Machine Learning‑Driven Optimization of Multivariate Chemical Process Parameters in Real‑Time Industrial Control Environments

Prasanna Kumar Yekula, P. Mathiyalagan, Saravana Kumar Krishnan, S. Muthuselvan, D. Aathisesan, Ajmeera Kiran

2025

Abstract

The dynamic and heterogeneous nature of contemporary chemical production processes necessitates that mechanistic models, enabling real-time adaptive intelligent regulation while ensuring operational safety, efficiency, and environmental compliance. Further existing machine learning (ML) methods, while promising in their own right, tend to have various shortcomings that include generalization issues, limited interpretability, challenges in systems integration with current management methods and lack of capability to support constantly changing process conditions. We present an adaptive and interpretable machine learning framework for optimizing multivariate chemical process parameters that map to industrial control applications where real-time feedback is necessary. In light of these, we explore the most advanced data augmentation and regularization techniques, as well as efficient scalability with state-of-the-art noisy or sparse dataset performance, and a system design that seamlessly integrates to existing SCADA/PLC systems. Our proposed framework also enhances explain ability and regulatory compliance through the use of explainable artificial intelligence methods such as SHAP values and LIME. It is designed for low-latency processing, supporting real-time decision-making, and incorporates online learning to adapt dynamically to changing process conditions. A human-in-the-loop mechanism further closes the gap between domain knowledge and automated decision-making based on data, enabling the two entities to learn from the experiences of the other, thereby capturing the feedback loop and ensuring trust, particularly in high-stake environments. This paper overcomes some major limitations found in prior works and lays the foundation for scalable, safe, and intelligent factories of the future.

Paper Citation

in Harvard Style

Yekula P., Mathiyalagan P., Krishnan S., Muthuselvan S., Aathisesan D. and Kiran A. (2025). Machine Learning‑Driven Optimization of Multivariate Chemical Process Parameters in Real‑Time Industrial Control Environments. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 279-287. DOI: 10.5220/0013862700004919

in Bibtex Style

@conference{icrdicct`2525,
author={Prasanna Yekula and P. Mathiyalagan and Saravana Krishnan and S. Muthuselvan and D. Aathisesan and Ajmeera Kiran},
title={Machine Learning‑Driven Optimization of Multivariate Chemical Process Parameters in Real‑Time Industrial Control Environments},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={279-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013862700004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Machine Learning‑Driven Optimization of Multivariate Chemical Process Parameters in Real‑Time Industrial Control Environments
SN - 978-989-758-777-1
AU - Yekula P.
AU - Mathiyalagan P.
AU - Krishnan S.
AU - Muthuselvan S.
AU - Aathisesan D.
AU - Kiran A.
PY - 2025
SP - 279
EP - 287
DO - 10.5220/0013862700004919
PB - SciTePress