污水处理过程中溶解氧的模糊神经网络控制

Fuzzy neural network controls of dissolved oxygen in the wastewater treatment processes

针对污水处理过程中水质变化剧烈,要求溶解氧的质量浓度不一等问题,提出了一种自适应模糊神经网络控制方法,对变参数活性污泥法污水处理系统的溶解氧的质量浓度进行控制,并通过调整量化因子减小系统的静态误差。仿真结果表明该控制方法能够在线调整隶属函数,优化控制规则,将其应用于活性污泥法污水处理系统中可以快速、准确地使溶解氧达到期望要求,并具有较强的鲁棒性。

The water quality changes violently in the sewage environment, and dissolved oxygen (DO) required is varied. To solve these problems, the paper proposes an adaptive fuzzy neural network controller to realize the control of DO in activated sludge model, and to adjust measured factor so as to reduce static error. The results of simulation show that the above controller can adjust subjection function on-line, optimize control rules, come to expectation with high veracity quickly and have great robustness when applying to activated sludge system.