污水处理曝气池溶解氧智能优化控制系统  

Dissolved Oxygen Intelligent Optimization Control System in the Aeration Tank of Wastewater Treatment

作　　者：黄银蓉[1] 张绍德[1] 

机构地区：[1]安徽工业大学电气信息学院,安徽马鞍山243002

出　　处：《信息与控制》2011年第3期393-400,共8页Information and Control

摘　　要：提出一种新的溶解氧优化控制方法,根据不同的进水水质,利用在线多输入多输出最小二乘支持向量机软测量模型预测出水参数值.将这些参数作为水质反馈信号,使用模糊神经网络动态优化与进水水质对应的溶解氧设定值.最后利用神经网络逆控制系统跟踪优化的溶解氧设定值,从而实现在达到出水指标的前提下,既能保证出水水质的稳定,又能有效消除曝气量冗余,实现曝气量的动态优化,有效减少电能消耗.A new control method for dissolved oxygen is presented.According to influent water quality,the method uses online MIMO-LSSVM（multiple input multiple output - least squares support vector machine） soft sensor model to predict effluent water quality parameters,and then takes these parameters as a feedback signal of water quality.The method uses fuzzy neural networks to optimize the DO（dissolved oxygen） settings corresponding to the influent water quality dynamically. At last,inverse control based on neural network is used to track the optimized dissolved oxygen settings.Under the premise of achieving the water targets,this method not only can ensure the stability of effluent water quality,but also can eliminate aeration redundancy effectively.In this way,the aeration can be optimized dynamically and power consumption can be reduced significantly.

关 键 词：最小二乘支持向量机 溶解氧 模糊神经网络 逆控制 软测量 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]