Artificial intelligence-assisted modelling of heavy metal adsorption using cellulose-based and bio-waste adsorbents : a focus on ANN and ANFIS architectures
dc.contributor.author | Kumari, Binu | |
dc.contributor.author | Seedat, Naadhira | |
dc.contributor.author | Moothi, Kapil | |
dc.contributor.author | Roopchund, Rishen | |
dc.date.accessioned | 2025-10-02T07:13:02Z | |
dc.date.available | 2025-10-02T07:13:02Z | |
dc.date.issued | 2025-12 | |
dc.description | DATA AVAILABILITY : No data was used for the research described in the article. | |
dc.description.abstract | This review explores the application of artificial intelligence (AI) models, specifically artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS), in predicting heavy metal adsorption performance using bio-based adsorbents. Focus is placed on sustainable materials such as cellulose nanocrystals (CNCs), agricultural waste-derived biochar, and microbial biomass. The review compiles more than 60 studies over the past decade, analysing model structures, input-output variables, training algorithms, and validation strategies. Performance metrics reveal that most ANN models achieve R² > 0.98, with NARX-ANN reaching as high as 0.9998 in time-resolved batch adsorption simulations. ANFIS models offer added interpretability through fuzzy rule extraction, though their adoption remains limited. Optimization techniques such as particle swarm optimization (PSO) and genetic algorithms (GA) improved RMSE by 5–15%.Comparative evaluation shows variability in model generalization depending on input complexity and adsorbent type. Despite promising results, the review identifies gaps in dataset standardization, model validation, and real-world applicability under multicomponent or noisy conditions. The novelty of this review lies in its cross-comparative benchmarking of ANN and ANFIS architectures applied specifically to bio-adsorbents, and its recommendations for engineering-grade AI deployment in environmental remediation systems. Future research should incorporate deep learning, sensor integration, and regulatory-informed optimization to enhance model robustness and scalability in wastewater treatment applications. HIGHLIGHTS • AI techniques such as ANN and ANFIS model heavy metal adsorption with high accuracy. • Bio-based adsorbents (e.g., CNC, biomass) are central to eco-friendly water treatment. • NARX-ANN achieves R² = 0.9998 in modeling batch adsorption kinetics. • ANFIS improves interpretability using fuzzy rule-based logic. • Key gaps include cross-validation, regulatory constraints, and dataset diversity. | |
dc.description.department | Chemical Engineering | |
dc.description.librarian | hj2025 | |
dc.description.sdg | SDG-12: Responsible consumption and production | |
dc.description.uri | https://www.sciencedirect.com/journal/results-in-engineering | |
dc.identifier.citation | Kumari, B., Seedat, N., Moothi, K. et al. 2025, 'Artificial intelligence-assisted modelling of heavy metal adsorption using cellulose-based and bio-waste adsorbents : a focus on ANN and ANFIS architectures', Results in Engineering, vol. 28, art. 107147, pp. 1-21, doi : 10.1016/j.rineng.2025.107147. | |
dc.identifier.issn | 2590-1230 (online) | |
dc.identifier.other | 10.1016/j.rineng.2025.107147 | |
dc.identifier.uri | http://hdl.handle.net/2263/104573 | |
dc.language.iso | en | |
dc.publisher | Elsevier | |
dc.rights | © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | |
dc.subject | Artificial intelligence (AI) | |
dc.subject | Artificial neural network (ANN) | |
dc.subject | Adaptive neuro-fuzzy inference system (ANFIS) | |
dc.subject | Adsorption modelling | |
dc.subject | Bio-based adsorbents | |
dc.subject | Cellulose nanocrystals | |
dc.subject | Heavy metals | |
dc.subject | Neural networks | |
dc.subject | Wastewater treatment | |
dc.title | Artificial intelligence-assisted modelling of heavy metal adsorption using cellulose-based and bio-waste adsorbents : a focus on ANN and ANFIS architectures | |
dc.type | Article |