Prediction of Carbon Content in Fly Ash in Ultra Supercritical Generation Unit with Long Short-term Memory Neural Network

Abstract

The accurate measurement of carbon content in fly ash is the basis of improving the thermal conversion efficiency of boiler and reducing the coal consumption of power generation. Based on the operation data of 1000MW Ultra-supercritical Unit in a thermal power plant, the input parameters of the model were selected by correlation analysis. The Long Short-Term Memory neural network (LSTM) was used to establish a prediction model with the boiler operation parameters as the input and the carbon content of the boiler fly ash as the output. According to the actual operation data of the unit, the prediction performance of the model was trained and tested. The simulation results show that the average prediction error of the LSTM model is 2.8136%, and 86% of the total samples are data with error less than 4.5%, which means it has high accuracy and strong generalization ability.