CAJ | 학술논문

流程雁阵(Process Goose Queue, PGQ)[1]为流程系统的分解协调优化提供了一个新的方法，然而，目前PGQ方法尚存在许多不足，如简单地将个体PGQ的状态跟踪处理为单目标优化问题，这与实际流程操作不符；而多级PGQ系统优化仍采用传统的数学规划方法，对模型要求苛刻且依赖于初值的选取。为此，论文提出了一个面向流程雁阵多目标跟踪的优化方法。首先对多级PGQ系统进行了结构优化，然后将NSGA-II用于多级PGQ系统中个体PGQ的多目标优化求解，得到Pareto解集；在此基础上，将逼近理想解排序法(TOPSIS)和扩展傅里叶幅值灵敏度分析法(EFAST)应用于个体PGQ的多目标决策，并从Pareto解集选取最优解在多级PGQ系统中逐级传递，实现流程系统的分解协调优化。仿真实例验证了方法的可行性和有效性。
류정안진(Process Goose Queue, PGQ)[1]위류정계통적분해협조우화제공료일개신적방법，연이，목전PGQ방법상존재허다불족，여간단지장개체PGQ적상태근종처리위단목표우화문제，저여실제류정조작불부；이다급PGQ계통우화잉채용전통적수학규화방법，대모형요구가각차의뢰우초치적선취。위차，논문제출료일개면향류정안진다목표근종적우화방법。수선대다급PGQ계통진행료결구우화，연후장NSGA-II용우다급PGQ계통중개체PGQ적다목표우화구해，득도Pareto해집；재차기출상，장핍근이상해배서법(TOPSIS)화확전부리협폭치령민도분석법(EFAST)응용우개체PGQ적다목표결책，병종Pareto해집선취최우해재다급PGQ계통중축급전체，실현류정계통적분해협조우화。방진실례험증료방법적가행성화유효성。
Process Goose Queue is a novel approach to the decomposition-coordination optimization of process systems. However, there are still several limitations in the previous PGQ method, such as considering individual PGQ’s optimization as single-objective optimization, which is not consistent with practical process operations; and using traditional mathematical programming methods to deal with multi-level PQGs optimization problems, which demand accurate process models and depend on the selection of initial values. In response, a novel optimization method for multi-objective tracking of PGQs is proposed in this paper. Firstly, the multi-level PGQ’s structure is facilitated. Secondly, NSGA-II methods are applied to solve the individual PGQ’s optimization and Pareto set is obtained. On the basis, TOPSIS and EFAST methods are employed in the individual PGQ’s multi-objective decision-making. And the optimal solution selected from the Pareto set is delivered to the next PGQ. A case study is carried out to demonstrate the feasibility and effectiveness of the proposed approaches.