Prediction and Optimization of Process Parameters using Artificial Intelligence and Machine Learning Models
Simon Bbumba, Moses Kigozi, Ibrahim Karume, Chinaecherem Tochukwu Arum, Moses Murungi, Prudence Mary Babirye, Solome Kirabo
Asian Journal of Applied Chemistry Research · pp. 11–33 · Published 6 Jan 2025
10.9734/ajacr/2025/v16i1317Abstract
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.
Cited by 19
P. Saravanamuthukumar, Ahmad Baharuddin Abdullah, Zarirah Karrim Wani · Progress in Additive Manufacturing · 2025
Tarek Ahasan, E. M. N. Thiloka Edirisooriya, Punhasa S. Senanayake · Molecules · 2025
Collins Letibo Yikii, Simon Bbumba, Emmanuel Tebandeke · Discover Catalysis · 2025
Showing 13 of 19 known citations — external sources report more than can currently be individually listed.
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