Optimization of Operating Conditions for Protein Production Plants using Mixed Integer Nonlinear Programming and Genetic Algorithms

Abstract

This work deals with the problem of the search for optimal investment cost of multiproduct batch chemical plants found in \n a chemical engineering process with uncertain demand. The aim of this work is to minimize the investment cost and find \n out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem by \n using Genetic Algorithms (GAs). This GAs consider an effective mixed continuous discrete coding method with a four point \n crossover operator, which take into account, the uncertainty on the demand using Gaussian process modeling. Experiments \n indicated that relatively good results could be obtained using 4-point crossover with an applied rate of 0.7 promised to give best \n performance. The results (number and size of equipment, investment cost, production time (Hi), CPU time and Idle times in \n plant) obtained by GAs are the best.