Gas Turbine – CO & NOx Emission Data Analysis With Predictive Modelling Using ML/AI Approaches

Due to growing environmental concerns in relation to global warming and pollutants, it’s become very critical to study about sources which are contributing towards it. Once such source is emissions from the industrial equipment’s like Gas Turbine (GT). GTs are used all over the world across various industries for power generation or mechanical drive operations. Some of these industries belongs to Energy, Utilities, Refineries, Chemical & Fertilizer sectors. In this study, efforts were be made to compare all commonly used ML regression models such as MLR, DTR, RFT, Adaboost Regressor, GBR, XGBoost Regressor using same dataset for emission prediction (CO & NOx). Model’s evaluation metrices were analyzed to predict the best model along with number of significant features. Evaluation metric included MAE, RAE, MAPE & RMSE. In addition, various features were analyzed for patterns using univariate & multivariate analysis graphical tools. This study can contribute towards new PEMS (Prediction Emission Monitoring System) designing using ML Regression Models which will help industrial companies in boosting their operation efficiencies, minimization of emissions (like CO & NOx) and making financial gains due to its cost effectiveness over traditional CEMS (Continuous Emission Monitoring System). Overall, these new PEMS will keep monitoring and optimizing industrial equipment’s operations for emissions control during their lifecycles.