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
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.