A distinct advantage of sequential quadratic programming (SQP) is global convergence that ensures convergence from a remote starting point. When the constraints are highly nonlinear such as in flowsheet optimization, however, locally convergent Newton's method used in SQP as the equation-solving tool may deteriorate the behavior of convergence. Our recognition that this issue remains to be resolved motivated us to study a two-tier SQP approach where the constraints for process simulation consisting of nonlinear equations are decoupled from the KKT system in order to block the adverse influence of nonlinearity on global convergence. Our equation oriented (EO) process simulator (Ishii and Otto, 2011) is employed to decouple the constraints and for maintaining feasibility of the decoupled constraints. The effectiveness and potential of the two-tier SQP approach for reliably and efficiently solving large-scale flowsheet optimization problems are numerically illustrated with fully thermally coupled distillation problems.

顺序二次编程（SQP）的显着优势是全局收敛，可确保从远程起点进行收敛。但是，当约束是高度非线性的（例如在流程图优化中）时，在SQP中用作方程求解工具的局部收敛的牛顿法可能会使收敛的行为恶化。我们认识到仍有待解决的问题，促使我们研究一种两层SQP方法，其中将由非线性方程组成的过程仿真约束与KKT系统分离，以阻止非线性对全局收敛的不利影响。我们的面向方程式（EO）的过程仿真器（Ishii和Otto，2011年）用于解耦约束，并保持解耦约束的可行性。