Applying Metamodels and Sequential Sampling for Constrained Optimization of Process Operations

摘要

This paper presents a framework for nonlinear constrained optimization of complex systems, in which the objective function and the constraints are represented by black box functions. The proposed approach replaces the complex nonlinear model based on first principles with Kriging metamodels. Coupled to Kriging, the “Constrained Expected Improvement” technique and a sequential sampling strategy are used to explore the metamodels, in order to find global solutions for the constrained nonlinear optimization problem. The methodology has been tested and compared with classical optimization procedures based on sequential quadratic programming. Both have been applied to three mathematical examples, and to a case study of chemical process operation optimization. The proposed framework shows accurate solutions and significant reduction in the computational time.