Optimal design of batch plants under economic and ecological considerations: Application to a biochemical batch plant (original) (raw)
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Computers & Chemical Engineering, 2006
This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the size of the equipment items as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a genetic algorithm (GA) with a discrete-event simulator (DES). Another incentive to use this kind of optimization method is that, there is no easy way of calculating derivatives of the objective functions, which then discards gradient optimization methods. To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a multiobjective genetic algorithm (MOGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design.
Ecodesign of Batch Processes: Optimal Design Strategies for Economic and Ecological Bioprocesses
International Journal of Chemical Reactor Engineering, 2007
This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the equipment item sizes as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a Multicriteria Genetic Algorithm (MUGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design.
Industrial & Engineering Chemistry Research, 2005
This paper presents a framework for optimal design of batch plants. It consists of a master optimization algorithm, i.e., a genetic algorithm (GA) coupled to a discrete-event simulation (DES). The innovative aspect of this work is the use of "shortcut" models included in the DES for describing the unit operations. The example of a protein production process serves as an illustration to show the effectiveness of the approach. The major interest is that the use of local models for unit operations allows the computation of an environmental index in combination with an economic indicator. The optimization framework determines the plant structure (parallel units, allocation of intermediate storage tanks), the batch plant decision variables (equipment sizes, batch sizes) and the process decision variables (e.g., final concentration at selected stages, volumetric ratio of phases at the liquid-liquid extraction, ...). The results show that a plant configuration can be easily improved, only by changing the campaign policy for instance. Optimization results for monocriterion cases (miminization of investment cost and two environmental impact criteria based on biomass produced and amount of solvent used) illustrate the efficiency of the methodology, finding a set of "good" solutions which may be interesting for the decision maker.
Constraint handling strategies in Genetic Algorithms application to optimal batch plant design
Chemical Engineering and Processing: Process Intensification, 2008
Optimal batch plant design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming (MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one.
Optimal design of multiproduct batch chemical plant using NSGA-II
Asia-Pacific Journal of Chemical Engineering, 2006
ABSTRACT The optimal design of a multiproduct batch chemical plant is formulated as a multiobjective optimization problem, and the resulting constrained mixed-integer nonlinear program (MINLP) is solved by the nondominated sorting genetic algorithm approach (NSGA-II). By putting bounds on the objective function values, the constrained MINLP problem can be solved efficiently by NSGA-II to generate a set of feasible nondominated solutions in the range desired by the decision-maker in a single run of the algorithm. The evolution of the entire set of nondominated solutions helps the decision-maker to make a better choice of the appropriate design from among several alternatives. The large set of solutions also provides a rich source of excellent initial guesses for solution of the same problem by alternative approaches to achieve any specific target for the objective functions. Copyright © 2006 Curtin University of Technology and John Wiley & Sons, Ltd.
A new methodology for the optimal design and production schedule of multipurpose batch plants
Industrial & Engineering Chemistry Research, 1989
A nonlinear mathematical programming formulation for the multipurpose batch plant design problem is presented. It simultaneously provides both the optimal equipment sizes and the best series of multiproduct campaigns a t one step by taking into account the whole space of feasible production runs. By making minor changes to some problem constraints, the proposed modeling can consider the use of parallel units at certain stages. The units can be assigned to either the same or distinct products. Moreover, the proposed modeling can also handle practical situations where intermediate and salable products are to be manufactured. This algorithmic approach has been successfully applied to discover the best design and production policy in several examples. Optimal solutions to problems involving as many as 7 products and 10 stages have been determined by solving a single, small-size, nonlinear program. T h e method is computationally efficient even if the starting point is far from the optimum. Chemical batch plants are generally grouped into two classes, multiproduct and multipurpose batch facilities. In the former ones, a range of products are manufactured by running a sequence of single-product campaigns. Each of the N desired products undergoes a series of M processing tasks. These are accomplished in a set of M equipment modules or batch stages, each one carrying out a distinct physical/chemical task. A batch of product is transferred to the next stage after completion of the longest task. There is no intermediate storage between stages, and the plant is operated on the zero-wait mode. Important contributions to the mathematical problem description and the understanding of the optimal problem patterns have already been made by several authors (
Computers & Industrial Engineering, 2008
This work deals with multiobjective optimization problems using Genetic Algorithms (GA). A MultiObjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process design and operability involve structural and decisional choices as well as the determination of operating conditions. In this paper, a design of a basic MOGA which copes successfully with a range of typical chemical engineering optimization problems is considered and the key points of its architecture described in detail. Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome. Four mathematical functions were used as a test bench. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto optimal solutions. Then, the results of two multiobjective case studies in batch plant design and retrofit were presented, showing the flexibility and adaptability of the MOGA to deal with various engineering problems.
Genetic algorithms for the scheduling of multiproduct batch plants within uncertain environment
Computer Aided Chemical Engineering, 2007
This study addresses the problem of batch plant scheduling. In addition, uncertainty on product demands is considered through probabilistic-based methods. In the resulting two-stage stochastic programming problem, the objective is to maximize an Expected Profit Value (EPV) while respecting a constraint forcing the makespan to be lower than a time horizon. A Genetic Algorithm (GA) is proposed for the solution of a multiproduct example. The variable encoding requires special attention. Computational tests are first carried out with a deterministic model to validate the GA efficiency. Then, different runs with different scenario sets highlight the existence of various solution classes, characterized by specific numbers of batches manufactured for each product. Further analysis finally enables to discuss if each schedule is really the best-fitted to the scenario set for which it has been determined.
Industrial & Engineering Chemistry Research, 2007
In this paper, a heuristic method is presented for the simultaneous solution of the synthesis and design problems of batch plants. A detailed nonlinear program (NLP) model is developed that considers a superstructure to represent all the configuration options for the plants. Usually, similar works in this area assume as a hard constraint the use of single-product campaigns. In this work, mixed campaigns are introduced to pose problems where this is a significant condition. Specific scheduling constraints are formulated, and a resolution strategy is presented to solve the problem. This formulation is valid for multiproduct batch plants and a special type of multipurpose plants where products follow different production paths sharing some but not all the stages. The approach is implemented for a Torula yeast, brandy, and bakery yeast production plant. To assess the method, different mixed campaigns are modeled. Economical and synthesis, design, and operational results are also reported.