摘要
针对污水处理过程中能耗大和罚款高等问题,设计了一种基于工况识别的污水处理智能优化控制系统。为保证工况识别的准确性和实时性,利用自适应遗传算法从多种入水参数中选取参考变量,然后基于建立的历史知识库,对入水实时工况进行识别。针对能耗和罚款的多目标优化问题,基于历史知识的引导,通过智能决策的方法从pareto解集中选出最优偏好解,并对知识库进行更新。利用国际基准仿真平台BSM1进行验证,结果表明所提方法有效利用了历史工况的最优解信息,提高了算法的收敛性,降低了计算成本,同时可将能耗和罚款控制在较低的范围。
Aiming at the problems in wastewater treatment process, such as high energy consumption and penalty, a condition recognition based intelligent optimal control system for wastewater treatment is proposed. In order to ensure the accuracy and real-time performance of condition identification, the adaptive genetic algorithm is used to select reference variables from a variety of influent parameters, then based on the established historical knowledge base, identifies the real-time influent condition. Multi-objective optimization for energy consumption and penalty is guided by historical knowledge, and through the method of intelligent decision-making, the optimal preference solution is selected from pareto solution set, then update the knowledge base. The international benchmark simulation platform BSM1 is used to verify the results. The results show that the proposed method effectively utilizes the optimal solution information of historical conditions, improves the convergence of the algorithm, reduces the computational cost, and can control the energy consumption and fines at a lower level.
作者
李永明
史旭东
熊伟丽
LI Yongming;SHI Xudong;XIONG Weili(School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,Jiangsu,China;Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education,Jiangnan University,Wuxi 214122,Jiangsu,China)
出处
《化工学报》
EI
CAS
CSCD
北大核心
2019年第11期4325-4336,共12页
CIESC Journal
基金
国家自然科学基金项目(61773182)
国家重点研发计划项目(2018YFC1603705-03)
江苏高校“青蓝工程”项目
关键词
污水处理
工况识别
过程控制
优化
历史知识
动态仿真
wastewater treatment
condition recognition
process control
optimization
historical knowledge
dynamic simulation
