Comparison of particle swarm optimization and genetic algorithm for multiproduct batch plant design of protein production

Abstract

Pharmaceutical researchers and biotechnology companies are devoted to developing medicines, such as: therapeutic proteins, human insulin, vaccines for hepatitis, food grade protein, chymosin detergent enzyme, and cryophilic protease. This allows patients to live longer, heathier, and more productive. However, in recent years, there has been an increased interest development of systematic method for the design of batch process in chemicals, food products, and pharmaceutical industries. Basically, batch plants are composed of items operating in a discontinuous way, where each batch then visits a fixed number of equipment items, as required by a given synthesis sequence so called production recipe. Many works in the literature on batch process design are based on expressions that relate the batch sizes linearly with the equipment sizes.1–10 The number required of volume and size of parallel equipment units in each stage is to be determined. Nevertheless, the design of batch plants requires involving how equipment may be utilized. However you look at it the optimal design of a multiproduct batch chemical process involves the production requirement of each product and the total production time available for all products has been considered. The number and size of parallel equipment units in each stage as well as the location and size of intermediate storage are to be determined in order to minimize the investment cost.