Prediction and Optimization of Process Parameters using Artificial Intelligence and Machine Learning Models

Simon Bbumba *

Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda, Department of Science, Faculty of Science and Computing, Ndejje University, P.O. Box 7088, Kampala, Uganda and Department of Chemistry, Faculty of Science, Muni University, P.O. Box 725, Arua, Uganda.

Moses Kigozi

Department of Chemistry, Busitema University, P. O. Box 236, Tororo, Uganda.

Ibrahim Karume

Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.

Chinaecherem Tochukwu Arum

Department of Material Science and Explosives, Faculty of Science, Nigerian Defence Academy, PMB 2109, Kaduna, Nigeria.

Moses Murungi

Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.

Prudence Mary Babirye

Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.

Solome Kirabo

Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.

*Author to whom correspondence should be addressed.


Abstract

Herein we reviewed Artificial intelligence (AI) and Machine learning (ML) models in the prediction and optimization of process parameters during the removal of toxic heavy metals and textile dyes. Parameters normally optimized include pH, contact time, initial concentration, adsorbent dosage, and temperature.  This review focuses on common AI models such as Artificial Neural Networks (ANN), Particle Swarm Optimization, and Genetic Algorithms (GA). Furthermore, the review describes the common prediction statistical indicators such as coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), absolute average deviation (AAD), etc. Lastly, this review highlights the significant potential of AI and ML in revolutionizing the field of wastewater treatment and mitigating the environmental impact of industrial pollution.

Keywords: Artificial intelligence, wastewater, adsorbents, toxic heavy metals, textile dyes


How to Cite

Bbumba, Simon, Moses Kigozi, Ibrahim Karume, Chinaecherem Tochukwu Arum, Moses Murungi, Prudence Mary Babirye, and Solome Kirabo. 2025. “Prediction and Optimization of Process Parameters Using Artificial Intelligence and Machine Learning Models”. Asian Journal of Applied Chemistry Research 16 (1):11-33. https://doi.org/10.9734/ajacr/2025/v16i1317.