article · Applied Organometallic Chemistry
This research evaluates several machine learning models to optimise the removal of iron, Fe(III) ions, using activated carbon derived from olive stone waste. The models tested include multilayer perceptron, general regression, radial basis function, and particle swarm optimisation artificial neural networks. These models were trained and tested using experimental adsorption data, with tuning applied to hidden layer neurons, propagation values, and optimisation algorithms. Among the approaches, the particle swarm optimisation artificial neural network achieved the highest predictive accuracy, recording a regression coefficient of 0.9997. Within this model, the coefficient C2 and the particle parameter contributed 49 percent and 19 percent, respectively, to reducing error. Overall, incorporating particle swarm optimisation algorithms substantially improved the capability of artificial neural networks to predict outcomes in complex biosorption processes.
Removing metal pollutants like iron from water can be made cheaper and more sustainable by using agricultural by-products such as olive stones. By applying advanced artificial intelligence tools to accurately model and predict this cleanup process, researchers can better understand and fine-tune operational conditions without relying solely on slow and expensive laboratory trial-and-error experiments.
The work supports process design for water treatment facilities or industrial waste processors seeking to use agricultural residues for pollutant removal. The models could be integrated into process control and optimisation software for environmental engineering teams. Based on the abstract, this represents early-stage computational and experimental research, requiring further validation and scale-up testing before operational deployment in commercial effluent treatment systems.
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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.
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DOI: 10.1002/aoc.7384
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