Skip to content
Research Article Open access CC BY 4.0

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/v16i1317

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.

Artificial intelligence wastewater adsorbents toxic heavy metals textile dyes

Cited by 19

Carbon nanotubes and graphene as counter electrodes in dye-sensitized solar cells

Simon Bbumba, Moses Kigozi, Ibrahim Karume · Discover Nano · 2025

Enhancing output in open-pit coal mining: The influence of front loading geometry on machinery functionality

Chairul Salam M, Muhammad Rinjani, Orhan Kural · Concurrent Engineering · 2025

АНАЛІТИЧНА МОДЕЛЬ КІНЕТИКИ ЕКСТРАКЦІЇ ЗРІДЖЕНИМИ ГАЗАМИ

Володимир Олексійович Потапов, Миколай Михайлович Цуркан, Дмитро Володимирович Білий · Scientific Works · 2025

Smart recovery of rare earth elements from acid mine drainage: Current methods, machine learning integration, and bibliometric analysis

Tumelo M. Mogashane, Moshalagae A. Motlatle, Lebohang Mokoena · Separation and Purification Technology · 2026

None-emission carbon nanomaterial derived from polystyrene plastic waste for the adsorption of carbon dioxide

Kigozi Moses, Ibrahim Karume, Simon Bbumba · Results in Materials · 2025

Showing 13 of 19 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

19

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.