Modeling of the biosorption of Fe(III) ions by olive‐stone activated carbon

• Published in: Applied organometallic chemistry Vol. 38; no. 4
• Main Authors: Massaoudi, Ayman, Echouchene, Fraj, Ayed, Mossaad Ben, Berguiga, Abdelwahed, Harchay, Ahlem, Belmabrouk, Hafedh
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01-04-2024
• Subjects: Activated carbon; Adsorption; Algorithms; Artificial neural networks; biosorption; Fe(III) ions; Machine learning; Mathematical models; Model accuracy; Multilayer perceptrons; Neural networks; Neurons; olive; Optimization techniques; Parameters; Particle swarm optimization; Radial basis function; Regression coefficients; Stone
• ISSN: 0268-2605; 1099-0739
• DOI: 10.1002/aoc.7384

This study deals with the optimization of Fe(III) ion removal using activated carbon from olive stone waste using advanced machine learning models. The main objective is to evaluate and compare the performance of machine learning models, specifically multilayer perceptron artificial neural network (MLP‐ANN), general regression artificial neural network (GR‐ANN), radial basis function artificial neural network (RBF‐ANN), and particle swarm optimization artificial neural network (PSO‐ANN) in predicting Fe(III) removal efficiency. Experimental data on adsorption parameters were used to train and test the models. Techniques such as tuning hidden layer neurons, optimizing propagation values, and using a Taguchi approach PSO algorithm were applied to improve the models. For the MLP‐ANN model, the optimal configuration contains 13 neurons in the hidden layer. Concerning the parameters involved in the PSO‐ANN model, the coefficient C2 and the particle have the main effect on the reduction of the error. Their contributions are respectively 49% and 19%. The PSO‐ANN model showed superior performance with the highest regression coefficient (0.9997) and remarkable prediction accuracy, surpassing other models such as MLP‐ANN and GR‐ANN. This research suggests that innovative optimization techniques, particularly using PSO algorithms, significantly enhance the predictive capabilities of machine learning models in complex adsorption processes, contributing to more accurate Fe(III) removal models.
The adsorption process depends on several parameters and experimental conditions. The effect of various parameters, namely, the initial concentration, time, stirring speed, temperature, and biosorbent dose on the removal efficiency of Fe(III) ions has been investigated in the present work. The objective of the present study is to develop machine learning models for the input and output parameters for modeling Fe(III) ions adsorption using activated carbon from olive‐stone waste. Several models have been investigated. The PSO‐ANN and GR‐ANN models are respectively the most accurate and the second most adequate ones.