Citation

D’Mello, Elton Elvis and Devaraj, Nisha Kumari (2025) Predictive Modelling of Arsenate Equilibrium Adsorption Parameters. In: 2025 Multimedia University Engineering Conference (MECON), 21-23 July 2025, Cyberjaya, Malaysia.

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Abstract

Arsenic contamination in water poses severe health risks, affecting millions globally. Adsorption using magnetic nanoparticles (e.g., Fe3O4, surface-modified variants) is an effective remediation technique, but the experimental optimization is resource-intensive. This study develops a predictive model combining linear and non-linear adsorption isotherms (Freundlich, Langmuir) as well as kinetic models (Pseudo-First, Second Order) with Artificial Neural Networks (ANN) to forecast arsenate (As(V)) adsorption efficiency. Experimental data under varying conditions (particle size and synthesis temperature) are analyzed, with model accuracy evaluated via RMSE tests. The ANN-enhanced framework aims to optimize predictions, reducing reliance on costly experiments while improving water treatment scalability and precision. This approach bridges theoretical modeling with practical applications for cleaner water systems.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Arsenic, Adsorption, Predictive Model, Machine Learning, Isotherm and Kinetic Studies
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	17 Mar 2026 08:08
Last Modified:	19 Mar 2026 00:07
URII:	http://shdl.mmu.edu.my/id/eprint/15510

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