Sequential Quadratic Programming (SQP) is the most efficient algorithm for nonlinear optimization.But a penalty function is usually used for linear search

causing some problems.Filter-SQP developed by Roger Fletcher and Sven Leyffer avoids using penalty function.In the view of filter-SQP

NLP problem has two objectives

one is minimizing objective function

the other is satisfying the constraints.The concept of filter is proposed on the basis of these two objectives.In this paper flowsheet optimization using filter-SQP in modular simulator environment was studied.Infeasible path strategy was used and the constraint function was composed of tear stream equation

specific design and unsatisfied inequality constraint.When filter could not find a step as the starting point of the next iteration

in order to avoid algorithm failure three strategies were used.They were restarting strategy

converging recycle strategy and feasible path strategy.A successive scaling strategy was proposed for filter-SQP to improve the efficiency of optimization.A case study of process optimization with filter-SQP was very encouraging.