摘要
采用多元线性回归理论,结合最小二乘法对气体分馏装置进行优化建模,提出将惩罚函数法与非支配排序遗传算法(NSGA-II)相结合的优化策略,对气体分馏装置优化模型进行求解得到Pareto最优解集。优化结果表明,文中提出的改进NSGA-II算法求得Pareto解集的收敛性和多目标优化点的分散程度要优于NSGA-II和NSGA算法,该算法克服了NSGA-II算法Pareto解集的分散程度不均匀、NSGA算法收敛性差的问题。通过对比气体分馏装置目前工况与改进NSGA-II优化算法的结果可知,改进算法的结果成功地解决了目前该气体分馏装置能耗过高的问题,使该装置达到了节能优化的目标,为气体分馏装置的节能与优化设计提供了新的有效方法。
This study provide insights into the multi-objective optimization problem of gas fractionation unit. The production data are used to fit a multiple linear regression model to identify the gas fractionation unit model parameters and to establish its multi-objective optimization model, using the energy consumption product output and recovery as the objective function. The non-dominant sorting genetic algorithm （NSGA-II） and penalty function algorithm are used to solve the multi-objective optimization model and to obtain the Pareto optimal solution sets. The optimization results show that the proposed method can increase the production and decrease energy consumption and emission of propylene by optimizing the operational conditions,and the improved NSGA-II algorithm has better feasibility validity and versatility than NSGA-II and NSGA algorithm. This optimal method can guide the operational of the gas fractionation unit.
出处
《江南大学学报（自然科学版）》
CAS
2013年第6期658-665,共8页
Joural of Jiangnan University (Natural Science Edition)
基金
国家自然科学基金项目(61203021)
辽宁省科技攻关项目(2011216011)
关键词
气体分馏装置
多目标优化
惩罚函数
gas fractionation unit, multi-objective optimization,SUMT
