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.