Abstract:

Aiming at the problem that the ion concentration cannot be detected online during the electrochemical wastewater treatment process, a soft-sensor modeling method based on K-means clustering algorithm of state transition is proposed. On the basis of analyzing the mechanism in the electrochemical process, the mechanism model of the electrochemical process was established according to the material balance and adsorption kinetics. Since single model cannot meet the requirements of accuracy, a K-means clustering algorithm based on state transition is proposed to cluster the original data set. This algorithm uses the state transfer algorithm to optimize the initial clustering center of the K-means algorithm,and introduce a matrix to realize clustering and outlier detection simultaneously during iterative process. Then,after clustering, the sub-models are established with the training subsets respectively, and the soft measurement model based on the multi-model switching method is obtained by synthesizing the sub-models.Finally,via field data verification of a wastewater treatment plant, the results show that the soft measurement model of ion concentration in electrochemical wastewater treatment process is reasonable and effective.

Key words: soft measurement, wastewater treatment process, kinetic modeling, state transition algorithm, electrochemical process

摘要：

针对电化学废水处理过程出口离子浓度无法在线检测的问题，提出了一种基于状态转移的K均值聚类算法的软测量建模方法。在分析内部反应机理的基础上，结合物料平衡和吸附动力学定理建立电化学过程的机理模型；由于单一的软测量模型难以满足实际的精度要求，提出一种基于状态转移的K均值聚类算法将原始数据集进行聚类，应用状态转移算法对K均值算法的初始聚类中心进行优化，同时，引入离群值矩阵动态迭代同时实现数据聚类和异常值检测；最后，对聚类后的不同训练子集分别建立子模型，综合各子模型得到基于多模型切换方法的软测量模型。通过某废水处理厂的现场数据进行实例验证，结果证明了所建立的电化学废水处理过程离子浓度软测量模型合理有效。

关键词: 软测量, 废水处理过程, 动力学模型, 状态转移算法, 电化学过程