Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research

SK Bhagat, TM Tung, ZM Yaseen - Journal of Cleaner Production, 2020 - Elsevier
The presence of various forms of heavy metals (HMs)(eg, Cu, Cd, Pb, Zn, Cr, Ni, As, Co, Hg,
Fe, Mn, Sb, and Ce) in water bodies and sediment has been increasing due to industrial and …

Application of neural network in metal adsorption using biomaterials (BMs): a review

A Nighojkar, K Zimmermann, M Ateia… - Environmental …, 2023 - pubs.rsc.org
With growing environmental consciousness, biomaterials (BMs) have garnered attention as
sustainable materials for the adsorption of hazardous water contaminants. These BMs are …

A comprehensive review on application of lignocellulose derived nanomaterial in heavy metals removal from wastewater

A Kumar, V Kumar - Chemistry Africa, 2023 - Springer
In the present scenario, heavy metals in wastewater are among the most severe
environmental problems. Wastewater treatments remain a critical issue to date despite …

Predictive model based on Adaptive Neuro-Fuzzy Inference System for estimation of Cephalexin adsorption on the Octenyl Succinic Anhydride starch

M Bouhedda, S Lefnaoui, S Rebouh… - … and Intelligent Laboratory …, 2019 - Elsevier
The purpose of this study was to investigate the potential of an amphiphilic biopolymer to
remove organic contaminants from aqueous solutions. The removal of Cephalexin antibiotic …

Neural network models for simulating adsorptive eviction of metal contaminants from effluent streams using natural materials (NMs)

A Nighojkar, A Plappally, W Soboyejo - Neural Computing and …, 2023 - Springer
With the rise in environmental-conscious research, natural materials (NMs) have drawn
attention as eco-sustainable solution for removing hazardous pollutants via adsorption …

Modeling of methane and carbon dioxide sorption capacity in tight reservoirs using Machine learning techniques

M Tavakolian, R Najafi-Silab, N Chen, A Kantzas - Fuel, 2024 - Elsevier
The viability of enhanced coalbed methane recovery (ECBM) and enhanced shale gas
recovery (ESGR) are abundantly explored in various studies since they present a solution …

Computational intelligence techniques for modeling of dynamic adsorption of organic pollutants on activated carbon

Y Mesellem, AAE Hadj, M Laidi, S Hanini… - Neural Computing and …, 2021 - Springer
The objective of this work is to compare the efficiency of three computational intelligence
techniques: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and …

Neuro-fuzzy modeling of ibuprofen-sustained release from tablets based on different cellulose derivatives

S Rebouh, S Lefnaoui, M Bouhedda… - Drug delivery and …, 2019 - Springer
In the present study, we investigated the drug release behavior from cellulose derivative
(CD) matrices in the oral form of tablets. We used the adaptive neural-fuzzy inference system …

Artificial neural network for modeling formulation and drug permeation of topical patches containing diclofenac sodium

S Lefnaoui, S Rebouh, M Bouhedda… - Drug delivery and …, 2020 - Springer
In this work, topical matrix patches of diclofenac sodium (DS) were formulated by the solvent
casting method using different ratios of chitosan (CTS) and kappa carrageenan (KC) …

Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges

BM Maurya, N Yadav, T Amudha, J Satheeshkumar… - Chemosphere, 2024 - Elsevier
Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a
threshold limit, cause toxic effects both on the environment and in humans upon entering …