基于同步聚类的污水水质混合在线软测量方法

污水处理过程工况频繁波动，单一模型难以保证软测量精度，提出了基于同步聚类的出水COD混合在线软测量方法。模型由简化机理模型和建模误差补偿模型组成，其中简化机理模型作为主模型，集成模型作为误差补偿模型。机理模型用于表征污水处理过程的基本动态机理特性；误差补偿集成模型中子模型均采用线性模型，用以补偿不同工况下的机理模型建模误差。子模型个数采用在线同步聚类算法进行划分，考虑了输入和输出数据的时间区间，同时考虑了相邻数据间的关联性，提高了计算效率，改善了模型的实时性。采用实际污水处理厂数据进行仿真实验，验证了所提建模方法在多个运行工况下仍具有较好的精度。

On-line soft sensor for water quality of wastewater based on synchronous clustering

Wastewater treatment plant is a complex process affected by many uncertain factors, which leads to frequent fluctuations of operating conditions. An online soft sensor of effluent COD（Chemical Oxygen Demand） is presented to deal with the problem that single model cannot guarantee the accuracy of soft sensor. The soft sensor is composed of simplified mechanistic model and error compensation model, where simplified model based on ASM1（Activated Sludge Model No.1） is used to describe the basic characteristics of dynamic mechanism in wastewater treatment process; error compensation model using integrated linear model is adopted to compensate model errors under different operating conditions. The number of the clusters can be achieved by synchronous clustering algorithm that considers the time interval between input and output data and the relevance of adjacent data. Linear model in error compensation model reduces the computational cost and improves the real-time performance of soft sensor. Simulations show that online hybrid soft sensor of COD has better prediction accuracy under multiple operating conditions.