摘要
根据污水处理系统的出水水质要求和节能目标,提出一种自适应优化调度策略,利用有序样本聚类法,根据入水量的变化情况进行自适应分段,再采用具有全局搜索能力的人工免疫算法确定控制器参数在每段的最优设定值,对污水系统进行动态优化控制.在活性污泥污水生化处理基准模型(benchmark simulation model No.1,BSM1)上进行仿真实验,结果证明了该自适应优化策略在污水处理及节能降耗方面的有效性.
In order to limit the effluent quality and optimize energy, a self-adaptive optimization scheduling strategy that is based on ordered sample clustering method is proposed for wastewater treatment plants. By partitioning the control periods adaptively in accordance with the influent condition, the optimal setting values of the con- trol variables are calculated using the artificial immune algorithm with its global searching ability, thus achie- ving a dynamic optimal control of the wastewater system. Simulation experiments are implemented on the acti- vated sludge biological treatment benchmark model No. 1 （ BSM1 ） , and the results show the effectiveness of the presented self-adaptive optimization strategy in energy-saving field of the wastewater treatment process.
出处
《信息与控制》
CSCD
北大核心
2015年第1期99-103,109,共6页
Information and Control
基金
中央高校基本科研业务费专项重点资助项目(2014ZZ0037)
广东省自然科学基金资助项目(S2011010001153)
广东省科技厅科技计划资助项目(2012A010800027)
广州市珠江科技新星项目(2011J2200084)
关键词
污水系统
有序样本聚类
人工免疫算法
基准模型
节能优化
wastewater system
ordered-sample clustering
artificial immune algorithmbenchmark model
energy optimization
