Systematic Waste Minimization in Chemical Processes. 3. Batch

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Ind. Eng. Chem. Res. 2006, 45, 4693-4705

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Systematic Waste Minimization in Chemical Processes. 3. Batch Operations Iskandar Halim† and Rajagopalan Srinivasan*,†,‡ Institute of Chemical and Engineering Sciences (ICES), 1 Pesek Road, Jurong Island, Singapore 627833, and Laboratory for Intelligent Applications in Chemical Engineering, Department of Chemical and Biomolecular Engineering, National UniVersity of Singapore, 10 Kent Ridge Crescent, Singapore 119260

The current drive toward ecosustainability has provided a strong impetus to implement waste minimization within the chemical industries. However, conducting a waste minimization analysis is expensive, timeconsuming, laborious, and knowledge-intensive. In part 1 of this series [Halim, I.; Srinivasan, R. Systematic Waste Minimization in Chemical Processes: Part I. Methodology. Ind. Eng. Chem. Res. 2002, 41 (2), 196], we described a systematic methodology based on the Environmental Optimization (ENVOP) technique for waste minimization analysis of chemical plants. In part 2 [Halim, I.; Srinivasan, R. Systematic Waste Minimization in Chemical Processes: Part II. Intelligent Decision Support System. Ind. Eng. Chem. Res. 2002, 41 (2), 208], we reported on ENVOPExpert, an expert system that implements the methodology. While the focus in parts 1 and 2 were on continuous processes, in part 3 we extend the waste minimization methodology to batch process plants and describe its implementation as an expert system called BATCHENVOPExpert (BEE). In contrast to continuous processes, during batch processing different wastes may be generated at different times even from the same process unit. The process flow diagram that serves as the backbone for ENVOP analysis therefore does not completely represent the waste generation mechanisms, and the process recipe has to be analyzed to both diagnose the source of waste generation and identify process modifications that will eliminate it. In this paper, we extend the process graph based waste diagnosis approach described in parts 1 and 2 for continuous processes to batch processes by incorporating knowledge and operating procedures of the process recipe, which can be modeled using Grafcets. A process graph (P-graph) model of the operation can then be generated by considering the flow diagram and the Grafcet of the process. This P-graph can be analyzed to diagnose the waste sources and heuristic-based waste minimization solutions can be derived at a high level. Specific variable-level solutions can also be implied by combining cause-andeffect knowledge and functional models of the process. We illustrate the approach by performing waste minimization analysis on an industrial herbicide manufacturing case study and compare our results with the available experts’ solutions. 1. Introduction The operating modes of a chemical plant can broadly be classified as continuous, batch, and semibatch (or semicontinuous). The batch operation, which is characterized by prescribed processing of raw materials and intermediates to produce one or more final products for a finite duration, has been the most widely used in the manufacture of high value added products such as fine chemicals, agricultural chemicals, food, and pharmaceuticals. Typically, manufacturing of such products generates high waste per unit of production. In fine chemicals and pharmaceutical processes, for example, it is estimated that production of 1 kg of product would generate up to 100 kg of wastes.3 In the context of the US batch chemical industry, this translates to an annual release of 400 million lbs of toxic chemicals and 13 million tons of greenhouse gases.4 With the current drive toward ecosustainability, chemical producers are facing mounting pressure to eliminate or at least reduce their waste generation. There are many reasons for the large quantities of waste production in a batch process. As product quality and purity specifications need to be strictly controlled, such as in the case of pharmaceutical products, large amounts of solvents and cleaning agents are commonly used, thus leading to high waste * To whom correspondence should be addressed. E-mail: [email protected]. Fax: +65 67791936. Tel.: +65 65168041. † ICES. ‡ National University of Singapore.

volumes. At the same time, complex reaction synthesis and lack of knowledge of the properties of materials may prevent suppression of byproducts and efficient recovery of the solvents and other valuable components for reuse or recycle. Furthermore, lack of understanding of the physicochemical phenomena involved in typical units (such as a crystallizer) may prevent improving the units to meet the environmental objectives. In the past, batch chemical industries could tolerate their high waste generation due to the high values of final products, which outstripped the costs of waste treatment and disposal. However, as regulations such as the Clean Air Act, Toxics Substances Control Act, and Kyoto Protocol get more stringent, waste has become more expensive to deal with. All these along with price competition from low-cost countries provide incentives for the batch industries to consider waste minimization measures. Waste minimization, which aims to manufacture products through zero emission, is one valuable tool in the drive toward ecosustainability. Nevertheless, performing waste minimization analysis of a process plant is laborious, time-consuming, expensive, and knowledge-intensive. The availability of an automated system that assists in synthesizing waste minimization opportunities in a chemical process is therefore attractive. Using such a system, different design alternatives and process variable changes can be evaluated quickly and compared effectively to the base-case process plant for improving the environmental performance. The presently available process simulation systems (Aspen Plus, HYSYS, Pro II, ChemCAD, SuperPro, etc.) focus mostly on other aspects of process design such as flowsheeting

10.1021/ie050792b CCC: $33.50 © 2006 American Chemical Society Published on Web 05/19/2006

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and do not have specific tools to aid in waste minimization analysis. We have addressed this shortcoming by developing an intelligent decision support system called ENVOPExpert to assist in waste minimization assessment of a plant.1,2,5 In this paper, we extend ENVOPExpert to batch processes. The waste profile in batch processes differs in many aspects from those in continuous processes. In a batch process, multiple operation steps can be performed in the same equipment. For example, a stirred jacket-heated vessel may be used to blend reactants, carry out a reaction, boil off solvent, or distill the product. As a result, wastes differing in quantity and compositions may be generated from the same piece of equipment. This is not accounted for by the methodology implemented in ENVOPExpert. To illustrate the important role of the operating sequence in the waste minimization analysis of a batch process, consider, as an example, a simple plant consisting of a jacketed vessel. This plant produces chemical C from reaction between chemical A and Chemical B followed by flashing of the reaction product. The entire operation of the plant can be grouped into three suboperations: “reaction in vessel”, “product flashing”, and “vessel cleaning”. The “reaction in vessel” step begins with charging raw materials A and B. After the reaction is completed, the next step is flashing the reaction products. Subsequently, “cleaning the vessel” task is performed and the vessel then becomes available for another operation. Let us say, besides the desired product C, the reaction between A and B also produces an undesirable byproduct D. Therefore, two waste streams of different compositions are possibly produced during the batch production, i.e., liquid phase mixture containing byproduct D from the flashing operation and wastewater from the vessel cleaning. The flow diagram (PFD) of this process then consists of a vessel with three input (A, B, and wash water) and three output (C, D, and wastewater) streams. Now, if we are to analyze the source of wastewater and byproduct streams based on this PFD alone, as in the case of a continuous process, there is no way we can conclude that stream D is caused by mixing of streams A and B, while the wastewater stream is from mixing the wash water with traces of A, B, C, and D. This fundamental difference necessitates new developments in the underlying knowledge representation and inference schemes that had been implemented in ENVOPExpert. This is indeed the subject of this paper, which is organized as follows: in the next section, a literature survey of different waste minimization methodologies in a batch process is presented. In section 3, we present an industrial herbicide manufacturing process case study. In section 4, we propose a knowledge representation and inference scheme for waste minimization in batch processes and illustrate it using the industrial case study. This is then followed by a comparison between the obtained results and the experts’ solutions in section 5. 2. Waste Minimization Approaches in Batch Processes Several methods for waste minimization in batch processes have been presented in the literature. These methods had been developed by extending the more established methodologies for continuous processes. Broadly, two approaches can be distinguished: quantitative and qualitative. The quantitative approach mainly involves pinch technology or mathematical programming application to solve plant scheduling and process synthesis problems by taking into account environmental considerations. Wang and Smith6 extended pinch analysis by incorporating a time factor to minimize wastewater generation in a batch plant. In their approach, wastewater was minimized by maximizing water reuse. Grau et al.7 described a design synthesis algorithm

for waste minimization in a multiproduct batch plant but considered only wastes generated during the tank cleaning process. Almato et al.8 formulated a mathematical model for minimizing freshwater usage through temporary storage facilities. However, their approach was limited to water with a single contaminant. Lee and Malone9 proposed a waste minimization strategy through scheduling of solvent recovery for reuse in a batch production process. Foo et al.10 extended the concept of a mass-exchange network (MEN) by introducing time-dependent analysis to minimize the requirement of mass-separating agents. One main drawback of the above-mentioned quantitative approaches is their restricted applications to a prespecified aspect of the overall waste problem, such as water or solvent reuse. This is partly due to the extensive amount of precise process knowledge required for the analysis. These approaches also have limited acceptance in operating plants due to the difficulty in setting up a mathematical formulation that fully models the numerous interconnections between different factors controlling waste generation. In the qualitative approach, the available techniques include the hierarchical design method and the checklist approach. Houghton et al.11 extended the hierarchical design method of Douglas12 to incorporate potential strategies for reducing waste generation in a batch plant. This was done through listing a series of design questions, which have to be considered at each level of the design hierarchy. Mulholland and Dyer13 applied a checklist approach to minimize waste generation in a batch reactor. In their approach, sets of waste minimization heuristics that focus on the charging, operation, discharging, and cleaning activities of the reactor were applied: for example, “consider using solvents with a lower vapor pressure to minimize evaporation and facilitate recovery”, “reduce vapor losses by improving seals on agitator and lids”, “reduce the batch temperature before discharge, to lower the vapor pressure of volatile organics”, and “replace solvent-based cleaning with aqueous-based cleaning”. Another qualitative approach to waste minimization is environmental optimization (ENVOP).14 As described in part 1, the procedures behind the ENVOP technique are similar to the ones in hazard and operability (HAZOP) analysis for process safety. In the ENVOP procedure, each stream and equipment in the process is identified for potential waste minimization solutions to particular waste streams. As in HAZOP, this is achieved by using guidewords that are a combination of a deviation keyword (more, less, larger, smaller, etc.) and relevant process variables (pressure, temperature, composition, etc.). Even though the application of the ENVOP technique has only been reported for continuous processes, it can be extended to batch plants. Compared to the quantitative approach, qualitative waste minimization analysis of a batch plant is more practical since less precise process data and knowledge are required. However, identifying unit operations and variables that contribute to the waste feature of the plant remains equally challenging as it demands considerable expertise and deep insight into the plant operation. In this paper, we describe BATCH-ENVOPExpert (BEE), an intelligent waste minimization system, which automates this analysis for batch process. BATCH-ENVOPExpert has been implemented in an object-oriented framework using Gensym’s G2 expert system shell with the following objective: GiVen the equipment flowsheet, operating procedure (production recipe), and chemistry of a batch process plant, the goal is to identify potential waste minimization alternatiVes. In this way, BATCH-ENVOPExpert is applicable to various phases of the process life cycle from conceptual design to

Ind. Eng. Chem. Res., Vol. 45, No. 13, 2006 4695 Table 1. Physical and Chemical Transformations in Dyelate Process Reaction (1) Reactant-A + Reactant-B f INT-1 + water byproduct liquid (catalyst: solvent liquid, mineral acid) (2) INT-1 + Na-OR f INT1-Na + volatile alcohol liquid (3) INT1-Na + H2SO4 f INT-1 + Na2SO4 (4) INT-1 + X2 + H2SO4 f Prod + HX Phase Change feed: water byproduct liquid, INT-1, Reactant-A, Reactant-B, solvent liquid, mineral acid evaporation process: (1) water byproduct liquid f water byproduct vapor + water byproduct liquid (2) solvent liquid f solvent vapor + solvent liquid feed: water byproduct vapor, solvent vapor evaporation process: (3) water byproduct vapor f water byproduct liquid (4) solvent vapor f solvent liquid feed: INT1-Na, volatile alcohol liquid, Reactant-A, Reactant-B, water byproduct liquid, INT-1, solvent liquid, mineral acid, Na-OR evaporation process: (5) water byproduct liquid f water byproduct vapor (6) solvent liquid f solvent vapor + solvent liquid (7) volatile alcohol liquid f volatile alcohol vapor Separation (1) input: Reactant-A, Reactant-B, water byproduct liquid, solvent liquid, mineral acid, INT-1, water byproduct vapor, solvent vapor tops: water byproduct vapor, solvent vapor bottoms: Reactant-A, Reactant-B, water byproduct liquid, solvent liquid, mineral acid, INT-1 (2) input: Reactant-A, Reactant-B, mineral acid, INT-1, Na-OR, INT1-Na, water byproduct vapor, water byproduct liquid, solvent vapor, volatile alcohol vapor, solvent liquid, volatile alcohol liquid tops: water byproduct vapor, solvent vapor, volatile alcohol vapor bottoms: Reactant-A, Reactant-B, mineral acid, INT-1, Na-OR, INT1-Na, solvent liquid (3) input: Reactant-A, Reactant-B, mineral acid, INT-1, Na-OR, INT1-Na, water, NaOH, H2SO4, Na2SO4, X2, Prod, HX tops: water, HX, H2SO4, Na2SO4 bottoms: Reactant-A, Reactant-B, mineral acid, INT-1, Na-OR, INT1-Na, water, NaOH, X2, Prod (4) input: solvent vapor, solvent liquid, water byproduct vapor, water byproduct liquid tops: solvent vapor, water byproduct vapor bottoms: solvent liquid, water byproduct liquid (5) input: water byproduct vapor, solvent vapor, volatile alcohol vapor distillate: volatile alcohol vapor bottoms: water byproduct vapor, solvent vapor (6) input: Reactant-A, Reactant-B, mineral acid, INT-1, Na-OR, INT1-Na, solvent liquid, water, NaOH extract: Reactant-A, Reactant-B, mineral acid, INT-1, Na-OR, solvent liquid, NaOH, INT1-Na raffinate: Reactant-A, Reactant-B, mineral acid, INT-1, Na-OR, INT1-Na, water, NaOH (7) input: Reactant-A, Reactant-B, mineral acid, INT-1, Na-OR, INT1-Na, water, NaOH, X2, Prod filtrate: Reactant-A, Reactant-B, mineral acid, INT-1, Na-OR, INT1-Na, water, NaOH, X2, Prod retentate: Prod (8) input: water, Prod filtrate: water, Prod retentate: Prod

retrofitting. In the following, we will discuss the waste minimization framework, but first we describe an industrial case study that will be used for illustrating the different components of the approach. 3. Case Study Description: Dyelate Manufacturing Process This case study involves the production of a herbicide named Dyelate.13 The process chemistry and flowsheet are shown in Table 1 and Figure 1, respectively. The process consists of a series of reaction steps involving Reactant-A, Reactant-B, and other reactant aids to form several intermediates, which are precursors to the production of Dyelate. The production recipe involves the following steps: (1) Condensation in Pot-1: Initially, Reactant-A and Reactant-B are reacted in Pot-1. The reaction, which is carried out using a mineral acid catalyst under boiling solvent condition, forms an intermediate INT-1 and water (byproduct). After the reaction is completed, a vacuum is used to separate the water byproduct and solvent components. The vapors are then condensed and decanted to recover the solvent for recycle and a water phase containing small amounts of residual solvent. The rest of the reaction mass, which is mostly INT-1, is cooled and transferred to Pot-2.

(2) Ring closure in Pot-2: In this step, INT-1 is rapidly reacted with a sodium alcohol salt Na-OR in Pot-2 to form an intermediate salt INT1-Na and a highly volatile alcohol byproduct. This is followed by heating the pot to remove the volatile components. The removed volatiles are then separated in a packed column, where the alcohol compound is condensed and recovered for reuse. Following the separation, water is added to Pot-2 to lower the viscosity of the reaction mass before extraction. (3) Extraction: The objective of the extraction step is to separate INT1-Na from the solvent components. In this step, water and caustic soda solution are added to increase the pH of the solution to aid the separation process. The solvent layer rises to the top of the extractor and is sent to a solventrecovery system, while the INT1-Na and water solution settles to the bottom of the extractor and is pumped to a holding tank. (4) Acidification and halogenation in Pot-3: From the holding tank, the INT1-Na solution is sent to Pot-3 for reaction with a sulfuric acid solution and chemical X2 to form precipitated Dyelate product Prod, byproduct acid HX, and Na2SO4. This is followed by a neutralization step through addition of caustic soda solution. The resulting HX is then vented and neutralized before being transferred to wastewater treatment.

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Figure 1. Equipment flowsheet for Dyelate manufacturing process. Table 2. Operating Procedure for Pot-1 task name

description

Charge Add Heat Add Add React Depressurize Flash Transfer Cool Transfer Cool Discharge Settle Discharge Discharge

charge Reactant-A from A-stream add solvent liquid from solvent stream heat Pot-1 to temperature T add mineral acid from feed catalyst stream add Reactant-B from B-stream react the content of Pot-1 depressurize the content of Pot-1 flash the content of Pot-1 transfer the tops of Pot-1 to condenser-1 cool the content of condenser-1 transfer the content of condenser-1 to condensate pot cool the bottoms of Pot-1 discharge the bottoms of Pot-1 to Pot-2 settle the content of condensate pot discharge the tops of condensate pot to recycled-solvent stream discharge the bottoms of condensate pot to wastewater stream

(5) Filtration and drying: The precipitated Prod slurry is first stored in a holding tank and then sent to a filtration-dryer unit to recover the desired product. During the filtration process, water is used to wash the product. The contaminated wash water from the filtration unit is later collected in a wastewater pot and sent for treatment. Tables 2-6 show the detailed operating steps. A team of experts has performed waste minimization analysis for this case study, and their results are available for comparison. 4. Framework for Waste Minimization Analysis of Batch Process Unlike in a continuous process, a batch process delivers its product not continuously but in discrete amounts. This means that the waste flow rate, composition, and other properties would

Table 3. Operating Procedure for Pot-2 task name Add React Heat Settle Transfer Distill Transfer Add Transfer

description add Na-OR from Na-OR stream react the content of Pot-2 heat the content of Pot-2 settle the content of Pot-2 transfer the tops of Pot-2 to packed column distill the content of packed column transfer the distillate of packed column to volatile pot add water from water stream to Pot-2 transfer the bottoms of Pot-2 to solvent extractor

Table 4. Operating Procedure for Solvent Extractor task name

description

Add Add Extract Discharge Add Extract Discharge Discharge

add water from water stream add NaOH from NaOH stream extract the content of solvent extractor discharge the raffinate of solvent extractor to intermediate tank add NaOH from NaOH stream to solvent extractor extract the content of solvent extractor discharge the extract of solvent extractor to solvent pot discharge the raffinate of solvent extractor to intermediate tank

vary with the execution of each operation performed. The previously proposed methodology for identifying waste minimization opportunities in continuous process plants1,2,5 implements three fundamental elements: P-graph, cause-and-effect digraph, and functional knowledge. As shown in Figure 2, in principle, the waste minimization procedure for batch processes is no different from that for continuous processes. The main distinction between the two is that in the batch plant, due to the additional temporal dimension, the analysis has to be performed with reference to the operating procedure (operation step) rather than the equipment flowsheet. In this section, we present the general waste minimization methodology as ap-

Ind. Eng. Chem. Res., Vol. 45, No. 13, 2006 4697 Table 5. Operating Procedure for Pot-3 task name

description

Transfer Add Acidify Heat Add Precipitate Settle Discharge Add Discharge

transfer the content of intermediate tank to Pot-3 add H2SO4 from water-H2SO4 stream acidify the content of Pot-3 heat the content of Pot-3 add X2 from X2 stream precipitate the content of Pot-3 settle the mixture of Pot-3 discharge the tops of Pot-3 to HX stream add NaOH from NaOH-water stream to Pot-3 discharge the bottoms of Pot-3 to slurry tank

Table 6. Operating Procedure for Product Filter task name

description

Transfer Filter Transfer Add Filter Discharge Discharge

transfer the content of slurry tank to product filter filter the content of product filter transfer the filtrate of product filter to wastewater pot add the content of washwater to product filter filter the content of product filter discharge the filtrate of product filter to wastewater pot discharge the retentate of product filter to final product

plicable to batch processes. It employs a two-step procedure for waste source diagnosis and waste minimization solution. First, all the materials present in each stream and unit of the process during every stage of operation have to be determined through qualitative mass balance. The next step is to diagnose the operation steps that contribute to the presence of each material in the waste streams. When the waste sources are detected, waste minimization alternatives, which seek to reduce waste at the sources or to segregate the useful materials from the waste stream for recycling, can be proposed through design heuristics. Detailed solutions of variable level can also be derived through cause-and-effect relations. All these steps can be systematically realized through the four fundamental elements shown in Figure 3: Grafcets for representing the operating procedure, P-graphs for representing the material flow in the process and deriving heuristic solutions, cause-and-effect knowledge for suggesting detailed operating condition changes, and functional models of the operations. 4.1. Grafcet Modeling. Grafcet, a representation scheme for discrete-event dynamic systems, has been widely employed for describing the sequential element of batch operating procedures.15 The International Society for Measurement and Control (ISA) S-88 standard16 for batch control systems also advocates the hierarchical representation of operating procedures using Grafcets. A Grafcet model consists of three basic components: steps, transitions, and tokens. Each of the recipe operations is modeled using steps (shown graphically as boxes), while the termination conditions associated with each operation are described by transitions (notated by bars) separating adjacent steps. A step can be either active or inactive and a token (represented by a black circle) is used to indicate the activation status of a step; an active step is indicated by a token residing in it. When a step becomes active, the action corresponding to the step is executed and the transition connected at the output of the step becomes activated; an active transition is denoted by a filled rectangle. When the condition associated with an active transition becomes true, the transition then fires to deactivate its input step and activate the output step. The reader is referred to Viswanathan et al.15 for a detailed description of Grafcets to model batch operating procedures. Operating procedures can be represented at varying levels of detail using hierarchical Grafcets. Each level of hierarchy represents the operation at a different level of abstraction and is significant in describing a batch process such as the Dyelate

Figure 2. Systematic waste minimization methodology for batch processes.

process. At the highest level is procedure steps followed by unit-procedure steps, operation steps, and phase steps as the lowest hierarchy (see Figure 4). The entire operation of the Dyelate process can be encapsulated by three procedure steps: “preparation”, “processing”, and “completion”. Associated with the “processing” procedure step is a set of unit-procedure steps, which are the major operation steps in the process recipe, i.e., condensation, ring closure, extraction, acidification-halogenation, and filtration-drying. Each unit-procedure step in turn consists of one or more operation steps that relate directly to the operational level of the process plant. In the case of the “condensation” unit-procedure step, these operation steps include charging of Reactant-A, adding solvent, heating reactant mixture, and discharging the products from separation unit. Each of the operation steps further consists of phase steps that can be associated with an operator action on a specific equipment (pump, valve, etc.) or instrument. Examples of phase steps include opening or closing of valve, switching on or off a pump, etc. Waste minimization analysis concentrates on the operation steps as they directly affect the waste generation phenomena. Figure 5 shows a portion of the Grafcet model for the condensation step of the Dyelate production process. In the figure, step S5 of the condensation unit-procedure step is currently active as it has a token residing in it. This means that the action associated with S5, i.e., add Reactant-B into the vessel, is currently being executed. At the same time, the output transition T5 will be continuously monitored until the end point of the S5 step, i.e., the desired amount of Reactant-B in the

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Figure 3. Integrated waste minimization framework.

vessel, is achieved. Once that condition is satisfied, transition T5 will be fired, which will cause S5 to become inactive; at that time the action associated with S6, i.e., react the vessel content, will be executed. This firing of transitions and the activationdeactivation of steps is the fundamental mechanism for modeling recipe execution in the proposed approach. The execution of the recipe determines the materials that are present in different units at various stages of the operation. The Grafcet model described above along with information about

the process flowsheet, material present at each input stream, and the reaction, separation, and phase change schemes (process chemistry) that take place over the predefined operating conditions is next used to perform a qualitative material balance of the process. 4.2. Qualitative Material Balance. The objective of the qualitative material balance is to identify each material component present in each unit and stream of the process during each operation step. Here, a process graph (P-graph) is used to

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Figure 4. Hierarchical Grafcet model of Dyelate process.

Figure 5. Grafcet representation of Dyelate’s condensation process.

describe the conditions of each stream and process unit pertinent to the recipe operation. The P-graph model originates from the work of Friedler et al.17-21 for representing process structure

to solve the synthesis problem in continuous processes. In their bipartite P-graph, an operating unit is represented by a bar, a material stream is represented by a circle, and connections

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Figure 6. P-graph unit representation of Grafcet operation step.

between material streams and operating units are represented by directed arcs. For batch operations, we adapt the P-graph to a recipe-centric viewpoint. A bar represents the operation step. Information on the stream or unit where the operation is performed, i.e., the operating unit, is also retained in the bar. A circle describes the materials present in the respective stream or unit during that step. The task of qualitative material balance can be stated as follows: GiVen a production recipe as captured in the Grafcet model and an equipment flowsheet, identify the materials present in each process unit and stream at different times throughout the production. This is equivalent to constructing the P-graph of the operating procedure. To achieve this, we can distinguish between four types of operations in a batch process: (1) those that involve a change in the operating unit without any change

Figure 7. Material flow and balance for condensation operation.

in the material or composition, e.g., charge and transfer; (2) those that involve a change in the materials within the same operating unit, e.g., react; (3) those that involve a change in the composition (phase change) within a operating unit, e.g., settle and crystallize; and (4) those that involve no change in either the materials, their composition, or the operating unit, e.g., heat and pressurize. Changes in composition or concentration during transfer are not considered. Each of the operation types has specific structures in the P-graph representation. Figure 6a shows the P-graph structure for type-1 operations; since material from a source unit is added to the existing contents (if any) of another unit, the structure consists of two input nodes, one bar representing the operation, and one output node. The list of materials in the output node corresponds to a concatenation of those in all the input nodes. The P-graph of type-2 operations have one input and one output node with a bar for the operation; however, the materials associated with the input and output nodes are different as shown in Figure 6b. Type-3 operations have one input node and multiple output nodes, with each output node corresponding to a different phase of materials with different compositions, as shown in Figure 6c. Type-4 operations with no change in operating unit or materials do not have to be represented explicitly in the P-graph. The procedure for constructing the P-graph of a batch operation is based on the above and involves two methods: (a) connecting the P-graph structures based on the operating procedure (i.e., within each operating unit), and (b) calculating changes in materials and composition during each operation using the knowledge of the process chemistry (i.e., reactions, separations, and phase-change phenomena) and the operating conditions (pressure, temperature, and list of materials) in the process unit at that stage. These two methods are repeated to simulate the propagation of material from the inlet streams or process units (sources) through the various units to the final outlet streams or units (sinks) in the flowsheet. To illustrate P-graph simulation, consider Figure 7, which describes the Dyelate process’ condensation step. The materials

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Figure 8. Waste flow of water byproduct.

present in each node are shown in the legend, and each bar is labeled by the task number of the Grafcet step as shown in Figure 5. The P-graph generation proceeds according to the sequence of the operations as indicated by the task number. The P-graph structure corresponding to operation “Charge Reactant-A from A-stream to Pot-1” is first created. This has one input and one output node. The structure of the “Add” operation (Figure 6a) forms the basis for operation-2 “Add solvent liquid from solvent-stream to Pot-1”. This structure is concatenated with the output node of “Charge” operation. The “heating Pot-1” operation is then connected to the output node of the “Add” operation. The “Add” operations (4 and 5) are created in a fashion similar to that of operation-2. During operation-6 “React mixture of Pot-1”, the process variables, i.e., pressure, temperature, and list of materials, would be compared with the reaction and separation information of the process (see Table 1). Since reaction-1 would occur under these conditions, the product of the reaction INT-1, as well as the byproduct water byproduct liquid would be added to the list of materials of operation-6 output node. A similar procedure is followed for operation-8, where the conditions of separation-1 are satisfied. Following the structure in Figure 6c, two output nodes corresponding to the two phasesstop and bottomsare therefore created. The procedure described above is repeated for all other tasks in the condensation operation to generate Figure 7. Similar procedures are also executed in sequence for other operations in the recipe, and the presence of materials at each process stream and unit is fully established. 4.3. P-graph Analysis for Heuristic-Based Solutions. Once the materials in each step of the process have been determined, the next step is to diagnose the operation steps that contribute to the presence of each material component in the waste streams. This is done by tracing each material starting from the waste streams and upstream through the P-graph to detect the wastegenerating operations as well as inefficient separation processes that lead to the escape of valuable materials into the waste streams. Here, we have adapted the superstructure reduction approach of Friedler et al.20 to track the waste origins. The procedure is illustrated in Figure 8, which is a substructure of the P-graph shown in Figure 7. Through the method of P-graph tracing, the sources of water byproduct liquid during the condensation operation can be diagnosed as the following: (a) byproduct formation during “react of Pot-1” (operation-6); (b) inefficiency in fully converting water byproduct vapor to water byproduct liquid during “cool vapor in condenser-1” (operation10); (c) inefficiency in separating water byproduct liquid during “settling mixture in condensate pot” (operation-14); (d) excess feed of Reactant-A and Reactant-B.

Once the waste-generating operations are found, alternatives to eliminate them can be proposed. Previously, we have identified the following design heuristics for waste minimization in continuous processes:2 (a) reduce useless materials in the material feed; (b) reduce waste byproduct formation; (c) improve the reaction and phase change operations; (d) improve the separation operation by adding an extra separation unit; (e) recover and recycle the useful material from waste stream. The P-graph representation naturally lends itself to such ifthen rules, and high-level process specific modifications can be efficiently identified. As the mechanisms of waste generation in batch processes are conceptually no different from those in continuous processes, the same heuristics are applied here as well. The P-graph analysis provides only the broad directions toward waste minimization from the perspective of recipe operations. Detailed analysis is needed to provide suggestions on which process variables or parameters should be manipulated in order to achieve the desired waste reduction. As an example, consider the waste minimization alternative “reduce byproduct formation during reaction”. The P-graph model is not capable of indicating which process variables have to be manipulated to reduce the byproducts. To arrive at such detailed alternatives, the cause-and-effect interactions among different variables affecting the performance of the reaction process need to be known. This can be obtained from a qualitative signed digraph model, mass- and energy-balance equations, or other causal models as described in the following section. 4.4. Causal Relationships of Variable Interaction. We have adapted signed digraphs to model the cause-and-effect relationships between the process phenomena and the variables they affect. Here, each operation step in the product recipe is characterized by physical or chemical transformations such as reaction, evaporation, vapor-liquid separation, and component mixing. Thus, a heating operation can be represented by heat transfer or evaporation, an agitation operation by mixing or reaction phenomena, and settling by vapor-liquid separation. In digraphs, phenomena and their associated variables are represented as nodes that are interconnected with directed arcs. Each node can take values “increase” or “decrease” and the directed arcs contain the value “+” to describe proportional or “-” for inverse relationship between two nodes. Examples of simplified cause-and-effect digraph models for heating, agitation, and settling operations are shown in Figure 9. The digraph model in Figure 9b shows that the conversion during a reaction is dependent on processing time as well as reaction coefficient; the latter in turn is affected by reaction temperature and agitation speed. Using such digraph representation, the high-level alterna-

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Figure 10. Linking digraph models through functional variables.

Figure 9. Digraph model of operation steps.

tive “improve the conversion of Reactant-A” during “react Pot1” operation derived from P-graph analysis can be further elaborated at the variable level as “increase the agitation duration”, “increase agitation temperature”, and “increase agitation speed”. In the same way, the alternative “improve settling operation” can be further explicated as “increase separation duration”. The main advantage of the digraph model to describe the cause-and-effect relationship in an operation step is that it is easy to develop, especially when the availability of process data is limited.22 This is particularly true in complex system processes where detailed models of the reactions are seldom available. However, one main limitation of the digraph model is that it is best suited to represent monotonic relations between the different nodes. In the case of the agitation digraph in Figure 9b, while reactant concentration is one of the important factors influencing the conversion, it is not represented in the digraph model since its effect on the reaction phenomena is dependent upon reaction kinetics (series, parallel, or combination), and cannot be easily generalized. Another drawback of the digraph model arises due to ambiguities from multiple, and nonlinear interaction. As an example, consider an output variable node “flow”, which is connected to two input variables “pressure” and “heat input” with “proportional” and “inverse” relationships, respectively. Here, ambiguities will arise in the value of “flow” when both input variables “increase”. To avoid such ambiguities, a more precise variable interaction model is needed, such as quantitative mass and energy balance equations.23 Process simulations can be performed using this model, and the effect of variables either individually or simultaneously on waste amount and composition can be compared to the base case. 4.5. Functional Models. While causal models facilitate identification of the controlling variables that need to be optimized to have the desired effect on waste generation, the change may have to be effected by modifying conditions or end points in another operation step. Consider as an example

the waste minimization suggestion “increase the agitation temperature” that can be derived from the digraph model of agitation (Figure 9b)sthe operation-6 of the condensation step. The change in temperature has to be brought forth at a preceding operation “heating the vessel mixture” (operation-3). To identify the operating step where changes are to be implemented, functional models are necessary. Functional models have been commonly used to describe the function of process units in continuous processes. For example, Rodriguez-Martinez et al.24 describe a knowledge representation for retrofitting continuous chemical plants, where each process unit is classified according to its general and specific functions in the plant. The general function of a heater, for example, is to change the temperature of a stream, while the specific function is to increase the temperature. Here, we extend their approach to batch operations. We have classified each operation step in the process recipe in term of the phenomena it causes (general function) and the variables affecting the phenomena (specific function). The general function of each step is captured by the phenomenon node as discussed in the previous section. In this section, we explain the functional variables of operation steps. Table 7 shows the functional models for common operation steps. For example, the general function of the “react” operation is reaction and its main variables are concentration and temperature of the reactants. On the other hand, the function of “flash” operation is separation and its functional variables are concentration, pressure, and temperature of the incoming feed. Once the list of functional variables representing an operation is available, the next step is to connect the operations that share the same functional variables. In this case, as temperature is the functional variable of the “heating”, “agitation”, and “settling” operations, their causal models are internally linked together to form a network as shown in Figure 10. Thus, given a product recipe of a batch process, the entire chain of functional interactions between operation steps can be established. We have implemented the methodology described in the previous section as an expert system called BATCH-ENVOPExpert (BEE) that automates waste minimization analysis. BATCH-ENVOPExpert performs its analysis using domain expertise that is embedded in its knowledge library. BATCHENVOPExpert is structured similarly to ENVOPExpert, the knowledge domain of which is divided into two distinct parts: process-specific information about the process under study and a process-general waste minimization domain. The processspecific part has to be input by the user for each case study and

Table 7. Functional Variables for Major Operation Steps operation step

general function

functional variables

acidify/neutralize add/charge/discharge/transfer agitate/react cool/heat crystallize/distill/flash/filter/settling depressurize/pressurize

reaction material flow reaction cooling/heating separation depressurize/pressurize

concentration, pH concentration, temperature, pressure concentration, temperature temperature concentration, temperature, pressure pressure

Ind. Eng. Chem. Res., Vol. 45, No. 13, 2006 4703 Table 8. Comparison between BATCH-ENVOPExpert’s and Experts’ Waste Minimization Alternatives for the Condensation Step experts’ results

BATCH-ENVOPExpert’s solution

Steam or air strip solvent from wastewater stream. Direct recycle or recovery-recycle of useful components in wastewater stream. Improve the vacuum system to minimize the wastewater stream. Use a heterogeneous catalyst instead of a homogeneous one. Change from a homogeneous to a heterogeneous catalyst. Operate Pot-1 at lower absolute pressure. Optimize the operating conditions, feed addition and distribution, and mixing to reduce byproducts formation. Increase stirring speed, temperature, and agitation duration during agitation (reaction) operation. Increase the temperature during heating operation. Consider using a reaction agent to suppress byproducts. Consider alternative process chemistry to avoid byproduct formation. Decrease the temperature in Pot-1 during vapor-transfer operation. Depressurize Pot-1 more effectively. Use a colder decanter temperature to improve separation. Cool the vapor discharge more effectively inside condenser-1. Decrease the temperature in condenser-1. Increase the pressure in condenser-1. Use an alternative solvent that boils at lower temperature. Substitute Reactant-A, Reactant-B, solvent, and catalyst with other materials. Prevent excessive feed of Reactant-A, Reactant-B, solvent, and catalyst. Increase the temperature of Reactant-A and Reactant-B streams. Table 9. Comparison between BATCH-ENVOPExpert’s and Experts’ Waste Minimization Alternatives for the Ring-Closure and Extraction Steps operation step ring closure

extraction

experts’ results

BATCH-ENVOPExpert’s solution

Replace Na-OR solvent with volatile alcohol byproduct, Substitute Na-OR and water with other materials. Prevent excessive feed ammonia, or a solid base of Na-OR and water. Use semibatch addition of Na-OR. Use a pump-around Optimize the operating conditions, feed addition and distribution, loop with external heat exchanger to improve mixing in Pot-2. and mixing to reduce byproduct formation. Increase the temperature of Na-OR stream. Increase stirring speed, temperature, and agitation duration during agitation (reaction) operation. Use a pH controller to improve extraction efficiency. Improve the design, operation, and control of the solvent extractor. Eliminate the extraction step by reconsidering water addition. Substitute NaOH and water with other materials. Prevent excessive feed Use alternative separation technology. of NaOH and water. Use an alternative separation technology. Use an ion-exchange process after extraction step for Use a further separation process after the extraction step to recover useful further separation. materials. Install a larger solvent-pot storage tank to facilitate better separation.

Table 10. Comparison between BATCH-ENVOPExpert’s and Experts’ Waste Minimization Alternatives for the Acidification-Halogenation and Filtration Steps operation step

experts’ results

acidification-halogenation Recycle HX byproduct to replace H2SO4 stream. Use hydrogen peroxide to regeneratereuse X2 from HX. Improve the pH control system and operating procedure. Improve reactant mole ratios in Pot-3.

filtration

Collect wash water and reuse in the process. Implement countercurrent, multistage water washing. Reuse aqueous waste from the filtration step to flush piping. Use alternative separation technology.

Lower the slurry feed temperature.

consists of information about the process recipe, materials, process chemistry, and flowsheet. The process-general part, on the other hand, provides the framework for representing the process-specific information in the form of Grafcet, P-graph, functional, and digraph models. The two are linked through an inference engine that implements the methodology using rules and methods. 5. Waste Minimization for Dyelate Manufacturing Process In this section, we report BATCH-ENVOPExpert’s results from waste minimization analysis on the Dyelate production process. In the interest of space, the detailed implementation of BATCH-ENVOPExpert to the case study is not described

BATCH-ENVOPExpert’s solution Direct recycle or recovery-recycle of useful components in HX stream. Use further separation process after the settling step to recover useful materials. Use alternative separation technology. Substitute NaOH, X2, and H2SO4 with other materials. Prevent excessive feed of NaOH, X2, and H2SO4. Increase settling time during settling operation. Improve the design, operation, and control of Pot-3. Optimize the operating conditions, feed addition and distribution, and mixing to reduce byproducts formation. Consider using a reaction agent to suppress byproducts. Consider alternative process chemistry to avoid byproduct formation. Substitute water with other materials. Prevent excessive feed of water. Direct recycle or recovery-recycle of useful components in wastewater pot. Recycle the waste stream for the next batch. Perform the operation one time only. Install a larger wastewater-pot storage tank to facilitate better separation. Improve the design, operation, and control of product filter. Use further separation process after the filtration step to recover useful materials. Use alternative separation technology. Decrease the temperature during filtration operation. Decrease the temperature of the slurry tank.

here. As described in section 3, the process generates three waste streams: wastewater from the condensate pot, HX vapor stream from the Pot-3, and wash water from the filtration unit. The P-graph diagnosis of these streams reveals the following waste sources: excessive charging of organic salt and solvent, reaction byproducts in the vessel, and ineffective separations during the distillation and settling operation in the drowning tank. Based on this diagnosis, the detailed waste minimization alternatives are derived by concentrating on the following recipe-level alternatives: (a) Minimize waste byproducts formed during the reaction steps in Pot-1, Pot-2, and Pot-3, respectively. (b) Reduce the excess feed of reactants, catalyst, solvent, water, and wash water.

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(c) Increase the condensation rate in condenser-1. (d) Improve the efficiency of material separation during the extraction step. Tables 8-10 list the complete set of waste minimization alternatives identified by BATCH-ENVOPExpert for each operation step in the process recipe along with the team’s results. A comparison between BATCH-ENVOPExpert and the team’s results indicates that BATCH-ENVOPExpert’s results are inline with the those of the expert team. BATCH-ENVOPExpert was found to successfully identify almost all the alternatives identified by the team. However, the comparison between the two results shows that the team derives more detailed alternatives than BATCH-ENVOPExpert. For example, the team’s alternative “replace the Na-OR base with ammonia or solid base during the ring-closure step” is more specific than the “substitute Na-OR with other material to avoid byproduct formation” identified by BATCH-ENVOPExpert. In the same way, the team reports an alternative “use semibatch addition of Na-OR” in contrast to “optimize the operating conditions, feed addition and distribution, and mixing to reduce byproduct formation”. For BATCH-ENVOPExpert to propose such detailed alternatives, it requires extra technology-specific knowledge that is currently not available in the knowledge base. A comparison between BATCH-ENVOPExpert and the team’s results also shows that BATCH-ENVOPExpert identified options not mentioned in the experts’ results; for example, the alternative “increase the temperature of Na-OR stream” in the ring-closure operation step is not reported by the team. This is due to the extensive recipe-level heuristics that are embedded in the knowledge library and the variable-level alternatives based on the cause-and-effect analysis. It is also possible that the team studied this option and eliminated it based on a quantitative cost-benefit analysis. For such a study, the quantitative relationships among reactant concentration, reaction pressure, temperature, kinetics, reactor configuration, and other reaction properties would need to be evaluated; this is the subject of our current research. 6. Conclusions In summary, we presented a general approach for automated waste minimization analysis of batch processes as well as an expert system that implements this framework. Waste minimization analysis of batch operations requires four fundamental elements: Grafcets, P-graphs, cause-and-effect models, and functional models. The Grafcet model represents the process recipe, the P-graph model provides a framework for representing material propagation and recipe-level waste minimization suggestions, and the cause-and-effect models and functional models provide a way to distill these high-level alternatives to detailed variable-level ones. Although this paper has focused on batch processes, the proposed methodology can also be applied to continuous operations with the Grafcet model describing the standard operating procedures of the continuous plant, with the P-graph, cause-and-effect, and functional models remaining unchanged. BATCH-ENVOPExpert has been tested successfully on an industrial herbicide manufacturing case study. The findings show that the methodology is able to derive suitable waste minimization suggestions that are in-line with the human experts’ solutions. While performance of BATCH-ENVOPExpert is very promising, we realize the drawbacks of the qualitative analysis and are currently developing a unified qualitative-quantitative framework that incorporates quantitative material and energy balance models, along with energy utilization (exergy), process

economics, and environmental impact (through the WAR algorithm25). The proposed approach can also be extended to address batch-to-batch conformity problem. As batch operation is highly dynamic, minor run-to-run variations from the normal operating condition can result in out-of-spec products, which eventually would become wastes. For example, failure to provide sufficient backflush during the filtration operation may result in off-specification products. The P-graph approach provides a mechanism to identify the on-line factors that affect each operation and have a potential role in batch failure. Suitable correction mechanism in the event of faults can also be generated by using the functional models. Such on-line decision support for batch fault diagnosis and rectification is another direction of our current research. Literature Cited (1) Halim, I.; Srinivasan, R. Systematic Waste Minimization in Chemical Processes: Part I. Methodology. Ind. Eng. Chem. Res. 2002, 41 (2), 196. (2) Halim, I.; Srinivasan, R. Systematic Waste Minimization in Chemical Processes: Part II. Intelligent Decision Support System. Ind. Eng. Chem. Res. 2002, 41 (2), 208. (3) Sheldon, R. A. Catalysis: The Key to Waste Minimization. J. Chem. Technol. Biotechnol. 1997, 68, 381. (4) USEPA. Principal Findings: The U.S. Specialty-Batch Chemical Industry Draft, 2000, http://www.resourcesaver.com/file/sectorstar/program_ 317.pdf. (5) Halim, I.; Srinivasan, R. An Integrated Decision Support System for Waste Minimization Analysis in Chemical Processes. EnViron. Sci. Technol. 2002, 36 (7), 1640. (6) Wang, Y. P.; Smith, R. Time Pinch Analysis. Chem. Eng. Res. Des. 1995, 73, 905. (7) Grau, R.; Graells, M.; Corominas, J.; Espuna, A.; Puigjaner, L. Global Strategy for Energy and Waste Analysis in Scheduling and Planning of Multiproduct Batch Chemical Processes. Comput. Chem. Eng. 1996, 20 (6-7), 853. (8) Almato, A.; Espuna, A.; Puigjaner, L. Optimisation of Water Use in Batch Process Industries. Comput. Chem. Eng. 1999, 23 (10), 1427. (9) Lee, Y. G.; Malone, M. F. Batch Process Planning for Waste Minimization. Ind. Eng. Chem. Res. 2000, 39 (6), 2035. (10) Foo, C. Y.; Manan, Z. A.; Yunus, R. M.; Aziz, R. A. Synthesis of Mass Exchange Network for Batch Processes-Part I: Utility Targeting. Chem. Eng. Sci. 2004, 59, 1009. (11) Houghton, C.; Sowerby, B.; Crittenden, B. Clean Design of Batch Processes. In Case Studies in EnVironmental Technology; Sharratt, P., Sparshott, M., Eds.; Institution of Chemical Engineers: Rugby, Warwickshire, UK, 1996. (12) Douglas, J. M. Conceptual Design of Chemical Processes; McGrawHill: New York, 1988. (13) Mulholland, K. L.; Dyer, J. A. Pollution PreVention: Methodology, Technologies and Practices; American Institute of Chemical Engineers: New York, 1999. (14) Isalski, W. H. ENVOP for waste minimization. EnViron. Prot. Bull. 1995, 34, 16. (15) Viswanathan, S.; Johnsson, C.; Srinivasan, R.; Venkatasubramanian, V.; Arzen, K. E. Automating Operating Procedure Synthesis for Batch Processes: Part I. Knowledge Representation and Planning Framework. Comput. Chem. Eng. 1998, 22 (11), 1673. (16) International Society for Measurement and Control. ISA S88.011995: Batch Control part 1: models and terminology; ANSI/ISA & City: Research Triangle Park, NC, 1995. (17) Friedler, F.; Tarjan, K.; Huang, Y. W.; Fan, L. T. Combinatorial Algorithms for Process Synthesis. Comput. Chem. Eng. 1992, 16, S313. (18) Friedler, F.; Tarjan, K.; Huang, Y. W.; Fan, L. T. Graph-Theoretic Approach to Process Synthesis: Axioms and Theorems. Chem. Eng. Sci. 1992, 47, 1973. (19) Friedler, F.; Tarjan, K.; Huang, Y. W.; Fan, L. T. Graph-Theoretic Approach to Process Synthesis: Polynomial Algorithm for Maximal Structure Generation. Comput. Chem. Eng. 1993, 17, 929. (20) Friedler, F.; Varga, J. B.; Fan, L. T. Algorithmic Approach to the Integration of Total Flowsheet Synthesis and Waste Minimization. In Pollution PreVention Via Process and Product Modifications; El-Halwagi,

Ind. Eng. Chem. Res., Vol. 45, No. 13, 2006 4705 M. M., Petrides, D. P., Eds.; American Institute of Chemical Engineers: New York, 1994. (21) Friedler, F.; Varga, J. B.; Fan, L. T. Decision Mapping: A Tool for Consistent and Complete Decisions in Process Synthesis. Chem. Eng. Sci. 1995, 50, 1755. (22) Srinivasan, R.; Venkatasubramanian, V. Automating HAZOP Analysis of Batch Chemical Plants: Part I. Knowledge Representation Framework. Comput. Chem. Eng. 1998, 22, 1345. (23) Halim, I.; Srinivasan, R. Design Synthesis for Simultaneous Waste Source Reduction and Recycling Analysis in Batch Processes. In European Symposium on Computer Aided Process Engineerings15; Puigjaner, L., Ed.; Elsevier Science: Amsterdam, 2005.

(24) Rodriguez-Martinez, A.; Lopez-Arevalo, I.; Banares-Alcantara, R.; Aldea, A. Multi-model knowledge representation in the retrofit of processes. Comput. Chem. Eng. 2004, 28, 781. (25) Cabezas, H.; Bare, J. C.; Mallick, S. K. Pollution prevention with chemical process simulators: the generalized waste reduction (WAR) algorithmsfull version. Comput. Chem. Eng. 1999, 23 (4-5), 623-634.

ReceiVed for reView July 5, 2005 ReVised manuscript receiVed April 3, 2006 Accepted April 5, 2006 IE050792B