208
Ind. Eng. Chem. Res. 2002, 41, 208-219
Systematic Waste Minimization in Chemical Processes. 2. Intelligent Decision Support System Iskandar Halim† and Rajagopalan Srinivasan* Laboratory for Intelligence Applications in Chemical Engineering, Department of Chemical and Environmental Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore
In part 1 of this paper, a systematic methodology for a waste minimization analysis applicable to any chemical processes is proposed. In part 2, we present the architecture of ENVOPExpert, an expert system that implements the methodology. Given information concerning the process in the form of a flow sheet, process chemistry, and material information, ENVOPExpert can automatically detect the waste components in the process, diagnose the sources where they originate, and suggest intelligent design alternatives to eliminate or minimize them. ENVOPExpert consists of waste minimization domain knowledge organized into process-general waste minimization knowledge and process-specific information. The inference engine contains algorithms for qualitative simulation, waste diagnosis, and alternatives generation, which use the process-specific information as a basis to perform the analysis. ENVOPExpert also assists the user to screen and rank the various alternatives using a waste minimization index. The application of ENVOPExpert to an industrial case study is illustrated, and the results obtained are compared to the available experts’ solutions. 1. Introduction The issue of pollution prevention within chemical industries has evolved significantly over the past decades. The end-of-pipe approach, which has traditionally been the chief solution to pollution prevention, is no longer viewed as the complete problem solver. The need to build environmentally friendlier plants has challenged the chemical industries to consider minimization or even elimination of wastes starting from the early stages of process development. In practice, a waste minimization study is done by a team of experts in a two-stage review. During an initial brainstorming session, the team would assess the different parts of the process and derive several possible alternatives to reduce the waste generated within the process. Because source reduction gets a higher priority, it is explored first before recycling options are considered. All of the alternatives are evaluated in the next stage of the study, which is usually done on the basis of technical and economical feasibilities. Through this, the most feasible alternatives are short-listed and implemented in the plant. A thorough waste minimization analysis requires specialized expertise and is laborious, time-consuming, expensive, and knowledge-intensive. This has caused a major technical barrier for implementing a waste minimization program within the industry. An intelligent system that can automate a waste minimization analysis would certainly be beneficial because it can perform a systematic and thorough evaluation of waste minimization options and reduce the team’s time and effort. In part 1 of this paper,1 we propose a systematic methodology for synthesizing a waste minimization * To whom correspondence should be addressed. E-mail:
[email protected]. Tel: +65 8748041. Fax: +65 7791936. † Current affiliation: Environmental Technology Institute, Singapore.
analysis generic to any chemical processes. In part 2, an intelligent decision support system called ENVOPExpert that implements the methodology is presented. ENVOPExpert has been built using Gensym’s G2 expert system shell with the following task: given a flow diagram and process chemistry of a chemical process plant, the goal is to identify opportunities to minimize process waste generated in the plant. This makes ENVOPExpert applicable to various stages of the process life cycle, from conceptual design to retrofitting. ENVOPExpert also assists the user in deciding between the various waste minimization alternatives by screening and ranking their indices according to investment cost, type of implementation, payback time, and percentage of waste reduction. The outline of this paper is as follows: In the next section, a screening and ranking approach commonly used in waste minimization studies is described. ENVOPExpert and its major components, including the knowledge organization and inference algorithms, are described in section 3. The application of ENVOPExpert to an industrial case study involving a chemical intermediate process is illustrated, and the results are discussed in section 4. 2. Screening and Ranking Approaches for Waste Minimization The two stages of the waste minimization review are individually complex as explained here. While legal requirements increasingly necessitate a detailed assessment of the impact of waste emissions, the large number of waste streams typically encountered in a chemical process plant makes assessment of every individual waste stream very difficult. To overcome this, each waste stream in the process is first screened on the basis of a set of selection criteria, including2 (1) the quantity and frequency of the waste stream, (2) cost of managing the existing waste stream, (3) possible regulatory impact
10.1021/ie0102089 CCC: $22.00 © 2002 American Chemical Society Published on Web 12/15/2001
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002 209 Table 1. Pollution Prevention Index of Smith and Khan4 criteria
weight
activity
index value
pollution prevention type source reduction (SR) recycling (R) waste treatment (WT)
1011 1010 109
ease of implementation (EI)
108
procedure change retrofit equipment new equipment higher purity solvent material substitution
5 4 3 2 1
percentage of waste reduction (PR)
105
0-100%
1
capital cost (CC)
104
no cost low ($150,000)
5 4 3 2 1
payback (PB)
103
depth of the solution (DS)
1
1 1 1
0-9 years company case study EPA case study consultant report other option
1 1000 100 10 1
waste minimization index WI ) SR × 1011 + R × 1010 + EI × 108 + PR × 105 + CC × 104 + (9 - PB) × 103 + DS
in the future, (4) safety and health risks to the employees and the community, (5) ease and cost of implementing pollution prevention options, and (6) demonstrated effectiveness of pollution prevention options. The index value is calculated for each waste stream on the basis of these criteria. It may also be desirable to assign different weights to each criterion. A criterion considered more important can be given a higher weight and would, thus, affect the index the most. The overall “waste index” of a waste stream is calculated as the weighted sum of the individual criterion values assigned to the stream. The important waste streams in the process would be flagged through their high index values and would be the ones analyzed further in the second stage. The next stage involves the analysis of each of the important waste streams for potential waste minimization alternatives. The ENVOP technique3 may be used at this stage to target minimization or complete elimination of these streams. The reader is referred to part 1 of this paper for a detailed discussion of the ENVOP technique. Once all of the waste minimization options for all of the important waste streams have been identified, they have to be compared by taking into account factors such as cost ease of implementation, efficiency, and so forth. An indexing procedure can be applied at this stage to screen and rank the proposed waste minimization alternatives by referencing them to the following criteria:2 (1) priority in the waste management hierarchy, in which source reduction is favored over recycling, (2) percentage of waste reduction that can be achieved, (3) current treatment and disposal costs, (4) ease of implementation, (5) proven performance, (6) safety and health risks, and (7) quantifiable results. Feasible waste minimization alternatives identified by their high index values would be implemented in the process. Smith and Khan4 proposed an index made up of eight weighted factors for ranking pollution-prevention solutions (see Table 1). In their index, at the highest level, all of the pollution prevention options are classified into source reduction, recycling, or waste treatment. Because source reduction is superior to recycling and waste
treatment, it is assigned the highest weight of 1011. Recycling is assigned a weight of 1010 and waste treatment 109. The other five criteria used in the calculation are described in terms of the effort required to implement the alternative (ease of implementation), percentage of waste reduction that can be achieved, capital-cost requirements, payback time, and the scale on which the solution has been previously reported (depth of solution). Under the ease of implementation criterion, a procedural change is the easiest to implement and, thus, is assigned the highest index value. Retrofitting existing equipment is assigned the second highest value under this category because it is simpler than adding new equipment. Changing to a higherpurity solvent and material substitution generally require major modifications to the processes and are assigned lower index values. A higher waste reduction percentage, lower capital-cost requirement, and shorter payback time naturally receive higher index values in proportion to their associated quantitative values. Under the depth of solution category, a company case study is the most preferred, followed by an EPA case study, a consultant report, and literature solutions, in that order. This is because the company and EPA case studies are the most credible because of their actual implementation in a real process plant. They are, thus, assigned higher weights followed by the consultant report and solutions from literature. There are several shortcomings associated with the use of an index rating procedure.2 First, assigning an index or weight to each of the criteria on the perceived importance is subjective, and the results heavily depend on the choice of the criterion values and their weights. Second, while it is generally clear when very high or very low values should be assigned for a criterion, for intermediate cases where the values lie in between, the assignment is not straightforward. Despite these shortcomings, the index is crucial in screening and ranking the waste minimization alternatives for large and complex process plants. We have adapted Smith and Khan’s index for the purpose of ranking waste minimization alternatives. Our waste minimization index (WMI) comprises five
210
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002
Figure 1. Architecture of ENVOPExpert.
different weighted factors. We include all of the criteria given by Smith and Khan4 with the exception of the depth of solution category. At the highest level, all waste minimization alternatives can be categorized as either source reduction or recycle, with source reduction given a higher index value. The waste treatment criterion is excluded because such an option is not part of waste minimization activity. We have also adopted the criteria weight as given by Smith and Khan4 in our index. These weights and the criterion values are not fixed, and the user can vary them to suit the needs of the situation. To illustrate the WMI calculation, consider a waste minimization example involving “direct recycle of a waste stream containing a useful component X from a unit Y”. Assume that this alternative successfully reduces the overall waste quantity by 100%, requires an investment of $100,000 for new equipment and piping costs, and has a 1-year payback time. The waste minimization index for this alternative can be calculated as
WMI ) 1 × 1010 + 3 × 108 + 100 × 105 + 2 × 104 + (9 - 1) × 103 ) 10 310 028 000 The waste minimization index can be integrated as part of the systematic waste minimization methodology. 3. ENVOPExpert: Decision Support System for Waste Minimization The waste minimization procedure discussed in part 1 of this paper lends itself to automation because of its systematic nature. This procedure that is based on the ENVOP technique has close similarity with the hazard and operability (HAZOP) analysis commonly used in safety analysis. Venkatasubramanian and Vaid-
hyanathan5 reported an expert system called HAZOPExpert that can automatically perform HAZOP analysis. In their system, the knowledge required for HAZOP analysis is divided into process-general knowledge, which consists of a unit operation library and an automated HAZOP algorithm, and process-specific knowledge, which contains the user input information about the process material properties and the process and instrumentation diagram (P&ID) of the process. A similar knowledge organization is implemented in ENVOPExpert as shown in Figure 1. ENVOPExpert consists of three main components: (1) a knowledge library, (2) a waste minimization inference engine, and (3) an integrated graphical user interface (GUI). Each of these is discussed further in the following sections. 3.1. Knowledge Library. ENVOPExpert performs its waste minimization analysis using domain expertise that is embedded in its knowledge library. The knowledge is clearly divided into two distinct parts: processspecific information about the process under study and process-general waste minimization domain knowledge. The process-specific part has to be input by the user for each case study and stores information about process materials, process chemistry, the flow sheet, and the operating conditions of each unit. The process-general knowledge provides the framework for representing the process-specific information of the process under review. The structured information is then augmented with a corresponding process graph (P graph) and functional and directed graph (digraph) models and is analyzed using the algorithms in the inference engine. The user specifies all of the process information in terms of the process flow sheet, operating conditions of each unit in the flow sheet, process material information, and process chemistry. Information about each process material is encapsulated in a material object.
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002 211
The functions of the material in the process (reactant, catalyst, solvent, etc.) are captured as an attribute of each material object. It is possible for the same material to serve multiple functions in the process. One example of such a material is steam, which is commonly used as a heating agent in heat-transfer equipment, whereas in a stripper column, it is utilized as a stripping agent. In this case, the user can specify all of the functions of the material in the process. Besides process material information, the user also has to specify the material components present and the process conditions in each input stream, the operating condition of each unit in the flow sheet, and the process chemistry (reaction, separation, and phase change) that take place in the process. The process-general knowledge consists of class definitions for structuring the process-specific information and making it amenable to waste minimization analysis by the inference engine. Each process unit in the flow sheet is analyzed using six fundamental chemical engineering concepts central to waste minimization: (1) reaction, which encompasses reactor units such as plugflow reactors and stirred reactors, (2) separation, which incorporates separator units such as distillation columns, gas absorbers, and flash separators, (3) phase change, which is common in heat-transfer equipment such as heaters, coolers, and heat exchangers, (4) material transport, which involves the transfer of material from one point to another and is brought about by pumps, compressors, valves, and pipes, (5) storage, which comprises units for storing material such as tanks, and (6) the mixing of multiple streams into a single stream and the splitting of a stream into multiple streams. These fundamental concepts coupled with their waste detection diagnosis and minimization alternatives knowledge are embedded along with the unit operations class hierarchy implemented in ENVOPExpert. The processgeneral knowledge also provides the object infrastructure necessary for the P graph, digraph, and functional models. The process-specific information is structured using the mold described previously and used by the inference engine to perform a waste minimization analysis as described in the next section. 3.2. Waste Minimization Inference Engine. The inference engine facilitates the integration of the processspecific information and the waste minimization heuristic rules and methods embedded in ENVOPExpert’s knowledge base. The inference engine contains algorithms that execute the waste minimization methodology described in part 1. This is realized through four different facets as shown in Figure 2: material propagation, P graph analysis, functional representation, and digraph analysis as explained in the following paragraphs. The task of material propagation can be stated as follows: given a process description including the flow sheet, with a number of inlet streams, process units and outlet streams, operating conditions in process units, and materials in each inlet stream, identify the materials present in each process unit and stream. To achieve this, we have developed three basic methods which analyze the (1) material flow from equipment to pipe, (2) material flow from pipe to equipment, and (3) material generation and consumption through a chemical- and physical-phenomena-checking method. These are used to simulate the propagation of material from
the process inlet streams (source) through the various units to the final outlet streams (sinks) in the flow sheet. The equipment-to-pipe material flow method provides an algorithm for transferring the materials present in each process unit to its output streams. Similarly, the pipe-to-equipment method simulates material transfer from flow pipes to process units. The phenomenachecking method accounts for phenomena such as reaction, separation, or phase change that may occur in a unit over the predefined operating conditions. The algorithms for material propagation follow the principles of the sequential modular approach commonly found in process simulators. Starting from each process inlet stream, the equipment-to-pipe and pipe-to-equipment phenomena-checking methods are repeatedly executed. For every process unit, the phenomena-checking method identifies all of the phenomena possible in the unit, accounts for material generation and consumption, and updates the lists of materials in each output stream appropriately. When a material recycle is present in a process flow sheet, an initial stream tearing of the recycle unit is applied followed by checking of material across the recycle for convergence. All of these methods are executed in sequence for each unit of the process until the presence of materials at each process unit and process outlet stream is fully established. Once the materials present throughout the flow sheet have been determined, each of ENVOPExpert’s knowledge elements (P graph, digraph, and functional models) is activated to facilitate knowledge application between the different parts of ENVOPExpert. The algorithm for developing the P graph model follows the sequential modular approach of the material propagation algorithm with the exception that, in the P graph algorithm, the simulation is done backward, starting from the waste streams and tracing upstream to process units until terminated at a process input stream. Suppose there are three materials (A-C) in a process waste stream; consequently, three P graph models are developed, one for each material A-C in the waste stream. Through a material’s P graph model, all of the input streams and process units responsible for its presence in the waste stream can be identified. As discussed in part 1, four distinct sources of waste (useless material in inlet stream, useful material transformed at low conversion rate, useless material produced from reaction or phasechange phenomena, and ineffective separation of useful material) can be established using the P graph analysis. We have developed top-level waste minimization heuristics to identify these waste sources.1,6 These heuristics are implemented in ENVOPExpert through IF-THEN rules as shown in Table 2. These alternatives mention the broad modification required in the process unit or feed material to minimize waste. Digraph models of unit operations are used to further examine these top-level suggestions derived from P graph analysis. Detailed suggestions can specify the specific process variables that should be modified to obtain desirable changes. The pertinent process variables are identified using digraph models. A digraph model represents the various interactions, both variable-variable interactions and variable-phenomena interactions, in each process unit and is derived from material and energy balances. Figure 3 shows the digraph model of an absorption column. Each digraph node represents a process variable and can take the values’ “increase” or “decrease”. A directed arc connects
212
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002
Figure 2. Inference scheme implemented in ENVOPExpert.
two digraph nodes, and its arc value (promotion “+” or suppression “-”) appropriately captures the cause-and-
effect relation between the variables represented by the two nodes. In our approach, the top-level alternatives
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002 213 Table 2. Top-Level Waste Minimization Heuristics process unit
IF
THEN
inlet stream
there exists a useless material (impurity) there exists a useful material leading to waste stream
waste stream (purge waste stream)
there exists a useful material
remove that useless material (impurity) from entering the process prevent excessive feed of that material in the corresponding inlet stream use alternative material in the inlet stream direct recycle or reuse recycle of the material back to the process optimize the purge flowrate to reduce loss of useful material from the purge stream consider alternative process chemistry or avoid the reaction from occurring optimize and control the reactor operating variables provide an optimum feed addition and distribution improve reactants mixing add reaction agent to increase the yield of useful reaction product consider alternative reactor design improve the catalyst performance or change to alternative catalyst that eliminates waste byproduct add a recovery system after the reactor (phase-change equipment) to recover that product (final material) from becoming waste optimize the reactor (phase-change equipment) operating variables to increase the reactant (initial material) conversion add a recovery system after the reactor (phase-change equipment) to recover the reactant (initial material) and recycle it back to the reactor (phase-change equipment) add another reactor (phase-change equipment) after the reactor (phase-change equipment) to further transform the reactant (initial material) leaving that reactor optimize the separation in the separator
reactor (phase-change equipment)
there exists a useful material (purge waste stream) there is main reaction producing useless material there is side reaction producing useless material
the product (final material) from the reactor (phase-change equipment) leading to waste stream the reactant (initial material) in the reactor (phase-change equipment) becomes waste
separator
there is inefficient separation
Figure 3. Digraph model of an absorption column.
generated from the P graph analysis are directly translated to conclude a value for the phenomena nodes in the process unit digraphs. Once the phenomena node receives a value, this value is propagated to the variable nodes directly affecting the phenomena node (that is, variables nodes upstream of the phenomena node). When the connecting arc between two digraph nodes is “+”, the cause variable node will get the same value (increase or decrease) as the effect node. On the other hand, if the connecting arc is “-”, the value of the phenomena node will be passed to the variable node in the opposite direction. This digraph propagation is repeated for each upstream variable node until all of
the variable nodes have a value. The reader is referred to part 1 for a detailed discussion on the interaction between P graph and digraph models. A digraph model models the cause-and-effect relations between the variables and phenomena within a process unit. To link the digraph models of the different process units, a functional model of the process is used. In functional modeling, each unit and stream of the process is represented by its functions and the functional variables that characterize that unit. A functional model links two or more digraph models in a process section that share functional variables. Consider a process containing a condenser connected to the bottoms of an absorption column. The digraph model of the absorption column (see Figure 3) reveals that a decrease in the vapor content in the bottom feed reduces the wastes leaving from the top of the column. Through functional models, we are able to conclude that the condenser connected directly upstream of the column has an effect on the absorption performance, because both units have composition as one of their functional variables. Consequently, the digraph model of the condenser can be accessed with an objective to increase the condensation rate before the materials enter the column. The condenser digraph node representing vapor-to-liquid condensation will thus receive a value increase, and values for the other variables in the condenser connected to this phenomenon can be inferred. Detailed waste minimization alternatives throughout the process flow sheet can thus be identified using digraphs and functional models.
214
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002
Figure 4. Graphical user interface for inputting process-specific information.
3.3. Graphical User Interface. ENVOPExpert’s GUI provides for seamless interaction between the user and the knowledge and information contained in ENVOPExpert. A portion of the ENVOPExpert’s GUI for the inputting of process-specific information is shown in Figure 4. The GUI enables the user to construct a flow sheet by selecting the unit operations from the library, connecting them using streams, and specifying the operating conditions and materials present in each inlet stream. The GUI also provides for the inputting of other necessary process-specific information, such as the properties of materials, reactions, separations, and phase changes. Materials and chemistry not available in ENVOPExpert’s library can be added to the library. The user can browse and edit the process-specific information and the various models at any time using similar graphical screens. The GUI also enables the user to develop and add new waste minimization knowledge, including building new digraph models and editing existing digraphs. Once all of the necessary process-specific information has been input, a waste minimization analysis can be performed. The GUI provides various menu options to execute a waste diagnosis and minimization analysis and to derive the top-level and detailed alternatives. After the diagnosis and analysis are completed, the user can view the issues and the waste minimization options derived by ENVOPExpert. In ENVOPExpert, each proposed alternative is internally stored as a solution object, comprising attributes that describe the desired modification (that minimizes the waste), its applicability (description of the process units), the variable involved in the alternative (pressure, temperature, etc.), and the change required in the variable (optimize, increase,
decrease, etc.). To present these solutions to the user, the solution object is cast into natural language using the following structure: of to . In the previous condenser-absorber example, a solution object corresponding to the condenser unit may be described as follows: decrease temperature of condenser to increase condensation rate . The natural language results are presented as a table of waste minimization alternatives as shown in Figure 5. 4. Case Study: Chemical Manufacturing Process ENVOPExpert has been tested using a chemical production case study found in the literature. A team of experts had performed a waste minimization analysis of this case study, and their results are available for comparison with ENVOPExpert’s analysis. The case study involves a production of a chemical intermediate called PROD from an exothermic reaction between a reactant named REAC, air, and ammonia.7 Figure 6 describes the flow sheet of the process, and Table 3 shows the materials present in the different streams of the process. Initially, the reactant mixture is preheated in a series of heat exchangers before being passed to a fixed-bed catalytic reactor. From the reactor, the reaction effluents containing PROD, excess reactants, and waste byproducts are separated in a series of separation systems to separate between the products, wastes, and reactants. The separation steps involve the following operations: (1) aqueous scrubbing of reactor
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002 215
Figure 5. Graphical user interface for viewing ENVOPExpert’s results.
Figure 6. Flow sheet of a chemical intermediate process plant.
216
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002
Table 3. Qualitative Material Balance for Chemical Intermediate Production Processa
a
V ) vapor phase, L ) liquid phase, V-L ) both vapor and liquid phases.
Table 4. Comparison between ENVOPExpert’s and Experts’ Results for a Process Inlet Stream waste minimization alternatives stream or unit operation
experts’ results
ENVOPExpert’s solution
reactant feed
use pure oxygen in the reactor instead of air to eliminate nitrogen use a different heat sink instead of nitrogen, such as carbon dioxide or steam
makeup benzene
replace benzene with a different extraction solvent, such as toluene or xylene
ammonia solvent
sulfuric acid solvent
use caustic soda instead of ammonia for pH control of the water stream to the benzene extractor to reduce total nitrogen load to wastewater treatment not identified
water stream
not identified
steam
not identified
prevent excessive feed of useful material in the stream reactant feed remove impurity material from the stream reactant feed decrease temperature of the reactant feed to the optimum temperature that starts the reaction in the fixed-bed reactor prevent excessive feed of useful material in the makeup benzene stream use alternative extraction agent rather than current benzene in the makeup benzene stream prevent excessive feed of useful material in the ammonia solvent stream use alternative neutralizing agent rather than current material in the ammonia solvent stream prevent excessive feed of useful material in the sulfuric acid solvent stream use alternative washing agent rather than current material in the sulfuric acid solvent stream prevent excessive feed of useful material in the water stream prevent excessive feed of useful material in the steam stream
off-gas using water and sulfuric acid solvents to capture valuable reactants and products, (2) extraction of the reactants and products using benzene as a solvent, and (3) distillation to obtain a high purity of the reactant REAC and product PROD. This process has three waste streams: waste gas, wastewater, and off-gas. A systematic P graph analysis for every material present in each waste stream was first performed using ENVOPExpert. The P graph analysis reveals the following waste sources: (1) an impurity in the feed stream (IMPURE), (2) waste byproducts (CO2, NOx, SO2, COS, HCN) formed in the fixed-bed reactor, (3) excess feed of water, sulfuric acid, and benzene streams, (4) excess reactant mixture in the feed stream, and (5) inefficient material separation in the acid-scrubber, benzene-extractor, and benzenerecovery columns.
Each of these activates the digraph and functional models, and top-level and detailed waste minimization alternatives have been generated. Tables 4-9 list the waste minimization alternatives identified by ENVOPExpert along with the team’s results for different process units and streams. Once the alternatives have been identified, they are compared and ranked. Table 10 shows three of the derived alternatives: “direct recycle or recovery recycle of the useful components in wastewater stream”, “direct recycle or recovery recycle of the useful components in waste gas stream”, and “consider alternative reactor design”, for which the waste minimization index has been calculated. These alternatives are analogous to the following team’s suggestions: “freeze-crystallize reactant REAC and product PROD from wastewater”, “use a condenser and decanter to separate and recycle the
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002 217 Table 5. Comparison between ENVOPExpert’s and Experts’ Results for a Fixed-Bed Catalytic Reactor waste minimization alternatives for fixed-bed reactor experts’ results
ENVOPExpert’s solution
use a new or improved catalyst in the reactor replace the existing fixed-bed reactor with a fluidized reactor to eliminate hot spots that lead to unwanted byproduct formation use an indirect-contact heat exchanger to cool the reactor off-gas and condense the product and reactant
optimize and control reactor’s operating variables, provide optimum feed addition and distribution, and improve mixing to reduce the formation of waste byproduct and to increase the yield of useful material add reaction agent to increase the yield of reaction product change to alternative reactor design improve the performance of the catalyst or change the catalyst inside fixed-bed reactor to eliminate or minimize waste byproduct optimize the reactor operating variables to increase the conversion of useful material inside fixed-bed reactor add a recovery system after fixed-bed reactor to recover the reactant and recycle it back to the fixed-bed reactor add new reactor after fixed-bed reactor to further transform the useful material leaving from it add a recover system after fixed-bed reactor to recover useful reaction products
Table 6. Comparison between ENVOPExpert’s and Experts’ Results for Acid and Water Scrubbers waste minimization alternatives fpr acid scrubber and water scrubber experts’ results
ENVOPExpert’s solution
combine the acid and water scrubbers to reduce water consumption
increase the number of trays or the height of packing area and improve the control performance to improve the separation inside acid scrubber increase the number of trays or the height of packing area and improve the control performance to improve the separation inside water scrubber use alternative separation system besides acid scrubber, which does not transform useless material to another useless material use alternative separation system besides water scrubber, which does not transform useless material to another useless material increase the flowrate of absorbing liquid to remove useful material from the vapor entering at the bottom of acid scrubber decrease the temperature of acid scrubber for better absorption increase the pressure of acid scrubber for better absorption use further separation process after acid scrubber before going to off-gas stream to recover useful component use alternative separator system besides acid scrubber, which does not produce useless material redesign the separator acid scrubber to reduce formation of useless material use alternative separation system besides water scrubber, which does not produce useless material change the design of water scrubber to reduce formation of useless material
benzene from the steam stripper overheads”, and “replace the existing fixed-bed to fluidized bed reactor”. To carry out an index calculation on those alternatives, the information available from the team’s results is used. Because there is no information provided in terms of percentage waste reduction that can be achieved, we assume 100% waste reduction in each case. On the basis of the calculation results, the alternative “consider alternative reactor design” has the highest index value and is automatically ranked as the best waste minimization alternative. A comparison between ENVOPExpert’s and the team’s results indicates that ENVOPExpert’s results are in-
line with the conclusions of the experts’ team. ENVOPExpert was found to identify almost all of the 16 alternatives identified by the team and some others not mentioned in the experts’ results. In total, the ENVOPExpert system identified over 50 alternatives as compared with the team’s suggestions. This is because ENVOPExpert performs a more thorough and systematic analysis than the team. First, the waste analysis algorithm of ENVOPExpert is done with reference to each material present in each waste stream, while the team would have based their analysis on selected streams only. Second, we have embedded an extensive top-level waste minimization heuristics from the litera-
218
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002
Table 7. Comparison between ENVOPExpert’s and Experts’ Results for a Benzene Extractor waste minimization alternatives for benzene extractor experts’ results
ENVOPExpert’s solution
recycle the water stream from the benzene extractor to the water scrubber
increase the number of trays or the height of packing area and improve the control performance to improve the separation inside benzene extractor use further separation process after benzene extractor before going to steam stripper to recover useful component increase the flowrate of stripping vapor to remove useful material from the liquid entering at the top of benzene extractor decrease the pressure of benzene extractor for better stripping increase the temperature of benzene extractor for better stripping
Table 8. Comparison between ENVOPExpert’s and Experts’ Results for a Waste Stream waste minimization alternatives stream or unit operation off-gas
waste gas
wastewater
experts’ results
ENVOPExpert’s solution
use the chilled reactant REAC to scrub itself from the reactor off-gas recycle the acid scrubber off-gas to the reactor and add pure oxygen as makeup use a condenser and decanter to separate and recycle the benzene from the steam stripper overheads instead of thermal oxidizer recycle the water stream from the steam stripper to the water scrubber freeze-crystallize reactant REAC and product PROD from the water to improve product recovery and reduce total nitrogen load to wastewater treatment use a multi-effect evaporator to concentrate wastewater stream
direct recycle or recover recycle of useful material component in the off-gas stream direct recycle or recover recycle of useful material component in the waste-gas stream direct recycle or recover recycle of useful material component in the wastewater stream
Table 9. Comparison between ENVOPExpert’s and Experts’ Results for Heating Equipment waste minimization alternatives for preheater and electric heater experts’ results
ENVOPExpert’s solution
add a second heat exchanger in series with the preheater to improve energy recovery
change electric heater with heat-exchanger equipment to save energy decrease the power of electric heater to optimum that starts reaction in fixed-bed reactor decrease the temperature of reactant feed to optimum that starts reaction in fixed-bed reactor
ture into the knowledge base of ENVOPExpert. These offer a thorough analysis. Third, the functional models in ENVOPExpert implement strict functional relations between streams and equipment. As an example, consider the waste byproducts formed inside the fixed-bed reactor. The digraph model of the reactor indicates that an increase of the temperature inside the reactor would result in more waste byproducts. ENVOPExpert’s reactor functional model would show that a temperature increase in the reactant feed stream, heat from the preheater and the electric heater, would result in a temperature increase of the reactor feed. The team, however, would have filtered out the temperature effect of the reactant feed and possibly the preheater in relation to the high temperature inside the reactor by using their quantitative judgment. They would have rather focused on the electric heater as the unit that directly affects the reactor temperature. The comparison
between the two results also shows that the team derives more detailed alternatives than ENVOPExpert. The team’s alternative “use caustic soda instead of ammonia for pH control of the water stream” is more specific than the “use alternative neutralizing agent” identified by ENVOPExpert. For ENVOPExpert to propose such detailed options, it requires an extra technology-specific knowledge domain which is currently not available in ENVOPExpert. While the overall results from ENVOPExpert are very encouraging, the evaluation and analysis methods implemented in ENVOPExpert have some inherent shortcomings. It is possible for the ENVOPExpert system to generate inconsistencies when the overall waste minimization objectives conflict with the production goal. For example, increasing the temperature inside a reactor to reduce the generation of a particular waste byproduct may increase the formation of another
Ind. Eng. Chem. Res., Vol. 41, No. 2, 2002 219 Table 10. Screening and Ranking of Waste Minimization Alternatives alternative 1 ENVOPExpert’s suggestion
benefit
reduce useful material in wastewater
direct recycle or recovery recycle of the useful component in waste gas stream use a condenser and decanter to separate and recycle the benzene from the steam stripper overheads reduce useful material in waste gas
applicability waste minimization type ease of implementation capital cost ($) payback time (year) waste reduction (%) index rank
wastewater stream recycle add new equipment 3,500,000 >9 100 10,310,010,000 3
waste gas stream recycle add new equipment 100,000 1 100 10,310,028,000 2
expert’s solution
direct recycle or recovery recycle of the useful component in wastewater stream freeze-crystallize reactant REAC and product PROD from wastewater
alternative 2
waste byproduct in a downstream unit or decrease the production of useful material. This problem is similar to that encountered by human experts during their brainstorming session. One solution to this problem is to use more precise quantitative reasoning using a process simulator or numerical optimization. For this purpose, a unified qualitative-quantitative approach for integrating ENVOPExpert with a chemical process simulation package and optimization tool is currently being developed. The hybrid qualitative-quantitative algorithm would provide a framework for evaluating each waste minimization alternative obtained from ENVOPExpert, achieve a more robust waste minimization analysis, and keep the overall goal of the production and process economics in sight. 5. Conclusions A knowledge-based framework for automating a qualitative waste minimization analysis, called ENVOPExpert, has been proposed. This intelligent system implements the systematic waste minimization methodology as described in part 1 of this paper. It consists of three main components: a knowledge base that incorporates waste minimization domain knowledge and structured process-specific information; an inference engine with rules and methods for conducting waste diagnosis, analysis, and alternatives generation; and a GUI to provide the user-friendly, seamless interaction with the system. P graphs are used to diagnose waste sources and identify top-level minimization alternatives. These top-level alternatives are further analyzed using digraph and functional models and detailed alternatives are generated. The detailed alternatives can then be screened using a waste minimization index that ranks alternatives based on their type, ease of implementation, efficacy, cost, payback time, and so forth. ENVOPExpert has been tested successfully on an indus-
alternative 3 consider alternative reactor design replace the existing fixed-bed to fluidized bed reactor reduce formation of useless byproducts in reactor fixed-bed reactor source reduction add new equipment 500,000 2 100 100,310,016,000 1
trial case study obtained from the literature. The results show that ENVOPExpert is able to emulate closely the analysis, diagnosis, and decision-making process of the human experts. While the overall finding from the ENVOPExpert system is very promising, we realize that there are some drawbacks in the qualitative analysis implemented in ENVOPExpert and are developing a unified qualitative-quantitative methodology to address these. Literature Cited (1) Halim, I.; Srinivasan, R. Systematic Waste Minimization in Chemical Processes. 1. Methodology. Ind. Eng. Chem. Res. 2002, 41, 196. (2) Allen, D. T.; Rosselot, K. S. Pollution Prevention for Chemical Process; John Wiley and Sons: New York, 1997. (3) Isalski, W. H. ENVOP for waste minimization. Environ. Prot. Bull. 1995, 34, 16. (4) Smith, R. L.; Khan, J. A. Unit Operations Database for Transferring Waste Minimization Solutions. In Waste Minimization through Process Design; Rossiter, A. P., Ed.; McGraw-Hill: New York, 1995. (5) Venkatasubramanian, V.; Vaidhyanathan, R. A KnowledgeBased Framework for Automating HAZOP Analysis. AIChE J. 1999, 40 (3), 496. (6) Halim, I.; Srinivasan, R. An Intelligent System for Identifying Waste Minimization Opportunities in Chemical Processes. In European Symposium on Computer Aided Process Engineering 10; Pierucci, S., Ed.; Elsevier Science: Amsterdam, The Netherlands, 2000. (7) Mulholland, K. L.; Dyer, J. A. Pollution Prevention: Methodology, Technologies and Practices; American Institute of Chemical Engineers: New York, 1999.
Received for review March 5, 2001 Revised manuscript received October 3, 2001 Accepted October 16, 2001 IE0102089
