Online Prediction of Impurity Concentration on System Water in Power Plants

Abstract

On a thermal power plant, the water quality of system water needs to be properly monitored and controlled. The impurities dissolved in the system water are considered as the major factors on plant troubles. In the present circumstances, though, the measurement of impurity concentration relies on manual analyses, with a large time interval. Thus, online methods for monitoring impurities in system water are of great necessity for practical applications, making it possible to prevent troubles in advance. Here we propose a monitoring method of online prediction for the concentration of iron oxides, one of the main impurities dissolved in system water of a thermal power plant. The proposed method was based on a virtual sensing technique named soft sensor, and higher prediction accuracy was achieved by applying variable selection through genetic algorithm-based process variables and dynamics selection (GAVDS). Three case studies were carried out to examine the performance of the proposed method.