Optimal Investment Cost Under Uncertainty with Genetic Algorithms Application: Plants to Protein Production

Youness El Hamzaoui

Facultad de Ingeniería, Calle 56 No. 4 Esq., Avenida Concordia Col., Benito Juárez C.P. 24180 Cd. del Carmen, Campeche, México

Dr. Youness El Hamzaoui, Facultad de Ingeniería, Calle 56 No. 4 Esq., Avenida Concordia Col., Benito Juárez C.P. 24180 Cd. del Carmen, Campeche, México.

Keywords: Investment Cost; Genetic Algorithm; Gaussian Process Modeling; Batch Process; Optimal Design

Abstract

This work deals with the problem of the search for optimal investment cost of multiproduct batch chemical plants found in a chemical engineering process with uncertain demand. The aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem by using Genetics Algorithms (GAs). This GAs consider an effective mixed continuous discrete coding method with a four point crossover operator, which take into account, the uncertainty on the demand using Gaussian process modeling. The results (number and size of equipment, investment cost, production time (Hi), CPU time and Idle times in plant) obtained by GAs are the best.
This methodology can help the decision makers and constitutes a very promising framework for finding a set of “good solutions”.

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