基于改进FWA-NN的污水处理过程溶解氧浓度预测

为实现对污水处理过程溶解氧质量浓度的实时准确预测，提出了一种改进的混沌烟花混合优化算法并构建了基于改进算法的神经网络在线软测量模型.结合污水处理过程的数据特征，定义了一项新的样本相似度衡量指标用于提取更具代表性的建模数据.在改进算法中，为提高基本烟花算法初始成员的质量，定义了一种改进的两级正弦混沌映射并利用混沌运动的遍历性精选烟花算法的初始群成员；通过融合混沌算法改进了基本烟花算法的搜索机制，基于设定准则将寻优过程分为两阶段并采用两分群同时进行.测试结果表明改进算法的收敛速度和收敛精度有较大程度提高.将改进的软测量建模方法和样本数据提取方法用于污水处理过程溶解氧质量浓度软测量建模，应用结果表明该模型的均方根误差和平均泛化误差分别为0.0175和0.0118，具有较强的泛化性能.

Prediction of dissolved oxygen concentration in wastewater treatment process based on improved FWA-NN

To realize the quick and accurate measurement of the dissolved oxygen concentration (DO) in wastewater treatment process, a novel chaos-fireworks algorithm (FWA) based hybrid optimization algorithm was proposed and a neural network on-line soft-sensor model was built based on the improved algorithm. According to the property of the data collected from wastewater treatment process, a new measure of similarity degrees between samples was defined to extract more responsive modeling data. In the novel algorithm, a modified two-level sinusoidal chaotic mapping was defined and the initial members of FWA were well selected by utilizing the ergodicity of chaos. As a result, the quality of the initial population in standard FWA was improved. Next, the search mechanism of FWA was modified by introducing chaos optimization algorithm. The optimization procedure was divided into two phases and the population was divided into two subpopulations according to the predefined criterion. Test results confirmed that the improved FWA had higher convergence speed and convergence accuracy. The novel soft-sensor modeling method and the sample data extraction method was used to build a soft sensor model for real-time measuring DO in wastewater treatment process. Application results indicated the root mean square error and the root mean square error of this model were 0.0175 and 0.0118 respectively, it had good generalization ability.