Predict the composition of flue gases using supervised machine learning algorithm

The prediction of flue gases' composition is of significant importance in various industrial processes, environmental monitoring, and public health concerns. Traditional methods for analyzing flue gas composition are time-consuming, costly, and often require specialized equipment. In contrast, supervised machine learning algorithms offer a promising approach to accurately predict flue gas composition based on input data from various sources. This study explores the application of supervised machine learning algorithms to predict flue gas composition, aiming to provide a more efficient and cost-effective solution. The research involves data collection from diverse sources, feature engineering, and model selection, followed by a rigorous evaluation of the predictive performance. The results demonstrate the potential of the proposed approach in accurately estimating flue gas composition, paving the way for improved flue gas monitoring and regulation.