Parallel and distributed computation in chemical process optimization

Parallel computing and algorithms for chemical process optimization problems are reviewed. Details of cluster of workstations, a relatively recent development, are given to highlight its advantages compared with other approaches in parallel computing. As SQP has emerged as the algorithm of choice for solving large-scale chemical process optimization problems, several parallel strategies for SQP are also presented. Finally, a parallel strategy utilizing cluster of workstations is proposed to solve chemical process optimization problems efficiently. Observations indicate that the degree of granularity plays a major role in this approach. It should be carefully schemed to balance the load of communication and the distributed calculation steps. Computing results on a distillation column optimization problem demonstrate the efficiency of this approach.