Artificial Intelligence For Air Quality Control Systems: A Holistic Approach

Recent environmental regulations introduced by the United States environmental protection agency such as the Mercury Air Toxics Standards and Hazardous Air Pollution Standards have challenged environmental particulate control equipment especially the electro-static precipitators to operate beyond their design specifications. The impact is exacerbated in power plants burning a wide range of low and high-ranking fossil fuels relying on co-benefits from upstream processes such as the selective catalytic reactor and boilers. To alleviate and mitigate the challenge, this manuscript presents the utilization of modern and novel algorithms in machine learning and artificial intelligence for improving the efficiency and performance of electrostatic precipitators reflecting a holistic approach by considering upstream processes as model parameters. In addition, the paper discusses input relevance algorithms for neural networks and random forests such as partial derivatives, input perturbation and GINI importance comparing their performance and applicability for our case study. Our approach comprises of applying random forests and neural network algorithms to an electrostatic precipitator extending the model to include upstream process parameters such as the selective catalytic reactor and the air heaters. To study variable importance differences and model generalization performance between our employed algorithms, we developed a statistical approach to compare features data distributions impact on input relevance.