Ensemble Prediction Model for Dust Collection Efficiency of Wet Electrostatic Precipitator

Abstract

WESPs (Wet Electrostatic precipitators) are mainly installed in industries and factories where PM (particulate matter) is primarily generated. Such a wet type WESPs exhibits very excellent performance by showing a PM collection efficiency of 97 to 99%, but the PM collection efficiency may decrease rapidly due to a situation in which the dust collector and the discharge electrode is corroded by water. Thus, developing technology to predict efficient PM collection in the design and operation of WESPs is critical. Previous studies have mainly developed machine learning-based models to predict atmospheric PM concentrations using data measured by meteorological agencies. However, the analysis of models for predicting the dust collection efficiency of WESPs installed in factories and industrial facilities is insufficient. In this study, a WESPs was installed, and PM collection experiments were conducted. Nonlinear data such as operating conditions and PM measurements were collected, and ensemble PM collection efficiency prediction models were developed. According to the research results, the random forest model yielded excellent performance, with the best results achieved when the target was PM 7: R2, MAE, and MSE scores of 0.956, 0.747, and 1.748, respectively.