Multipurpose Batch Plants Research Articles

Optimization of preventive maintenance strategies in a multipurpose batch plant: application to semiconductor manufacturing

This paper addresses the problem of preventive maintenance (PM) strategy optimization in a semiconductor manufacturing environment, with the objective of minimizing maintenance costs. The approach developed takes into account the interaction of production and maintenance aspects. For this purpose, a discrete-event production-oriented simulator (MELISSA-C++) has been extended to incorporate equipment failures and maintenance operations, thus modeling residual breakdowns, occurring in a combined corrective/PM context. The usefulness of the simulation tool has also been demonstrated for the estimation of both direct and indirect maintenance costs, which are impossible to determine empirically due to the reentrant nature of product flows in a semiconductor manufacturing facility. The results obtained have confirmed the marked effect of equipment characteristics (bottleneck or non-limiting step) on maintenance cost evaluation. Following a tutorial example, typical results are presented and analyzed.

Combinatorial framework for effective scheduling of multipurpose batch plants

AbstractThe production scheduling of multipurpose batch plants is considered. A novel graph representation that looks at the specific characteristics of production scheduling in chemical processes is proposed. In these graphs, the nodes represent the production tasks, the arcs of the precedence relationships among them. The representation is flexible enough to consider a wide variety of production structures, including complex recipes. Both nonintermediate storage (NIS) and unlimited intermediate storage (UIS) transfer policies can be considered simply by choosing the appropriate precedence relationships. This representation provides the opportunity of incorporating highly efficient graph algorithms together with an appropriate branch‐and‐bound algorithm for solving multipurpose scheduling problems. The efficiency of the proposed method is established by solving examples and a complex case study.

Optimal Design of Batch Plants with Uncertain Demands Considering Switch Over of Operating Modes of Parallel Units

This paper addresses the problem of the optimal design of batch plants with uncertain demands of products' quantities. A conceptual two-stage stochastic programming problem is formulated with an objective function containing investment costs and expected revenues from products. With the proposed formulation, the mode of operation of parallel units could be optimized so that, for different products, the operation of parallel units may take a different mode (i.e., in-phase or out-of-phase). The original formulation is transformed into different forms either for multiproduct and multipurpose batch plants. A hybrid algorithm for solving the problems is developed. Compared with previous work where the operating mode of parallel units was considered to be fixed during operation, more economic optimality is obtained.

A multi-agent system for chemical supply chain simulation and management support

Modern chemical production is customer-driven and the desired delivery time for the products is often shorter than their campaign length. In addition, the raw materials supplying time is often long. These features make it desirable to provide tools to support collaborative supply chain decision making, preferably over the Internet, and where there are conflicts, compromise decisions can be quickly reached and the effects of the decisions can be quantitatively simulated. This paper des cribes such a multi-agent system (MAS) that can be used to simulate the dynamic behaviour and support the management of chemical supply chains over the Internet. Geographically distributed retailers, logistics, warehouses, plants and raw material suppliers are modelled as an open and re-configurable network of co-operative agents, each performing one or more supply chain functions. Communication between agents is made through the common agent communication language KQML (knowledge query message language). A t the simulation layer, the MAS allows distributed simulation of the chain behaviour dynamically, so that compromise decisions can be rapidly and quantitatively evaluated. Because in a chemical supply chain the scheduling of the plant often dominates the chain performance, an optimum scheduling system for batch plants is integrated into the MAS. The functions of the system are illustrated by reference to a case study for the supply and manufacture using a multi-purpose batch plant of paints and coatings.

Optimal Design and Layout of Industrial Facilities: An Application to Multipurpose Batch Plants

A mixed integer linear programming (MILP) formulation for the optimal design and layout of generic industrial facilities was proposed in part I of this work (see preceding paper). The applicability of the model is explored in this paper, through a study of the simultaneous design and layout of a batch multipurpose plants. Using as a basis the design model proposed by Barbosa-Póvoa and Macchietto (Comput. Chem. Eng. 1994, 18, 1013−1042), detailed design and layout aspects are studied in a single-level model. The resulting model determines simultaneously the optimal plant topology and layout over a two-dimensional space. In addition, the optimal plant operating conditions, such as schedule and resources consumption and production, are also obtained. Again, the problem is formulated as a mixed integer linear problem (MILP) in which binary variables are introduced to characterize operational and topological choices. The applicability of the proposed model is demonstrated through the solution of a set of representative examples.

A solution approach to the design of multi-period, multipurpose batch plants

A deterministic model for multipurpose, multiperiod batch plants was presented in a linearized form to predict the future design according to the change of demand by using a modified Benders’ Decomposition. The OSL code offered by the IBM corporation as optimizer was employed for solving several example problems. The decomposition method was successful, showing remarkable reduction in the computing times as compared with those of the direct solution method. Also the heuristic used as a solution approach for the multiperiod model provided an efficient methodology to the block-structured problem by dividing the large overall problem into the manageable single period blocks.

A New Simple Effective Continuous-time Model for Scheduling of General Batch Plants.

Batch processes are widely used in producing high-value chemical products for its good flexibility. The problem of short-term scheduling of batch plants is to determine the optimal utilization of available resources over a given time horizon. Models of most previous works in this domain rely on the definition of time slots or time events so that they involve many binary variables and constraints. In this paper, a new MILP model for scheduling of both multi-product and multi-purpose batch plants with unit-depended changeovers is introduced. Problems are described based on the concept of task in order to deal with complex instances. The proposed formulation uses a continuous time domain representation that does not rely on the definition of time slots or time events. Binary variables for sequencing are defined to indicate whether one task is prior to another in the processing sequence without concerning their accurate position. The model is also simplified by considering the specialty of symmetrical and complemental feature of the binary variables. In a result the number of both binary variables and constraints has been reduced significantly. Moreover, different heuristic rules for preordering can be flexibly embedded into the new model and directly assign the value of binary variables. Therefore much CPU time is saved even maybe without loss of rigorous optimality. Several case studies are presented that illustrate the efficiency of the new model. Comparisons with some previous works are also provided.

A Novel Continuous-Time MILP Formulation for Multipurpose Batch Plants. 1. Short-Term Scheduling

In this paper, a new concept, the so-called state sequence network representation, is presented. This representation is based on states only, eliminating the use of tasks and units. Using this new representation as a basis, a novel continuous-time MILP formulation for short-term scheduling of multipurpose batch plants is developed. The presented formulation can readily be extended to intermediate due date scenarios. Also presented in this paper is a new model for the duration constraints that explores degrees of freedom that are intrinsic in batch operations. These are due to operator intervention, raw material purity, and different catalyst types. Time points are used to denote the use or production of a particular state. This formulation leads to the least number of binary variables and much better results when compared to other continuous-time formulations published in the literature. The reduced number of binary variables is a result of considering states only, thereby eliminating binary variables corresponding to tasks and units. This method has been applied to literature examples and industrial problems which show significant improvement in reducing the number of binary variables and, hence, CPU times. The last section of this paper introduces the concept of units aggregation in reducing the binary dimension of large-scale problems. This makes it possible for the method to solve large-scale industrial problems. In the second paper of this series (Zhu, X. X.; Majozi, T. Ind. Eng. Chem. Res. 2001, 40, 5621−5634), it will be demonstrated how this formulation is applied in the context of integrated planning and scheduling.

Novel Continuous Time MILP Formulation for Multipurpose Batch Plants. 2. Integrated Planning and Scheduling

While the part 1 focuses on the application of the proposed formulation to scheduling, this paper focuses mainly on the integration of planning and scheduling in multipurpose batch plants. In dealing with this problem, the method presented in part 2 exploits the mathematical structure of the overall plant model. It is discovered that the overall model exhibits a block angular structure that is decomposed by raw material allocation. If raw materials can be allocated optimally to individual plants, solving individual models for each plant can produce the same results as solving an overall model for the site. This discovery leads to a decomposition strategy that consists of two levels. In the first level, only planning decisions are made, and the objective function is the maximization of the overall profit. The results from solving the planning model give optimal raw material allocation to different plants. In the second level, the raw material targets from the first (planning) level are incorporated into the scheduling submodels for each plant, which are solved independently without compromising global optimality. The objective function for each scheduling submodel is the maximization of product throughput. The scheduling level uses the concept of the state sequence network presented in part 1. Solving scheduling submodels for individual plants rather than the overall site model leads to problems with much a smaller number of binary variables and, hence, shorter CPU times. If conflicts arise, i.e., the planning targets are too optimistic to be realized at the scheduling level, the planning model is revisited with more realistic targets. This eventually becomes an iterative procedure that terminates once the planning and scheduling solutions converge within a specified tolerance. In this way, the planning model acts as coordination for scheduling models for individual plants. An industrial case study with three chemical processes is presented to demonstrate the effectiveness of this approach.

Real-time control/scheduling for multi-purpose batch plants

The current trend in the chemical process industry is to operate flexible multi-purpose batch plants. This configuration is capable of manufacturing a variety of chemical products using different combinations of available process resources. Flexible and dynamic control and scheduling are critical control activities necessary to realize agile manufacturing in a multi-purpose batch plant. In this paper, the real-time control/scheduling of a multi-purpose batch chemical plant is considered. A real-time control and scheduling approach, based on predictive control and situation calculus, is developed. Simulation results for the mean flow time using the proposed approach are compared with results obtained using a conventional dispatching rule. Statistical analysis shows that the performance of the proposed approach is preferred for the experimental plant.

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A computerized and integrated approach for heat exchanger network design in multipurpose batch plants

Significant savings in the utility cost of batch plants can be obtained by heat integration. In this study, an integrated mathematical programming approach is developed for the determination of the cost optimal heat exchanger network for multipurpose batch chemical plants. A single step, interactive computer program (BatcHEN) which is developed for the determination of the campaigns (i.e. the set of products which can be produced simultaneously), the heat exchange areas of all possible heat exchangers in the campaigns and finally the heat exchanger network are all discussed. A matrix search algorithm is used for the determination of the campaigns. Heat exchange areas for the possible heat exchangers are found by solving a nonlinear optimization model with a grid search algorithm. Finally the heat exchanger network optimization is modeled as a mixed integer linear programming problem and then solved by the modeling and optimization software GAMS/XA.

Design, synthesis and scheduling of multipurpose batch plants via an effective continuous-time formulation

Design, synthesis and scheduling issues are considered simultaneously for multipurpose batch plants. An earlier proposed continuous-time formulation for scheduling is extended to incorporate design and synthesis. Processing recipes are represented by the State-Task Network (STN). The superstructure of all possible plant designs is constructed according to the potential availability of all processing/storage units. The proposed model takes into account the trade-offs between capital costs, revenues and operational flexibility. Computational studies are presented to illustrate the effectiveness of the proposed formulation. Both linear and nonlinear models are included, resulting in MILP and mixed-integer nonlinear programming (MINLP) problems, respectively. The MILP problems are solved using a branch and bound method. Globally optimal solutions are obtained for the nonconvex MINLP problems based on a key property that arises due to the special structure of the resulting models. Comparisons with earlier approaches are also presented.

On-line fault diagnosis system support for reactive scheduling in multipurpose batch chemical plants

In this work, a simple strategy for the development and implementation of a fault diagnosis system (FDS) that interacts with a schedule optimiser in batch chemical plants is presented. The proposed FDS consists of an artificial neural network (ANN) structure supplemented with a knowledge-based expert system (KBES) in a block-oriented configuration. The system combines the adaptive learning diagnostic procedure of the ANN and the transparent deep knowledge representation of the KBES. The information needed to implement the FDS includes a historical database of past batches, a Hazard and Operability (HAZOP) analysis and a model of the plant. Two motivating case studies are presented to show the results of the proposed methodology. The first corresponds to a fed-batch reactor. In this example, the FDS performance is demonstrated through the simulation of different process faults. The second case study corresponds to a multipurpose batch plant. In this case, the results of reactive scheduling are shown by simulating different abnormal situations. A performance comparison is made against the traditional scheduling approach without the support of the proposed FDS.

An Improved RTN Continuous-Time Formulation for the Short-term Scheduling of Multipurpose Batch Plants

This paper presents an improved general mathematical programming formulation for optimal scheduling of batch processes based on the resource−task network (RTN) representation. The formulation uses a continuous-time representation and results in a mixed integer linear programming problem. It is a relaxation of the problem presented by Schilling (Schilling, G. Optimal Scheduling of Multipurpose Plants. Ph.D. Thesis, University of London, London, U., K., 1997). By allowing, if possible, finite storage within the processing tasks resource equipments of the involved raw materials and/or products, the proposed approach leads to simpler and less degenerate mathematical models. These models can be solved in significantly less CPU time, when compared to other RTN continuous-time formulations. Three published example problems are presented to illustrate the effectiveness of the proposed formulation. Finally, we show that the STN-based continuous-time scheduling formulation of Ierapetritou and Floudas (Ierapetritou,...

Retrofit design of multipurpose batch plants with multiple production routes

This work analyzes possible alterations of configuration of an existing multipurpose batch plant for which new production targets and selling profits have been specified. New equipment can be added to the batch plant in the form of a retrofit in order to become a more economical and optimal design. The retrofit problem is posed as a mixed-integer formulation (MILP). The application of the proposed model is illustrated using three examples where can be verified that the method is efficient and of easy application, when compared of the others strategies to solve the retrofit problem.

Scheduling multipurpose batch process industries with no-wait restrictions by simulated annealing

Scheduling problems in multipurpose batch process industries are very hard to solve because of the job shop like processing structure in combination with rigid technical constraints, such as no-wait restrictions. This paper shows that scheduling problems in this type of industry may be characterized as multiprocessor no-wait job shop problems with overlapping operations. A simulated annealing algorithm is proposed that obtains near-optimal solutions with respect to makespan. This paper shows that the no-wait restrictions require several adaptations of the neighborhood structure used by simulated annealing. The performance of the algorithm is evaluated by scheduling industrial instances from a multipurpose batch plant in the pharmaceutical industry. Our results indicate that simulated annealing consistently gives better results for a number of realistic instances than simple heuristics within acceptable computation time.

Utilization of processing time windows to enhance planning and scheduling in short term multipurpose batch plants

The problem considered is short term scheduling in multipurpose batch plants when the main objective is adherence to due dates. The solution approach contemplates a two-phase procedure. The planning phase consists of processing time-windows for all the necessary batches to meet the products' demand. This information is extensively uses in the scheduling phase. Three approaches are considered in the last phase, a MILP formulation using uniform discrete time representation; constrained based search (CBS) and simulated annealing (SA). And assessment is made of these techniques in heavily constrained problems and how these approaches deal with equipment units sharing and constraints imposed by material balances and storage conditions. The advantages of the two-phase approach are shown in each case with illustrative examples.

Scheduling of the batch annealing process — deterministic case

This study addresses the short-term scheduling problem for the batch annealing process in the heat treatment of steel coils. Scheduling in the batch annealing process has the following objective: determine the optimal movement plan for satisfying high utilization of shared equipment (a crane, furnaces, and coolers) among parallel bases. This process is a multipurpose batch plant which may contain re-entrant flows in the batch annealing cycles and units in parallel, including sequence-dependent setup time and transfer time. A novel mixed-integer linear programming (MILP) model for the scheduling of the batch annealing process is proposed. For large size problems of the batch annealing process, a scheduling solution can be obtained by using a novel algorithm. Although this procedure does not guarantee optimality of the solution, the computational efficiency was improved and feasible solutions for large problems can be obtained in a reasonable CPU time. Application of the proposed model and algorithm is illustrated with several example problems including a problem that consists of 29 pieces of equipment and up to 24 bases.