Optimal Design Of Batch Plants Research Articles

Optimal design for an ethanol plant combining first and second-generation technologies

The synthesis and optimal design of batch plants is addressed in this study. It was applied to the technology of conventional ethanol production in a Cuban distillery using the product of enzymatic hydrolysis of pretreated bagasse as another sugared substrate, starting from laboratory results. The optimal configuration of stages, the number of units in each stage, the unit sizes and minimum total production cost are obtained from the global optimization model and the proposed superstructure. This global model is a mixed integer nonlinear programming (MINLP) formulation, which is represented and resolved by the Professional Software, General Algebraic Modeling System (GAMS) version 23.5 applying DICOPT Solver. Different scenarios are analyzed: attaching pretreatment and enzymatic hydrolysis of bagasse to a conventional distillery plant, selling ethanol, or selling the furfural as by-product if there is a guaranteed market. With this, an actual net present value (VNA) of USD 44´893 358.7 and 1.51 years of Payback Period (PP) are obtained.

Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must constitute a basic part of the design problem. Rather than resorting to a traditional probabilistic approach for modeling the imprecision on product demands, this work proposes an alternative treatment by using fuzzy concepts. The design problem is tackled by introducing a new approach based on a multiobjective genetic algorithm, combined wit the fuzzy set theory for computing the objectives as fuzzy quantities. The problem takes into account simultaneous maximization of the fuzzy net present value N P ˜ V and of two other performance criteria, i.e. the production delay/advance and a flexibility index. The delay/advance objective is computed by comparing the fuzzy production time for the products to a given fuzzy time horizon, and the flexibility index represents the additional fuzzy production that the plant would be able to produce. The multiobjective optimization provides the Pareto's front which is a set of scenarios that are helpful for guiding the decision's maker in its final choices. About the solution procedure, a genetic algorithm was implemented since it is particularly well-suited to take into account the arithmetic of fuzzy numbers. Furthermore because a genetic algorithm is working on populations of potential solutions, this type of procedure is well adapted for multiobjective optimization.

An AHP-based decision-making tool for the solution of multiproduct batch plant design problem under imprecise demand

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessarily involves the way that equipment may be utilized, which means that plant scheduling and production must form an integral part of the design problem. This work relies on a previous study, which proposed an alternative treatment of the imprecision (demands) by introducing fuzzy concepts, embedded in a multi-objective Genetic Algorithm (GA) that takes into account simultaneously maximization of the net present value ( NPV) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The results showed that an additional interpretation step might be necessary to help the managers choosing among the non-dominated solutions provided by the GA. The analytic hierarchy process (AHP) is a strategy commonly used in Operations Research for the solution of this kind of multicriteria decision problems, allowing the apprehension of manager subjective judgments. The major aim of this study is thus to propose a software integrating the AHP theory for the analysis of the GA Pareto-optimal solutions, as an alternative decision-support tool for the batch plant design problem solution.

A fuzzy multiobjective algorithm for multiproduct batch plant: Application to protein production

This paper addresses the problem of the optimal design of batch plants with imprecise demands and proposes an alternative treatment of the imprecision by using fuzzy concepts. For this purpose, we extended a multiobjective genetic algorithm (MOGA) developed in previous works, taking into account simultaneously maximization of the net present value (NPV) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The former is computed by comparing the fuzzy computed production time to a given fuzzy production time horizon and the latter is based on the additional fuzzy demand that the plant is able to produce. The methodology provides a set of scenarios that are helpful to the decision's maker and constitutes a very promising framework for taken imprecision into account in new product development stage.

Optimal design of batch plants under economic and ecological considerations: Application to a biochemical batch plant

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 considerations, 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.

A Framework for Multiproduct Batch Plant Design with Environmental Consideration: Application To Protein Production

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.

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.

Optimal design of batch plants with single production routes

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTOptimal design of batch plants with single production routesNaim M. Faqir and Iftekhar A. KarimiCite this: Ind. Eng. Chem. Res. 1989, 28, 8, 1191–1202Publication Date (Print):August 1, 1989Publication History Published online1 May 2002Published inissue 1 August 1989https://doi.org/10.1021/ie00092a012RIGHTS & PERMISSIONSArticle Views41Altmetric-Citations18LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-Alerts Get e-Alerts