Utilizing machine learning for predictive analysis of emission levels to ensure compliance in refinery operations

This study explores the use of Machine learning (ML) to predict emission levels in refinery operations to support regulatory compliance. Refineries produce a large amount of pollution like CO₂, SOₓ, NOₓ, VOCs, and PM that cause environmental degradation and public ‎health concerns. Manual sampling and inspections are siusingmply not real-time and hence at risk for noncompliance. With the advent of ML-based predictive analytics, we can analyze large datasets, predict emission levels, and come up with preventive measures. ‎ When the ML models were applied to the emission prediction, they were found to have certain limitations like the quality of data, computational burden, model interpretability, and data privacy. These models include Linear Regression, Decision Trees, Support Vector Machines ‎‎(SVMs), Long Short-Term Memory (LSTM) networks, and ensembles of Random Forest and XGBoost. It recommends the integration of ‎ML with traditional monitoring, improvement of data quality, a guarantee of data privacy, and fostering of interdisciplinary collaboration. ‎ML application can be optimized with these strategies, practices can be driven towards sustainability, and compliance can be strengthened in ‎refinery operations‎.