CAJ | 학술논문

优化技术广泛用于化工生产中"最佳"工艺条件的确定,工程师常需在无先验信息情况下,从若干工艺条件中确定同时能满足多方需求的最佳方案,实现效益最大化.枚举法只能在较简单的情况下使用,随着生产实际复杂程度的增加,枚举法显得无能为力.近来提出的元启发式蚁群优化算法无论计算时间,还是优化质量,都能满足复杂体系的优化.本研究采用Pareto蚁群算法,对间歇自由基聚合反应器进行了多目标优化,结果表明,该算法具有较强的鲁棒性,可用于间歇自由基聚合反应器的设计.
우화기술엄범용우화공생산중"최가"공예조건적학정,공정사상수재무선험신식정황하,종약간공예조건중학정동시능만족다방수구적최가방안,실현효익최대화.매거법지능재교간단적정황하사용,수착생산실제복잡정도적증가,매거법현득무능위력.근래제출적원계발식의군우화산법무론계산시간,환시우화질량,도능만족복잡체계적우화.본연구채용Pareto의군산법,대간헐자유기취합반응기진행료다목표우화,결과표명,해산법구유교강적로봉성,가용우간헐자유기취합반응기적설계.
Optimization technologies are widely used in chemical industry for chemical engineers to select the "best" manipulating profile out of a given set of technological conditions. The engineers must consider multiple objectives and can improve their opportunities by determining and exploring the solution space of all efficient solutions interactively with little a priori preference information available. However, the enumerative method only works on small instances and the underlying complex optimization problems become increasingly demanding as the number of projects grow. The recently developed meta-heuristics, Ant Colony Optimization Algorithms, provide a useful compromise between the computation time and the quality of the approximated solution space. In this study, a Pareto Ant Colony Optimization Algorithm was applied to a real world batch polymerization process of multi-objective optimization. The results indicate that the algorithm is robust and useful for the chemical process optimization.