# Experimental and Machine Learning Modelling of Ni(II) Ion Adsorption onto Guar Gum: Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) Comparative Study

## TL;DR

This paper explores using guar gum to remove nickel ions from water, combining experiments with machine learning models to optimize the process.

## Contribution

A novel integration of experimental adsorption studies and machine learning models (ANN and KNN) for predicting Ni(II) removal efficiency.

## Key findings

- Guar gum adsorbent achieved 97% Ni(II) removal under optimal conditions.
- ANN model outperformed KNN in predicting removal efficiency (R2 = 0.97 vs. 0.95).
- Adsorption was found to be spontaneous, endothermic, and physical in nature.

## Abstract

In this study, a guar gum-based adsorbent was developed and evaluated for the removal of Ni(II) ions from aqueous solutions through a combined experimental and machine learning (ML) approach. The adsorbent was characterized using FTIR, SEM, XRD, TGA, and BET analyses to confirm surface functionality and porous morphology suitable for metal binding. Batch adsorption experiments were conducted to optimize the effects of pH, adsorbent dosage, contact time, temperature, and initial metal concentration. The adsorption efficiency increased with higher pH and adsorbent dosage, achieving a maximum Ni(II) removal of 97% (qₘ = 86.0 mg g−1) under optimal conditions (pH 6.0, dosage 1.0 g L−1, contact time 60 min, and initial concentration 50 mg L−1). The process followed the pseudo-second-order kinetic and Langmuir isotherm models. Thermodynamic results revealed the spontaneous, endothermic, and physical nature of the adsorption process. To complement the experimental findings, artificial neural network (ANN) and k-nearest neighbor (KNN) models were developed to predict Ni(II) removal efficiency based on process parameters. The ANN model yielded a higher prediction accuracy (R2 = 0.97) compared to KNN (R2 = 0.95), validating the strong correlation between experimental and predicted outcomes. The convergence of experimental optimization and ML prediction demonstrates a robust framework for designing eco-friendly, biopolymer-based adsorbents for heavy metal remediation.