate in the U.S. over the period 1982-2001. The environmental assessment methodology and software tool are the same as discussed in the early design section. Table 7 shows some important assessment results for the two improved base-case designs for both the n-butane and benzene process flowsheets. The n-butane process requires more investment in equipment than the benzene process. The n-butane process needs one more reactor than the benzene process because of the lower yield. The n-butane process produces nearly 3 times the amount of water as the benzene process, resulting in a larger absorber column. The raw material cost in the n-butane process is less than that in the benzene process because of the lower

1.02 × 103 7.27 × 105 1.67 × 107 0.00 0.00 0.00 6.54 × 107 2.46 × 103 7.17 × 104 6.13 × 10-4 5.30 7.03 × 103 2.02 × 103 0.00 0.00 0.00 5.13 × 104 2.49 × 10-1 0.00 1.57 × 10-7 3.72 × 8.20 × 104 1.42 × 107 0.00 0.00 0.00 3.33 × 107 1.75 × 103 0.00 4.71 × 10-4 4.51 × 3.37 × 10-3 2.60 × 105 0.00 0.00 0.00 6.59 × 106 1.28 × 102 5.17 × 103 1.46 × 10-5 1.22 × 9.13 × 10-6 7.03 × 102 0.00 0.00 0.00 1.79 × 104 3.47 × 10-1 1.40 × 101 3.95 × 10-8 2.17 × 1.62 × 10-3 1.38 × 105 0.00 0.00 0.00 3.57 × 106 6.33 × 101 9.40 × 103 1.15 × 10-5 1.28 × 9.54 × 10-3 8.12 × 105 0.00 0.00 0.00 2.10 × 107 3.72 × 102 5.52 × 104 6.73 × 10-5 a

All values are in units of kg/year, excluding IPC, which is unitless.

4.30 3.21 × 10-4 2.73 × 104 0.00 0.00 0.00 7.07 × 105 1.25 × 101 1.86 × 103 2.27 × 10-6 4.46 × 6.34 × 105 1.26 × 106 0.00 0.00 0.00 1.14 × 105 1.31 × 102 0.00 4.60 × 10-5 2.44 3.44 × 103 3.01 × 10-2 0.00 0.00 0.00 1.66 × 102 3.59 × 10-6 0.00 3.26 × 10-8 6.46 × 8.40 × 10-7 2.53 × 10-8 0.00 0.00 0.00 2.15 × 10-6 0.00 0.00 6.00 × 10-15 1.09 × 0.00 3.73 × 102 0.00 0.00 0.00 1.09 × 102 0.00 0.00 1.19 × 10-8 IFT IING IINH ICING ICINH IOD IGW ISF IAR IPC

distillation absorber

102 101

10-1 101 102 102

10-7

air compressor solvent pump reboiler feed heater n-butane vaporizer reactors MA tank solvent tank n-butane tank

Table 6. Risk Index of Each Emission Source for the n-Butane Process as Shown in Figure 4a

10-4

total


price of n-butane (0.214 $/kg) than benzene (0.280 $/kg). The n-butane process is more profitable than the benzene process, as shown by the NPV values in Table 7. The environmental assessment shows several interesting features for these improved process flowsheets. The n-butane process has a much lower environmental impact than the benzene process, as shown by the IPC values (189 times lower). The main reason for this difference is that the n-butane process has no carcinogenic impacts, whereas the benzene process does. In the n-butane process, the major contributor to IPC is the human inhalation toxicity index, IINH, which comprises 93.93% of the composite index, followed by IGW (3.44%) and IFT (1.55%). The emission of CO from incineration of the absorber offgas is the major contributor to IINH. The emission of CO should be further reduced to decrease the environmental impacts from the n-butane process, for example, by increasing the selectivity of the reaction. The majority of IFT is contributed by the emission of maleic anhydride from the reactors and the emission of maleic anhydride from incineration of the absorber offgas. IGW is dominated by the emission of CO2, not only from utility consumption, but also from incineration of the absorber and distillation column offgas. In the benzene process, IPC is dominated by ICING and ICINH (99.82% of total IPC). These two indices reflect the emission of benzene from the reactors and also from the absorber offgas pollution control device (incinerator). This indicates that an effort should be made to reduce these two indices, that is, to reduce the amount of unreacted benzene exiting the reactor by increasing the reactor conversion of benzene. 3.7. Scaled Gradient Analysis. The process diagnostic summary (PDS) tables suggest variables to be included in the scaled gradient analysis. The manufacturing revenue and expense statement table indicates that reducing raw material usage in both processes will greatly improve the economic performance. The important variables that are related to raw material usage are reaction temperature, reaction pressure, ratio of air to benzene (or n-butane), recovery of MA in the two columns, and absorption temperature. In the benzene process, the environmental impact summary concludes that emissions of benzene from the reactors and incineration of the absorber offgas dominate the environmental performance, whereas in the n-butane process, emissions of CO from incineration of the absorber offgas, reactors, and utilities dominate the environmental performance. Variables related to the reaction step, such as reaction temperature, reaction pressure, and ratio of air to benzene (or n-butane), should be included. In addition to these variables, the reflux ratio and minimum approach temperature of each set of heat integration exchangers (those between the reactor feed and offgas and that between the recycle solvent and distillation column feed) should be included. When conducting the SGA, one design variable is changed while the others are kept constant and equal to their base-case values, and the specified production rate of MA, which is 50 × 106 lb/year, is maintained. Variables will be excluded from subsequent optimization only if both the economic and environmental SGAs indicate that they can be neglected. Table 8 lists each design variable with its units, incremental change, and scale factor. The scale factor is set according to the maximum range of each design

546


Figure 6. Modified base-case flowsheet of MA production from n-butane.

variable, and each design variable is changed slightly from the base-case value within the maximum range. 3.7.1. SGA Results for MA Production from Benzene. In the interest of brevity, only the final results from the SGA will be discussed. The economicbased SGA shows that all of the variables are important in optimization, except the solvent inlet temperature (variable 3 from Table 8), reflux ratio (variable 9), and the approach temperature of the heat integration exchanger between the recycle solvent and the distillation column feed (variable 11). The environmental-based SGA shows that four of the design variables (variables 3, 4, 9, and 11) can be excluded from optimization. Three variables are common (variables 3, 9, and 11) and can be excluded from optimization on the basis of both the economic and environmental SGA results. 3.7.2. SGA Results for MA Production from n-Butane. The economic SGA shows that all of the variables, except the solvent inlet temperature (variable 3), reflux ratio (variable 9) and the minimum approach temperature of the heat integration exchanger between the recycle solvent and the distillation column feed (variable 11), should be included in the optimization. In the environmental SGA, variables 3-5 and 9-11 can be excluded from the optimization. When the results from the economic and environmental SGAs are compared, variables 3, 9, and 11 can be excluded from both the economic and environmental optimizations. Therefore, when the process is optimized using the AHP, the same manipulated variables are used as in the optimization based on economics as the objective. 3.8. Optimization of Maleic Anhydride Process Flowsheets. The optimization is carried out in two

Table 7. Comparison of the Modified Base-Case Flowsheet for MA Productiona Economic Performance NPVb CIc OCd RCe

units

n-butane

benzene

$ $ $/year $/year

3,044,252 19,472,756 13,049,907 5,274,830

2,561,415 16,081,942 14,211,938 6,809,811

Environmental Performance n-butane

IFTf IINHg IINGh ICINHi ICINHj IGWk IARl ISFm IODn IPCo

units

value

kg/year kg/year kg/year kg/year kg/year kg/year kg/year kg/year kg/year -

9.05 × 102 1.59 × 107 7.53 × 105 0.00 0.00 4.08 × 107 5.28 × 103 2.03 × 103 0.00 5.40 × 10-4

contribution to IPC (%) 1.554 93.933 0.404 0.000 0.000 3.438 0.543 0.127 0.000

benzene value 1.80 × 103 4.26 × 106 7.94 × 106 4.27 × 104 4.27 × 104 4.61 × 107 3.77 × 103 6.37 × 103 0.00 1.02 × 10-1

contribution to IPC (%) 0.016 0.133 0.002 49.912 49.912 0.021 0.002 0.002 0.000

a Acceptable rate of return, 20%; yearly inflation rate, 3.4% (the average inflation rate for the period 1982-2001). b NPV ) net present value. c CI ) capital investment. d OC ) operating cost. e RC ) raw material cost. f I g FT ) fish toxicity index. IINH ) human inhalation toxicity index. h IING ) human ingestion toxicity index. i I j CINH ) human carcinogenic inhalation toxicity index. ICING ) human carcinogenic ingestion toxicity index. k IGW ) global warming index. l IAR ) acid rain index. m ISF ) smog formation index. n I o OD ) ozone depletion index. IPC ) process composite environmental index.

steps: structural and parametric optimizations. In the structural optimization, those variables having a large

Ind. Eng. Chem. Res., Vol. 43, No. 2, 2004 547 Table 8. SGA: Units, Incremental Change, and Scale Factor for Each Design Variable design variable

units

1 2 3 4

decrease the recovery of maleic anhydride in the absorber increase the recovery of maleic anhydride in the absorber increase the solvent inlet temperature decrease the recovery of maleic anhydride in the distillation column

°C -

5 6 7 8 9 10

increase the feed ratio of air to benzene (or n-butane) increase the reactor inlet pressure decrease the reactor inlet temperature increase the reactor inlet temperature increase the reflux ratio increase the minimum approach temperature of heat exchanger(s) between reactor feed and offgas increase the minimum approach temperature of heat exchanger between recycle solvent and distillation column feed

kPa °C °C °C

11 a

°C

incremental change

scale factor

-0.01 0.009 5 -0.019a -0.017b 5 10 -5 5 0.1 5

0.1 0.1 10 0.1

5

10 30 20 20 0.5 10 10

Benzene process. b n-Butane process.

Table 9. Variations in Uniform Annual Worth (UAW) (Excluding MA Revenue) and IPC with the Minimum Approach Temperature of the Heat Integration Exchanger between the Reactor Feed and Offgas for the Benzene Process minimum approach temperature (°C)

UAW

IPC

10 12.5 15 17.5 20

($11,236,034) ($11,202,743) ($11,136,130) ($11,122,252) ($11,104,244)

1.02 × 10-1 1.17 × 10-1 1.11 × 10-1 1.06 × 10-1 1.01 × 10-1

influence on the structure of the flowsheet are optimized. Then, the parametric optimization is conducted, varying manipulated variables to determine the best operating configuration while maintaining the fixed equipment layout and size. 3.8.1. Maleic Anhydride Production from Benzene. According to the results from the economic SGA, among the remaining variables, only three will improve the process economic performance (variables 2, 4, and 10), and one of these three variables (variable 10) will make substantial structural changes to the flowsheet. This variable is optimized manually over its parameter range. Increasing the minimum approach temperature from 10 to 20 °C of the exchanger for variable 10 will increase UAW and decrease IPC slightly, as shown in Table 9. As a result of this analysis, this variable is changed to its maximum value, 20 °C. The parameters that are included in the parametric optimization are shown in Table 10. The recycle solvent flow rate is the variable used to reflect the influence of the recovery of MA in the absorber, and the reflux ratio is used to manipulate the recovery of MA in the distillation column. When the process is optimized using the AHP, five variables are included: reactor inlet temperature, reactor inlet pressure, feed ratio of air to benzene, recycle solvent flow rate, and reflux ratio. For optimization based on NPV, these five variables are also used. For optimization based on IPC, only four manipulated variables need to be considered. They are feed ratio of air to benzene, reactor inlet temperature and pressure, and recycle solvent flow rate. The optimization is performed using a genetic algorithm embedded in the SCENE software. Before the optimization is executed, several optimization parameters must be set, including the initial population size, mutation probability, and convergence criterion, to increase the probability of achieving the global optimum. In this case study, the

population size is 100, the mutation probability is 0.04, and 100 generations are calculated. If the process is optimized using the AHP (Table 10), the process has an NPV of $3.44 MM and an IPC of 9.24 × 10-2. When NPV is the objective, NPV is $3.49 MM and IPC is 9.24 × 10-2; that is, the profitability is improved slightly while the environmental impact remains unchanged. More significantly, compared to the values in Table 7 (improved base-case flowsheet), NPV is improved by 34%, and IPC is reduced by 9%. When the optimization is based only on IPC, IPC is reduced by about 44% relative to Table 7; however, at the same time, NPV is decreased to -$2.13 MM. Thus, there is a significant tradeoff between economic and environmental performance metrics when the objective function for parametric optimization is either NPV or IPC. Substantial improvement in both economic and environmental performance is achieved relative to the improved basecase design by the performance of structural and parametric optimization. It is observed that the optimum of the process obtained using the AHP is very close to that based on NPV not only in the values of NPV and IPC, but also in the optimum parameter values. The reason for this outcome is that a larger weighting factor is assigned to economics (0.82) than to environment (0.18)40 in the calculation of the AHP score. The process based on IPC operates at a higher reactor temperature and pressure. As stated before, IPC is dominated by ICING and ICINH, and these two indices are closely tied to the fugitive release of benzene from the reactors and the uncontrolled benzene from the incineration of the absorber offgas. As the reactor temperature and pressure are increased, the conversion of benzene increases (lowering IPC); however, the selectivity toward MA decreases (decreasing NPV). Therefore, to minimize the environmental impacts, efforts should be made to increase the conversion of benzene. On the other hand, if the process is optimized on NPV, a higher selectivity is needed to improve the process. These two conflicting objectives are reconciled if the process is optimized according to the AHP. 3.8.2. Maleic Anhydride Production from nButane. According to the results from SGA, the solvent inlet temperature, the reflux ratio, and the minimum approach temperature of the heat integration exchanger between the recycle solvent and the distillation column feed can be excluded from the optimizations based on NPV and AHP. Three more variables, namely, the recovery of MA in the distillation column, the feed ratio

548


Table 10. Optimization Results Obtained when Using AHP, NPV, and IPC as Objective Functions for the Benzene Process AHP operating conditions reflux ratio reactor inlet temperature reactor inlet pressure recycle solvent flow rate feed ratio of air to benzene

units

range

value

minimum approach temperature (°C)

UAW

IPC

°C kPa kgmol/h -

0.81-1.3 375-395 147-177 100-160 66-76

1.28 375.00 147.00 160.00 66.11

10 12.5 15 17.5 20

($10,556,894) ($10,430,013) ($10,354,949) ($10,299,323) ($10,245,948)

5.40 × 10-4 5.40 × 10-4 5.42 × 10-4 5.42 × 10-4 5.44 × 10-4

indices IFT IING IINH ICING NPV

units

value

kg/year kg/year kg/year kg/year MM$

1.66 × 103 7.18 × 105 4.70 × 106 3.89 × 104 3.44

ICINH IGW ISF IAR IPC

units

value

kg/year kg/year kg/year kg/year -

3.89 × 104 4.59 × 107 5.91 × 103 3.78 × 103 9.24 × 10-2

NPV operating conditions reflux ratio reactor inlet temperature reactor inlet pressure recycle solvent flow rate feed ratio of air to benzene

units

range

value

°C kPa kgmol/h -

0.8-1.3 375-395 147-177 100-160 66-76

1.30 375.00 147.00 159.96 66.00


units

value


1.66 × 7.18 × 105 4.72 × 106 3.88 × 104 3.49 103


units

value


3.88 × 104 4.60 × 107 5.91 × 103 3.77 × 103 9.24 × 10-2

IPC operating conditions reactor inlet temperature reactor inlet pressure recycle solvent flow rate feed ratio of air to benzene

unit

range

value

°C kPa kgmol/h -

375-395 147-177 100-160 66-76

395.00 177.00 130.18 66.00


units

value


1.49 × 8.56 × 105 7.99 × 106 2.37 × 104 -2.13 103

Table 11. Variations in Total UAW (Excluding MA Revenue) and IPC with the Minimum Approach Temperature of the Heat Integration Exchangers between the Reactor Feed and Offgas for the n-Butane Process


units

value


2.37 × 104 5.31 × 107 4.31 × 103 5.81 × 103 5.67 × 10-2

of air to n-butane, and the minimum approach temperature of the heat integration exchangers between the reactor feed and offgas, can be eliminated for the optimization based on IPC. Among the important variables for optimization, the minimum approach temperature of the heat integration exchanger between the reactor feed and the offgas (variable 10) will make substantial structural changes. This variable is changed manually over its parameter range, and the results are shown in Table 11. Increasing this variable will the improve economic performance, as the total UAW increases, while IPC changes very little. In the structural optimization, the minimum approach temperature is changed to 20 °C for this heat integration exchanger. The parameters that are included in the parametric optimization are listed in Table 12. The recycle solvent

Table 12. Optimization Results Obtained when Using AHP, NPV, and IPC as Objective Functions for the n-Butane Process AHP operating conditions reflux ratio reactor inlet temperature reactor inlet pressure recycle solvent flow rate feed ratio of air to n-butane

units

range

value

°C kPa kgmol/h -

0.8-1.3 390-410 153.8-173.8 170-230 60-70

1.27 399.55 153.80 230.00 62.30

indices IFT IING IINH NPV

units

value

kg/year kg/year kg/year MM$

8.00 × 6.60 × 104 1.59 × 107 5.14 102

IGW ISF IAR IPC

units

value

kg/year kg/year kg/year -

4.05 × 107 2.04 × 103 5.46 × 103 5.38 × 10-4

NPV operating conditions reflux ratio reactor inlet temperature reactor inlet pressure recycle solvent flow rate feed ratio of air to n-butane

units

range

value

°C kPa kgmol/h -

0.8-1.3 390-410 153.8-173.8 170-230 60-70

1.07 399.08 153.80 230.00 62.10


units

value


8.01 × 102 6.61 × 105 1.60 × 107 5.14

IGW ISF IAR IPC

units

value


4.05 × 107 2.04 × 103 5.48 × 103 5.40 × 10-4

IPC operating conditions reactor inlet temperature reactor inlet pressure recycle solvent flow rate

units

range

value

°C kPa kgmol/h

390-410 153.8-173.8 170-230

390.00 153.80 230.00


units

value


8.24 × 102 6.77 × 105 1.50 × 107 4.73

IGW ISF IAR IPC

units

value


4.06 × 107 1.92 × 103 5.38 × 103 5.11 × 10-4

flow rate is used to reflect the influence of the recovery of MA in the absorber, and the reflux ratio is used to reflect the influence of the recovery of MA in the distillation column. Other manipulated variables include the reactor inlet temperature and pressure and the feed ratio of air to n-butane. If the process is optimized on NPV, even though the reflux ratio is not


important, it is included to manipulate the recovery of MA in the distillation column. Therefore, these five variables are also included in the economic optimization, and similarly for the optimization based on the AHP. In the environmental optimization, only three variables are considered: the recycle solvent flow rate and the reactor inlet temperature and pressure. If the n-butane process is optimized according to the AHP, the process has an NPV of $5.14 MM and an IPC of 5.38 × 10-4. Compared to the values in Table 7, NPV is improved by 69%, and IPC is slightly decreased. When this process is optimized only on NPV, NPV is $5.14 MM, and IPC is 5.40 × 10-4. When the process is optimized on IPC, IPC is reduced (by 5%) to 5.11 × 10-4; however, at the same time, NPV is decreased to $4.73 MM. These results also are similar to those obtained for the optimizations based on the AHP or NPV as the objective function. Comparing the optimum operating configurations of the processes based on NPV and IPC, one can see that the process based on IPC operates at a lower reactor temperature. As stated before, IPC is dominated by IINH because of the emission of CO from incineration of absorber offgas. Therefore, when the reactor temperature is decreased, the selectivity increases, and less CO is produced; however, the conversion is lower. This also explains why NPV is reduced at a lower reactor temperature. 3.8.3. Comparisons of Optimized Benzene and n-Butane Processes. According to the results from the previous section, the optimized n-butane process has both superior economic and environmental performance compared to the optimized benzene process. If these optimized processes are compared using the AHP, the AHP scores are 0.5309 and 0.4691 for the n-butane and benzene processes, respectively. The n-butane process uses larger equipment, but its raw material cost is much less, and the overall economic performance is better. These results confirm the studies of earlier researchers,50,51 which showed that the process using n-butane as the feedstock is more favorable because of the low raw material price. However, in those studies, no environmental assessments were conducted. 3.9. Summary of Environmentally Conscious Design Results. This case study follows the design procedures in section 2 to perform both early and detailed design of the MA production process utilizing benzene or n-butane as the primary feedstock. In the early stages of design, the process is divided into reaction and separation systems. These two systems are designed separately and then are coupled together to form the entire process flowsheet. Process simulation is applied to the flowsheet, and process diagnostic summary (PDS) tables are created to identify opportunities for early process improvement strategies. In detailed design, SGA is then conducted to identify important variables for optimization. Structural and parametric optimization steps are carried out sequentially to determine the best configuration for the process. The decisions at every stage of design are made according to the considerations of both economic and environmental performance. 3.9.1. Continuous Improvement of the Designs. After application of this hierarchical environmentally conscious design methodology, a key design question is how much improvement in environmental and economic performance is realized at key stages in this design

Figure 7. Values of NPV and IPC of benzene process at different design stages.

Figure 8. Values of NPV and IPC of n-butane process at different design stages.

sequence. Figures 7 and 8 show the values of NPV and IPC for both processes at three key design stages: (i) base-case flowsheet, (ii) modified (improved) base case, and (iii) optimum design (AHP objective). As the design proceeds, there are improvements in both environmental and economic performance. IPC for the n-butane process design is decreased by 12% for the modified base-case flowsheet compared to the original base case and then by another 0.4% upon optimization. The explanation for this behavior is that CO, the dominant chemical in IPC, is reduced significantly as a result of early design process improvement (heat integration) steps, but optimization provides negligible additional CO emission reductions from the reactor and from energy consumption. For the benzene process, this same sequence of process improvement steps results in negligible improvement and then a decrease of 7.6% in IPC upon optimization. The reason for this behavior is that heat integration does not reduce benzene emissions, the dominant chemical for IPC in the benzene process, whereas optimization changes the reactor conditions, resulting in higher conversions of benzene. The changes in economic performance (NPV) are more significant for both the n-butane and benzene process designs, and more-or-less continual improvements in NPV occur upon progressing from early to detailed design and optimization. 3.9.2. Accuracy of Early Design Assessment. In light of these detailed design, assessment, and optimization results, we revisit the early design screening economic and environmental indicators to determine accuracy in predicting final design performance. It was shown in the screening assessment of reaction pathways that the n-butane process has lower costs per unit of product than the benzene process: 0.0207 $/mol of MA versus 0.0312 $/mol of MA. This difference is approximately 51% in cost, suggesting that the economic performance of the n-butane process is superior to that of the benzene process. The detailed design and optimization results in Tables 10 (AHP) and 12 (AHP) indicate that the difference in economic performance (NPV) of these processes is 50%, which is agrees closely with the screening assessment. For environmental impacts, when the screening assessment results in

550


Table 1 are converted to IPC, the n-butane process is lower by a factor of 266 compared to the benzene process. In contrast, in the optimized designs in this paper, IPC for n-butane is lower by a factor of 172 compared to that of the benzene process. Thus, although screening economic and environmental assessments correctly predict that the n-butane process is superior to the benzene process, they slightly overpredict the economic and environmental differences between the processes. Nonetheless, the screening assessment method from early design correctly indicates the superior reaction pathway. 3.9.3. Design Guidance and Heuristics. Several observations and design guidance results obtained from this case study are described next. 1. According to the environmental PDS tables, only a small number of chemicals and unit operations are responsible for the majority of the environmental impact of the process. The environmental performance of the process in the MA case study is dominated by the reaction system because either the reactants or byproducts have a relatively high toxicity, which dominates IPC in this case study. In the benzene process, the environmental assessment tends to encourage the design to operate at higher conversions of benzene, where the reactor operates at high temperature and residence time. In the process using n-butane as the feedstock, the emission of CO from incineration of the absorber offgas is the major contributor to IPC. The environmental assessment and optimization forces the design to produce less CO byproduct. Therefore, the process should operate at lower temperature and shorter residence time. 2. According to the economic PDS, the economic performance is dominated by raw material cost, and among various utility costs, the utility cost for the air compressor is the most important if heat integration schemes are implemented. The economic assessment tends to design the process to achieve a maximum yield of MA and reduce the usage of utilities. As in the environmental assessment, the economic performance is dominated by the reaction system. Combining these two observations implies that the upstream process (reactions) is more important than the downstream processes (separations) in evaluating process performance for this case study. The process performance hinges on the reaction system, with the design and operation of the separation system constrained by the upstream reaction step. 3. According to the environmental SGA in both processes, the variables that are identified as being unimportant are related to the separation system operation. This result implies that the most important variables are related to the reaction system, confirming statement 2 above. 4. The most time-consuming step in this hierarchical method for environmentally conscious design is the optimization (about 15 h using a Pentium4 PC for one run of 100 generations). This is the reason for reducing the parameter set for optimization using SGA. The most effective step that will provide the most valuable information for understanding and improving the process performance is the process diagnostic summary (PDS) tables. In this step, the most important process units are identified, the basis for the initial design is examined, and process improvement strategies are proposed. The process improvement suggestions result-

ing from this step for the MA case study include heat integration, alternative utility type, and realistic pollution control schemes. 5. The environmental PDS tables present the risk index of each chemical from each emission source and the contribution of each index to the total environmental impact, IPC. The normalization method using national impact data is the most important step in identifying which of the chemicals and unit operations dominate IPC and, as a result, direct the process improvement strategies. For the benzene process, this normalization step used national benzene emissions from the TRI database, and because of lack of data, additional national carcinogenic compound impacts were not included. National benchmarking studies for carcinogenic effects and other impacts are needed to improve environmentally conscious design. 6. The optimum process design based on the AHP method combining economic and environmental performance is closer to the economic-based optimum. This is due to a higher weighting of economics versus environmental impacts in the decision analysis (AHP). This weighting approach seems realistic because even small reductions in design profitability are not tolerated in industrial practice. 7. The environmental assessment identifies different optimum operating configurations than the economic assessment. This is especially true in the benzene process, where the environmental assessment leads the decision makers to design the process to achieve a higher conversion of raw material. In the n-butane process, it is desired to design the reactor to achieve a higher selectivity. 4. Conclusions The purpose of this paper is to demonstrate a method for performing environmentally conscious chemical process design during both early and later detailed design stages. A case study is presented utilizing this hierarchical design methodology. The case study shows that the process can achieve significant gains in profit and lower environmental impacts by using this design methodology in the early stages of design. For example in the n-butane process, the annual utility cost is greatly decreased from $3,690,041/year in the base case to $726,003/year in the modified base case, while IPC is reduced from 6.13 × 10-4 to 5.40 × 10-4. Some design guidance and design heuristics conclusions are derived on the basis of this case study. 1. A set of guidelines for sequentially conducting environmentally conscious process design is proposed. These guidelines should help engineers to perform similar design assessments on the basis of both economic and environmental considerations. 2. This paper also provides guidelines for conducting decision analysis and multiobjective optimization on the basis of both economic and environmental assessments. These guidelines should help engineers to incorporate environmental considerations effectively into design. 3. The methodologies in this paper utilize assessments that illuminate the effects of specific process units and chemicals on cumulative process performance. This leads logically to process improvement alternatives that can be implemented in design and then evaluated. 4. From this case study, the design of MA production, economic and environmental improvements were observed as a result of both early and later detailed design


assessment activities. However, early design assessment and improvement steps are more easily implemented than later detailed optimization-based assessment and improvement steps, and therefore, these early steps represent targets of opportunity for environmentally conscious design. 5. As shown in Tables 10 and 12, optimizing a chemical process on the basis of either environmental or economic objectives yields very different optimum design conditions for operation. Therefore, it is very important to include both economic and environmental aspects in the design decision to make the tradeoffs between these at times competing design objectives more apparent. As this case study demonstrates, the design methodology and assessment tools presented here are effective in achieving an environmentally conscious process design. Acknowledgment This research is based on work supported by the National Center for Clean Industrial and Treatment Technologies (CenCITT) at MTU and by an NSF/Lucent Technologies Industrial Ecology Research Fellowship (BES-9814504). Acronyms AHP ) analytic hierarchy process CAD ) computer-aided design DORT ) design option ranking tool ECD ) environmentally conscious design EFRAT ) environmental fate and risk assessment tool FCI ) fixed capital investment LCA ) life cycle assessment MA ) maleic anhydride MEIM ) methodology for environmental impact minimization MIPS ) material intensity per service MLI ) mass-loss index NPV ) net present value OLE ) object linking and embedding PDS ) process diagnostic summary tables PP ) payback period SCENE ) simultaneous comparison of environmental and nonenvironmental criteria SGA ) scaled gradient analysis TAC ) total annualized cost UAW ) uniform annual worth WAR ) waste reduction algorithm

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Received for review May 19, 2003 Revised manuscript received October 15, 2003 Accepted October 29, 2003 IE0304356

Systematic Framework for Environmentally Conscious Chemical

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