Improved large-scale reduced SQP algorithm for process optimization

To improve the efficiency and stability of reduced sequential quadratic programming(rSQP) algorithm,and extend it to solve much larger scale problems of process systems,an improved rSQP algorithm for optimization of large-scale process systems was presented.In the improved algorithm,a new rule for selection of basis was adopted.Basis was adjusted at every iteration according to the rule,so the stability can be improved obviously.Also,an integrated line search of filter method,with the advantages of normal line search method and filter method,was incorporated into the algorithm to obtain steplength.Numerical results of some benchmark examples and three large examples with variable dimension show that the proposed algorithm can reduce the number of iterations and function evaluations.and is much more effective than standard sequential quadratic programming(SQP) algorithm.Also the stability of rSQP algorithm is improved.