Current trends toward increased model detail and rigorous optimization of chemical process accelerate the need to solve very large systems.Even with the high performance computers nowadays

there still exists many difficulties for a single computer to solve large-scale chemical process optimization problems.In this paper

parallel computing and algorithms for chemical process optimization problems are reviewed.Details of cluster of workstations

a relatively recen 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.