System for diagnosis and optimization of combustion in pulverized coal boilers based on artificial intelligence methods

Abstract

Efficient and reliable monitoring of processes in coal-fired boilers requires advanced combustion diagnostic and optimization methods. This paper introduces an integrated system based on machine learning and deep learning technologies to diagnose and optimize combustion regimes in pulverized coal-fired boilers, aiming to improve efficiency and safety. An artificial neural network accurately simulated thermogravimetric mass loss curves, achieving an average coefficient of determination of 99%. Deep learning methods were employed to detect combustion regimes by monitoring the coal flame and identifying anomalies in flame images. For datasets lacking precise measurements, an unsupervised autoencoder was developed. It achieved an average precision of 77% and a recall of 66%. For datasets with measurements, a supervised convolutional neural network provided a higher average recall of 89%. Various machine learning algorithms were employed to predict deviations from stable combustion modes, and a long short-term memory network with an attention mechanism performed best. It had a mean absolute percentage error of up to 8% and an average coefficient of determination of 91%.