Environ. Sci. Technol. 2002, 36, 1640-1648
Integrated Decision Support System for Waste Minimization Analysis in Chemical Processes ISKANDAR HALIM† AND RAJAGOPALAN SRINIVASAN* Laboratory for Intelligent Applications in Chemical Engineering, Department of Chemical and Environmental Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260
The need to build and operate environmentally friendly plants has challenged the chemical industry to consider waste minimization or even elimination starting from the early stages of process development. A thorough waste minimization analysis requires specialized expertise and is laborious, time-consuming, expensive, and knowledgeintensive. This has caused a major technical barrier for implementing waste minimization programs within the industry. Previously, we had reported a systematic methodology and a knowledge-based system, called ENVOPExpert, for identifying waste minimization opportunities in chemical processes. In this paper, we propose an integrated qualitative-quantitative methodology to identify waste minimization alternatives and assess their efficacy in terms of environmental impact and process economics. A qualitative analysis is first conducted to identify the sources of wastes and to propose alternatives for eliminating or minimizing them. Environmental impact of each alternative is then calculated by doing a quantitative pollutant balance. The capital expenditure required for implementing the alternative and the resulting plant operating costs are also calculated and used in the evaluation of the waste minimization alternatives. Through this, practical and costeffective options can be identified. This methodology has been implemented as an integrated decision support system and tested using the hydrodealkylation process case study with satisfactory results.
Introduction The issue of clean production has challenged the chemical industries to initiate new approaches to tackle pollution problems. The traditional end-of-pipe treatment approach is no longer viewed as an adequate, stand-alone, pollution problem solver. Increasing public awareness of the impact of industrial pollution, more stringent discharge standards, and escalating waste treatment and disposal costs have placed enormous pressure on the chemical industries to shift their paradigm of pollution prevention from the end-of-pipe treatment to waste minimization or even total elimination at the point of generation. Numerous techniques and methodologies for pollution prevention have been published in the literature. In the broadest sense, all of these available techniques can be classified into qualitative and quantitative approaches. In * Corresponding author e-mail:
[email protected]; telephone: +65 8748041; fax: +65 7791936. † Present address: Environmental Technology Institute, Innovation Centre, Block 2, Unit 237, 18 Nanyang Dr., Singapore 637723. 1640
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the qualitative approach, methods such as Environmental Optimization (ENVOP) and Douglas’ hierarchical procedure are used to identify possible waste minimization alternatives. ENVOP technique is a waste minimization procedure that follows the approach of Hazard and Operability (HAZOP) analysis in process safety (1). During an ENVOP study, each process line and unit operation is analyzed to identify potential waste minimization alternatives that meet the desired environmental objectives. These alternatives are derived by combining a set of qualitative guidewords (such as more, less, etc.) with process variables (such as pressure, temperature, flow rate, etc). In Douglas’ procedure (2), the hierarchiacal decision structure for process design, developed by Douglas (3), is extended to incorporate potential strategies to reduce waste generation right from the early stages of design. The basic waste minimization solutions that can be derived through this procedure can be summed up as “changing the chemistry”, “changing the process”, “changing the equipment”, “changing the solvent”, and “reuse and recycle of the material”. The quantitative approach to waste minimization involves changing the process operating conditions or synthesizing a process structure through numerical optimization or by using a process simulator. Hopper et al. (4) demonstrated the opportunity for minimizing wastes through gradual changes in the reactor and separator variables, using a process simulator. Cabezas et al. (5) used a methodology called the WAste Reduction (WAR) algorithm for quantifying the potential environmental impact of chemical processes. In their approach, the WAR algorithm is used in conjunction with a process simulator to evaluate the environmental impact of process modifications. Young and Cabezas (6) extended the WAR methodology to incorporate the environmental impact of energy generated and consumed within the process. WAR-based environmental analysis can be combined with economic evaluation of a process design as demonstrated by Fu et al. (7). In their approach, a coupling between the economic and the environmental objectives of the process was presented as a multi-objective optimization problem and then solved using stochastic modeling and Pareto set. Dantus and High (8) formulated a multi-objective optimization problem that simultaneously targets maximizing profit while minimizing environmental impact and solved this by combining compromise programming with simulated annealing. One common shortcoming of the above-mentioned quantitative approaches is due to the complexities involved in modeling industrial-scale process with a large number of interconnections between the streams and the units. The resulting optimization problem is usually quite large and difficult to solve. Another shortcoming arises from the fact that these techniques require considerable skill and expertise in a number of areas. Recognizing each unit and variable within the process that contributes to waste generation in the process is challenging and requires deep insight into the process and its operations. The numerical techniques used to identify and evaluate the process modifications also require considerable know-how of the specific techniques and their nuances that is seldom available in chemical plants. In this paper, we address this important problem of industrial significance by developing a methodology for identifying process modifications that reduce waste generation and are economically prudent. We have previously developed an intelligent system, called ENVOPExpert, for qualitative waste minimization analysis (9-13). ENVOPExpert is broadly based on the ENVOP 10.1021/es0155175 CCC: $22.00
2002 American Chemical Society Published on Web 02/19/2002
FIGURE 1. Waste minimization assessment and the role of ENVOPExpert. technique and uses expert knowledge to automatically identify the source of wastes in a chemical process and propose process changes to eliminate or minimize them. ENVOPExpert has been tested on a number of industrialscale processes including a hydrocarbons separation process and a chemical intermediate manufacturing process and was found to generate results comparable to the analysis by human experts (12, 13). In this paper, we propose a framework for synthesizing waste minimization alternatives through the integration of ENVOPExpert with quantitative environmental impact and process economics analysis. The outline of the rest of this paper is as follows: Next, we provide overviews of ENVOPExpert and the environmental impact calculation using the WAR algorithm. In the next section, we propose the integrated qualitative-quantitative methodology for identifying waste minimization alternatives and evaluating them based on WAR and process economics. The integrated framework is illustrated in the last section through a case study involving the hydrodealkylation (HDA) process for manufacture of benzene from toluene.
Waste Minimization Methodology In 1988, the U.S. EPA published a document that established a systematic procedure for performing waste minimization assessment in process plants (14). The procedure involves the following steps (see Figure 1): (i) Planning and organization: to establish waste minimization goals, objectives, and tasks. (ii) Assessment phase: where the evaluation team is organized, plant and waste data collected, plant operations reviewed, and options for minimizing wastes generated.
(iii) Feasibility analysis phase: when options are screened on the basis of technical and economic feasibility. (iv) Implementation phase: where the most promising options are implemented and their performance evaluated. A thorough waste minimization assessment is thus laborious, time-consuming, expensive, knowledge-intensive, and requires specialized expertise of the team. This has caused a major technical barrier for implementing waste minimization within the industry. An intelligent system that can automate waste minimization analysis would certainly be beneficial since it can perform a systematic and thorough evaluation of waste minimization options and reduce the team’s time and effort. Such a system must be capable of first identifying waste sources that arise in the process, assisting the nonexpert in terms of possible suggestions that eliminate or minimize the waste sources and then highlighting suggestions that are both environmentally friendly (in terms of impact on environment) and cost-effective. We have previously developed a waste minimization methodology that is amenable to automation (12). This methodology employs a two-step procedure: (i) waste detection and diagnosis and (ii) waste minimization options generation. A detailed description of this methodology is presented in the Supporting Information. This methodology has been implemented as a knowledge-based expert system, called ENVOPExpert, using Gensym’s expert system shell G2. ENVOPExpert is capable of providing technical and decision support for identifying waste minimization alternatives (see Figure 1). The qualitative analysis implemented in ENVOPExpert offers a number of unique advantages. Since waste minimization domain expertise is incorporated into ENVOL. 36, NO. 7, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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VOPExpert, it obviates the need for specialized expertise and makes it easy to use. The knowledge library, which is currently built using literature solutions and design heuristics, can be continuously updated with new knowledge. The systematic methodology embedded in ENVOPExpert also makes it applicable to different stages of the process life cycle from conceptual design to retrofitting. However, the inherent nature of the ENVOPExpert’s qualitative analysis also leads to a number of shortcomings. It is possible for the qualitative analysis embedded in ENVOPExpert to generate inconsistent results if counteracting process phenomena are present. For example, increasing temperature inside a reactor to reduce the generation of a particular waste byproduct may increase the formation of another waste byproduct in a downstream unit or decrease the production of useful product. ENVOPExpert also does not account for the impact of process modifications on the process economics. These shortcomings can be addressed by incorporating additional quantitative knowledge about the process into the analysis. WAR Algorithm. The WAR algorithm is perhaps the most practical environmental impact calculation tool accomplished to date (15). The WAR algorithm was first developed by Hilaly and Sikdar (16), who introduced the concept of pollution balance based on the mass balance of pollutants. Cabezas et al. (5) later improved the original WAR algorithm and developed a generalized WAR algorithm based on the potential environmental impact (PEI) balance of pollutants. From the PEI balance calculations, a relative indication of the environmental friendliness of the chemical process can be obtained. The following are some of the key components of the WAR algorithm. In the WAR algorithm, a potential environmental impact (I˙ ) of a chemical k in a nonproduct (NP) stream of j of a process is expressed as
I˙ NP ) M ˙ jx NP kj ψk
(1)
where M ˙ j is the mass flow rate of stream j, x NP kj is the mass fraction of chemical k in the nonproduct stream j, and ψk is defined as the overall potential environmental impact of chemical k, which is developed using the following expression:
ψk )
∑R ψ l
s k,l
(2)
l
where Rl is a relative weighting factor for impact category s type l independent of chemical k, and ψk,l is the specific potential environmental impact of chemical k for an environmental impact type l, which includes the following categories: global warming, ozone depletion, acid rain, smog formation, human toxicity, aquatic toxicity, and terrestrial toxicity. Impact scores ψk of several chemicals used in the production of ammonia, methyl ethyl ketone, acrylic acid, and benzene have been quantified using this expression (57). On the basis of steady-state balances, the environmental impact of any processes can be written as follows:
I˙ gen ) I˙ out - I˙ in
(3)
where I˙ in is the input impact rate of stream entering the system, I˙ out is the output impact rate of stream leaving the system, and I˙ gen is the rate of impact generation by the system. For a balance involving only the nonproduct (NP) streams, the following analogous equation can be written: NP NP I˙ gen ) I˙ out - I˙ NP in
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(4)
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Using the terminology in eq 1 and the impact balance of eq 4, the potential environmental impact I˙ gen generated by the nonproduct stream can thus be described as NP ) I˙ gen
∑M˙ ∑x out j
j
NP kj ψk
∑M˙ ∑x in j
-
k
j
NP kj ψk
(5)
k
To take into account the product stream of the process, an index ˆI NP gen is introduced as follows: NP ˆI gen )
NP I˙ gen
∑P˙
(6)
p
p
where ˆI NP gen is a measure of the potential impact created by all nonproduct streams in producing the products P˙ . The interested reader is referred to Cabezas et al. (5) for a detailed description of the WAR algorithm. The WAR algorithm provides a metric for the environmental friendliness of a process. It can also be used to evaluate process modifications for their environmental impact. One drawback of the WAR algorithm arises due to the difficulty, ambiguity, and subjectivity involved in combining the different impacts generated by the process into a single value, ψk. Also, the WAR algorithm does not directly provide any guidance on the actual origin of the waste in the process or the modifications that would minimize the waste. WAR should therefore be used with methods for generating process alternatives.
Integrated Qualitative-Quantitative Framework In this paper, an integrated framework that combines the waste identification and alternative generation capabilities of ENVOPExpert with the quantitative alternative assessment of the WAR algorithm is proposed. The framework comprises of the following steps, as shown in Figure 2: (i) Base-case process flowsheet simulation using a process simulator. (ii) Environmental impact calculation using WAR algorithm and process economic analysis. (iii) Qualitative waste minimization analysis using ENVOPExpert to generate alternatives. (iv) Modification to the base process based on the proposed alternatives. (v) Comparison between the modified and the base-case process in terms of environmental impact and economics. Initially, the steady-state material and energy balances of the process are performed using a process simulator with the main objective of calculating the flow rates of each waste stream in the process. We have used the HYSYS simulator (17) for this purpose, although other commercial simulators could also be used. On the basis of the simulation results, the PEI contributed by each waste stream is calculated using the WAR algorithm to obtain the overall environmental impact of this base-case process against which all process modifications can be compared. The costs for this base-case process are also calculated. In the next step, the process is qualitatively analyzed to derive alternatives that eliminate or minimize the waste generated within the process. This is done using ENVOPExpert, which contains various heuristic rules, procedures, and P-graph, signed digraph, and functional models as described below (see Figure 3). First, the sources of each material component that make up the waste stream are identified. The P-graph representation of a process provides a convenient framework for diagnosing the origins of waste in the process and for deriving top-level waste minimization alternatives. Starting from each waste stream and tracing upstream using the P-graph model, sources of waste, such as impurities in inlet stream, useful
FIGURE 2. Integrated qualitative-quantitative waste minimization framework. material transformed at low conversion rate, waste byproduct produced from reaction or phase change phenomena and ineffective separation of useful material, are identified. Once the waste sources are detected, ways to eliminate them are proposed. For each waste origin, top-level waste minimization alternatives (such as remove impurities, optimize the reactor unit, improve the separation in the separator unit,
and recycle waste stream) that identify the broad modification required in the process unit or feed material to minimize waste are obtained through the use of domain knowledge embedded in the form of heuristic rules. The broad suggestions from the P-graph analysis are distilled further using digraph and functional knowledge to derive detailed alternatives. A digraph model represents the cause-and-effect VOL. 36, NO. 7, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. ENVOPExpert ’s algorithm. interactions among the different variables in each process unit and their influence on the underlying physiochemical phenomena. In ENVOPExpert, the top-level alternatives generated by the P-graph heuristics are directly translated to conclude a value for the phenomena nodes in the process unit digraphs. Detailed alternatives are identified by propagating this value from the phenomena node to the other nodes in the digraph. To link the digraph models of different process units, a functional model of the process is used. 1644
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Detailed description of the methodology including the P-graph, digraphs, and functional models is provided in the Supporting Information. The final results from ENVOPExpert are a series of waste minimization alternatives that cover basic suggestions such as materials substitution, stream recycling, change of process chemistry, and optimizing certain unit’s variables of the process. These alternatives have to be assessed in detail using quantitative techniques to improve the base-case process.
FIGURE 4. Base-case flowsheet of HDA process. The following step involves implementing the proposed alternatives to the base-case process to evaluate their efficacy. In our approach, process modifications are implemented following Douglas’ hierarchical decision procedure (3) and cover the input-output structure of the process, reactor system, and separator system. [Decisions related to heat exchanger network have not been considered explicitly in the current work and is the subject of our future work.] At each hierarchical level, the environmental and the economic impacts of the modification are calculated to measure the feasibility of the proposed alternatives. The WAR algorithm is adopted for comparing the degree of environmental friendliness of each alternative applied to the base process. Process economics are measured using the overall profit after implementing the alternatives. This profit factor incorporates the required investment (capital and operating costs) and the resulting production rates. It might be necessary at this stage to search for the optimum process condition that considers the tradeoff between production and waste generation, that is, profit and environmental impact. For this purpose, an optimization tool, such as the HYSYS optimizer embedded inside HYSYS simulator, can be utilized to search for the conditions that favor the waste minimization objective of the process. In the context of waste minimization, this objective may be translated as minimize the concentration of waste byproduct generated inside a reactor, optimize the separation inside a separator, etc. These optimizations would be achieved by manipulating the operating conditions in the unit subject to equipment and process constraints. For example, in a reactor, changes in reactant concentration, pressure, and temperature
may be necessary to minimize the concentration of a waste byproduct or maximize the reaction selectivity. Similarly for a distillation unit, the tradeoff between increased reflux ratio, increased energy requirement, and improved separation efficiency could be investigated.
Case Study: Hydrodealkylation of Toluene to Benzene To illustrate the integrated framework, we describe its application to a case study involving HDA of toluene to benzene. This case study had been previously used by Fu et al. (7) to illustrate their multi-objective optimization framework for waste minimization. Figure 4 shows the flowsheet of this process as adapted from Douglas (3). Fresh toluene and hydrogen are initially mixed with a recycle stream containing hydrogen, methane, benzene, and toluene. The feed mixture is heated in a furnace to about 641 °C before being passed to an adiabatic reactor for the HDA reaction. In the reactor, toluene and hydrogen react to form the benzene product. This main reaction is accomplished by the generation of methane byproduct and diphenyl waste. The reactor effluent containing unreacted hydrogen, toluene, and reaction products is condensed using cooling water in a cooler. This is followed by separation in a flash separator to remove the aromatics from non-condensable hydrogen and methane. The vapor stream leaving the top of the separator contains a significant amount of methane and hydrogen. A fraction of this vapor is purged as saleable byproduct while the rest of the stream is recycled and mixed with the raw materials. The liquid from the flash separator is split into two streams. The first stream (with about 26% of the liquid) is mixed with the reactor effluent stream and recycled back VOL. 36, NO. 7, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Cost Basis for the HDA Processa unit
cost
product and byproduct benzene productb methane purge hydrogen purge raw material hydrogen raw material toluene raw material utility stream cooling water fuel electricity equipment (fixed charges) compressor stabilizer column benezene column toluene column furnace reactor
$19.90/kmol $3.37/kmol $1.08/kmol $2.50/kmol $14.00/kmol $8 000 000/kJ $700 000/kJ $4 000 000/kJ $0.05/kWh annual basis $7155 + 815 × power (kW) $650 + 1000 × no. of trays $16 300 + 1550 × no. of trays $3900 + 1120 × no. of trays $34 500 + 1.172 × heat duty (kJ/h) × 10-6 × operating hour/yr $74 300 + 1257 × vol (m3)
Profit ) ∑product and byproduct - ∑raw material - ∑utility ∑equipment charges. b>95% mol. a
to the cooler. The second stream is passed through a series of distillation columns (stabilizer, benzene, and toluene columns) to separate the benzene product from the other components. The benzene product is obtained at 99% (mol) purity. A feed-recycle stream from the toluene column, which contains high purity toluene, is sent to a storage tank. There are two waste streams in this process: diphenyl from the bottom of the toluene column and the small amount of vapor from the top of the stabilizer column and containing mainly methane and benzene. The process currently produces benzene at the rate of 7762 kg/h, and the profit is $ 437 000 per annum as calculated using the unit cost basis shown in Table 1 (7). Let us assume that the process plant built upon this flowsheet has been in existence and we are interested in retrofitting the process to minimize wastes without adversely affecting process economics. Following the integrated methodology, we first use ENVOPExpert to derive the qualitative
waste minimization alternatives shown in Table 2. Hence, quantitative analysis based on those alternatives was investigated using HYSYS simulator with the objective of minimizing the environment impact while keeping the overall process profit on focus. Table 3 shows the potential enviNP ronmental index of the base process in terms of I˙ NP out, I˙ gen, NP NP NP ˆI NP , and ˆ I . The negative values of I ˙ and ˆ I are out gen gen gen obtained since some of the input materials are converted to products and products are not included in the impact calculation (see eqs 5 and 6). The low ˆI NP out significantly means that the process has a considerably higher amount of raw materials transformed into products, i.e., low percentage of waste material as compared with the amount of products being produced. Because of space limitations, we illustrate only some of the important ENVOPExpert’s alternatives using the quantitative analysis. Input-Output Level. At this level, ENVOPExpert proposes three alternatives: (i) direct recycle or recovery-recycle of the useful component from the waste streams, (ii) use alternative feed instead of hydrogen and toluene, and (iii) decrease the flow rate of hydrogen and toluene streams. The first alternative, direct recycle of waste vapor stream back to the furnace, has been evaluated, and the results are shown in Table 3. Compared with the base process, this option leads to a small increment (2%) in I˙ NP out. This is mainly due to the slight increase of the waste quantity in the downstream diphenyl waste stream. However, the profit increases by 14%, thus making this alternative attractive to implement. The second alternative, use of a different process chemistry, requires changing the entire process. This would entail major modifications to the current process and is therefore rejected as unsuitable. The third alternativesdecrease hydrogen and toluene flow ratessmeans reduced benzene throughput, and since this is in conflict with the production objective, it is also rejected. Reactor Level. At the reactor level, the alternatives can be broadly expressed as “optimize the reactor’s variables to increase the conversion of hydrogen and toluene and reduce the diphenyl production”. Examination of the reactor unit indicates that the diphenyl byproduct is formed from an exothermic reaction. In this case, one of the ENVOPExpert’s suggestions is “decrease the furnace temperature”, which
TABLE 2. Qualitative Waste Minimization Alternatives of HDA Process level of hierarchy
unit or stream
input-output hydrogen feed stream toluene feed stream waste vapor reaction
separation
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waste minimization alternative
prevent excessive feed of hydrogen component in hydrogen stream use alternative material in hydrogen stream prevent excessive feed of toluene component in toluene stream use alternative material in toluene stream direct recycle or recovery-recycle of hydrogen, methane, and benzene component from waste vapor diphenyl waste direct recycle or recovery-recycle of benzene and toluene component from diphenyl waste reactor add recovery system reactor after reactor to recover toluene and hydrogen component and recycle them back to reactor add new reactor after current reactor to further transform toluene and hydrogen component leaving reactor optimize reactor operating variables to increase conversion of toluene and hydrogen component inside reactor furnace decrease temperature of furnace to optimum temperature to start reaction hydrogen stream decrease temperature of hydrogen stream to optimum temperature to start reaction toluene stream decrease temperature of toluene stream to optimum temperature to start reaction stabilizer optimize reflux ratio and other operating variables of stabilizer improve control system of stabilizer use further separation system after stabilizer to recover useful benzene benzene column optimize reflux ratio and other operating variables of benzene column improve control system of benzene column toluene column optimize reflux ratio and other operating variables of toluene column improve control system of toluene column use further separation system after stream-splitter to recover useful toluene and benzene
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 36, NO. 7, 2002
TABLE 3. Environmental and Economic Analysis of HDA Process environmental impact (impact h-1) waste minimization alternative
I˙ NP out
I˙ NP gen
ˆI NP out
ˆI NP gen
base-case input-output level direct recycle of waste vapor stream reactor level decrease furnace temp from 641 to 632 °C increase furnace temp from 641 to 650 °C separator level increase reflux ratio in toluene column from 1.1 to 1.5 decrease reflux ratio in stabilizer column from 1.0 to 0.2 increase reflux ratio in benzene column from 1.0 to 2.0 decrease reflux ratio in stabilizer column from 1.0 to 0.2 and increase reflux ratio in toluene column from 1.1 to 1.5
400
-19 139
0.04
-1.85
437 000
408
-19 129
0.04
-1.85
496 000
518 633
-19 020 -18 865
0.05 0.06
-1.89 -1.75
414 000 572 000
387 391 1018 377
-19 500 -19 147 -18 519 -19 160
0.04 0.04 0.10 0.04
-1.85 -1.84 -1.86 -1.84
442 000 468 000 -333 000 473 000
would in turn reduce the reaction temperature. The simulation results show that reducing the furnace temperature from 641 to 632 °C lowers the diphenyl formation by 17%. However, this was accompanied by decreased benzene production and efficiency in the downstream separation process. As can be seen from Table 3, the potential environmental impact of the process as given by I˙ NP out rises by 30%, and the overall profit reduces by 5% as compared to the base process. This alternative is therefore not suitable for implementation. Separator Level. The feasible suggestions at this level can be summed up as “optimize the separator’s variables to reduce the presence of undesirable materials in each waste stream”. Here, three design changes to the separators’ reflux ratio are considered. Examination of the waste streams shows that the presence of toluene in the diphenyl waste stream poses a significant contribution to the overall environmental impact of the process. Thus, reducing the presence of toluene by increasing the reflux ratio of the toluene column was simulated. The results showed that increasing the reflux ratio of the toluene column from 1.1 to 1.5 reduced the I˙ NP out of the process from 400 to 387. This was accompanied with increased toluene recovery in the feed-recycle stream thus improving the process profit, in this case by 1%. The other contributor to the environmental performance of the process is the benzene in the waste vapor stream. It is therefore desirable to reduce the presence of benzene in this stream by reducing the reflux ratio of the stabilizer. In fact, decreasing the reflux ratio from 1 to 0.2 reduced the I˙ NP out of the process by 2% and increased the profit by 7% through the energy saving of the column. Both of these alternatives are thus feasible for implementation. Increasing the reflux ratio of the benzene column will eventually increase the amount of benzene product recovered. However, the simulation results already showed a high benzene recovery at the current reflux ratio. An increase in the reflux ratio for this column was carried out from 1.0 to 2.0. The results showed a very small increment in the benzene production rate but required much higher energy consumption, thus making the process lose profitability. Interestingly, higher reflux ratio in the benzene column severely affected the separation efficiency of the downstream toluene column. This is shown in Table 3 as an increase in the environmental impact from 400 to 1018. Consequently, this alternative is also rejected. It is also logical to investigate the effect of simultaneously decreasing the stabilizer’s reflux ratio and increasing the reflux ratio of the toluene column. Simulation results show that the I˙ NP out of the process was lowered by 6% and the profit increased by 8%. It is apparent that this alternative is most optimal as compared with the previous alternatives since it yields the lowest environmental impact and the highest profit.
profit ($)
This shows that implementing waste minimization does not necessarily reduce the profitability of the process. Instead, there is an economic incentive in implementing waste minimization to a process. However, an interesting question that naturally arises is the following: Is the profit from implementing this alternative the maximum as compared to all the modifications possible for the process? To investigate this, an increase in the furnace temperature was simulated. As expected, the results showed that increasing the furnace temperature, in this case from 641 to 650 °C, resulted in an increase in the environmental impact of the process (I˙ NP out ) 633). But this alternative showed a higher profit ($572 000) than the ones previously obtained. This highlights the tradeoff between environmental impact and process profitability for the HDA case study. Optimal stream flow rates from the benzene column that simultaneously minimize the environmental impact and maximize the process profit was examined using the HYSYS′ optimizer tool. The result shows a slight improvement both in the environmental impact (I˙ NP out ) 395) and the profit ($439 000) as compared to the base process. The above case study illustrates the following advantages of the integrated framework: a qualitative waste minimization analysis using ENVOPExpert is a practical, fast, and comprehensive approach to identify and generate feasible alternatives for implementation in the process; a quantitative approach using process simulator and the WAR algorithm is helpful in evaluating each of the alternatives on the basis of cost and environmental impact analysis. Through the implementation of this framework, the usually large, difficult, and expensive nonlinear optimization problem for solving waste minimization of the entire plant can be replaced by smaller optimization problems that focus only on the relevant section of the process. Another advantage is that designers can also take into account other important nonquantifiable factors such as safety, plant layout, etc. while deciding the best process modification. The case study results also indicate that the qualitative analysis embedded in ENVOPExpert can guide the nonexpert in identifying waste minimization alternatives and establish the integrated methodology as a practical approach to material waste minimization in largescale chemical processes. Encouraged by these positive results, we are currently working on improving the usability of ENVOPExpert. The current implementation requires the user to switch between the G2-based ENVOPExpert and the simulation in HYSYS. Recent developments in Computer Aided Process Engineering-Open Simulation Environment (CAPE-OPEN) have enabled collaboration between different process engineering software through data and property sharing capabilities (18). We intend to develop an integrated solution with ENVOPExpert seamlessly communicating with any CAPE-OPEN compliant process simulator and using the VOL. 36, NO. 7, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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results for WAR and process economics calculations. This will significantly enhance the ease of use of ENVOPExpert as the “green design” decision support tool. In addition, we will also augment the knowledge base in ENVOPExpert and extend our methodology to energy optimizations.
Supporting Information Available Sequence of procedures (including figures) employed by ENVOPExpert. This material is available free of charge via the Internet at http://pubs.acs.org.
Literature Cited (1) Isalski, W. H. Environ. Prot. Bull. 1995, 34, 16-21. (2) Douglas, J. M. Ind. Eng. Chem. Res. 1992, 31, 238-243. (3) Douglas, J. M. Conceptual Design of Chemical Processes; McGrawHill: New York, 1988. (4) Hopper, J. R.; Yaws, C. L.; Vichalak, M.; Ho, T. C. Waste Manage. 1994, 14 (3), 187-202. (5) Cabezas, H.; Bare, J. C.; Mallick, S. K. Comput. Chem. Eng. 1999, 23, 623-634. (6) Young, D. M.; Cabezas, H. Comput. Chem. Eng. 1999, 23, 14771491. (7) Fu, Y.; Diwekar, U. M.; Young, D.; Cabezas, H. Clean Prod. Processes 2000, 2, 92-107. (8) Dantus, M. M.; High, K. A. Ind. Eng. Chem. Res. 1996, 35 (12), 4566-4578.
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(9) Halim, I.; Srinivasan, R. An intelligent system for identifying waste minimization opportunities in chemical processes. In European Symposium on Computer Aided Process Engineering - 10; Pierruci, S., Ed.; Elsevier Science: Amsterdam, 2000; pp 829-834. (10) Halim, I.; Srinivasan, R. AIChE Annu. Meeting, Los Angeles, CA, November 12-17, 2000; Paper 233r. (11) Halim, I.; Srinivasan, R. Regional Symposium on Chemical Engineering (RSCE), Singapore, December 11-13, 2000; Paper PDD6.3. (12) Halim, I.; Srinivasan, R. Ind. Eng. Chem. Res. 2002, 41, 196-207. (13) Halim, I.; Srinivasan, R. Ind. Eng. Chem. Res. 2002, 41, 208-219. (14) U.S. EPA. Waste Minimization Opportunity Assessment Manual; U.S. Environmental Protection Agency, Hazardous Waste Engineering Research Laboratory: Cincinnati, OH, 1988. (15) Yang, Y.; Shi, L. Comput. Chem. Eng. 2000, 24, 1409-1419. (16) Hilaly, A. K.; Sikdar, S. K. J. Air Waste Manage. Assoc. 1994, 44, 1303-1308. (17) Hyprotech. HYSYS.Plant, Ver. 2.1; Hyprotech: Calgary, Canada, 1999. (18) Braunschweig, B. L.; Pantelides, C. C.; Britt, H. I.; Sama, S. Chem. Eng. Progr. 2000, September, 65-76.
Received for review May 10, 2001. Revised manuscript received December 19, 2001. Accepted December 20, 2001. ES0155175
