An Evolutionary Method For Multi-Objective Tracking Of Process Goose Queue

Process Goose Queue is suggested as a novel and effective approach to the decomposition-coordination optimization of process systems. However, the previous PGQ approach considers the objective tracking of individual PGQs as single-objective optimization issues, which is apparently inconsistent with practical process operations. In addition, traditional mathematical programming methods are employed to deal with multi-level PQGs optimization problems, demanding for accurate process models and being inconsistent to the mechanism of PGQ formation adjustments. In response, an evolutionary optimization method for multi-objective tracking of PGQs is proposed in this paper. The main work of the paper is presented as follows.1. Based on PGQ’s decomposition-coordination metrics, an approach to simplify the structures of multi-level PGQs is introduced to transfer a multi-branch PGQ system into a single-branch one. Thereby, the optimization problems can be facilitated in terms of computations as well as initial value assignments.2. The NSGA-II method is accommodated to solve the multi-objective optimization problem of an individual PGQ so as to obtain the Pareto set. Optimal solutions are subsequently selected from the Pareto set by TOPSIS method before delivering to the neighbor PGQ, where the decision-making weight matrix is attained by EFAST method. The proposed methods enjoy taking account of the influences of multiple objectives on the PGQ system and less demanding for accurate process models, as well as improving the local optimality.3. The typical TE benchmark process is employed as the case study to show the steady state optimization, which demonstrates the potential benefits of the improved PGQ method.