Predicting particle collection performance of a wet electrostatic precipitator under varied conditions with artificial neural networks

The prediction of particle collection is important in designing and operating an electrostatic precipitator (ESP). In this work, a three-layer artificial neural network (ANN) was developed to predict the wet ESP performance under various conditions. The mean square error (MSE), standard deviation error (SDE), and variance account for (VAF) were used to evaluate the ANN model. The univariate analysis method was used to determine the contribution weight of each parameter. The MSE, SDE, and VAF for the trained ANN model were 0.0027%, 0.0362%, and 97.95%, respectively. The current decrease with operation time accounted for the inaccurate evaluation of wet ESP performance. The operating voltage, corona current, gas temperature, and residence time contributed 61.53%, 36.65%, 1.26%, and 0.56% to particle collection respectively. Furthermore, current and voltage dominated the particle collection in different regions. The research findings provide a valuable research approach to retrofit the design and operation of wet ESPs. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A three-layer artificial neural network was developed to predict the wet ESP performance, with parameters contributing to particle collection weight determined, and current and voltage dominating particle collection in different regions.