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

Authors

  • Frederick Gyaase

    Department of Engineering and Physical Sciences, University of Wyoming

  • Olalekan Samuel Okunlola

    Department of Mechanical and Materials Engineering, University of Cincinnati

  • Adeyinka M Olusanya

    Department of Geography, Oklahoma State University

  • Adeleye Adedokun

    Department of Geography, Oklahoma State University

  • Olufunmilayo Ifeoluwa Somoye

    Department of Data Science. Northeastern University

  • Akpevwe Theophilus Erhieyovwe

    Department of Physics Illinois Institute of Tecchnology

How to Cite

Gyaase, F., Okunlola, O. S., Olusanya, A. M., Adedokun , A. ., Somoye, O. I., & Erhieyovwe , A. T. . (2025). Utilizing machine learning for predictive ‎analysis of emission levels to ensure compliance in refinery operations. International Journal of Engineering and Technology, 14(1), 24-29. https://doi.org/10.14419/6x85gr51

Received date: March 23, 2025

Accepted date: April 14, 2025

Published date: May 17, 2025

DOI:

https://doi.org/10.14419/6x85gr51

Keywords:

Machine Learning; Predictive Emission; Refinery Operation; Regulatory Compliance

Abstract

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.

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How to Cite

Gyaase, F., Okunlola, O. S., Olusanya, A. M., Adedokun , A. ., Somoye, O. I., & Erhieyovwe , A. T. . (2025). Utilizing machine learning for predictive ‎analysis of emission levels to ensure compliance in refinery operations. International Journal of Engineering and Technology, 14(1), 24-29. https://doi.org/10.14419/6x85gr51

Received date: March 23, 2025

Accepted date: April 14, 2025

Published date: May 17, 2025