Optimal Design of Multiproduct Batch Chemical Process Using Genetic Algorithms

For the first time, genetic algorithms (GAs) are applied to the optimal design of the multiproduct batch chemical process (MBCP) successfully. An effective multiparameter crossed binary coding method is developed. The GAs have the advantages of no special demand for initial values of decision variables, lower computer storage, and less CPU time for computation. Better results are obtained in comparison with the results of mathematical programming (MP) and simulated annealing (SA). The effectiveness of GAs in solving the complex design problem of batch chemical process is demonstrated.