Abstract
The aim of this work was to model multi-system dynamic adsorption using an artificial intelligence technique. A set of data points, collected from scientific papers containing the dynamic adsorption kinetics on activated carbon, was used to build the artificial neural network (ANN). The studied parameters were molar mass, initial concentration, flow rate, bed height, particle diameter, BET surface area, average pore diameter, time, and concentration of dimensionless effluents. Results showed that the optimized ANN was obtained with a high correlation coefficient, <i>R</i> = 0.997, a root mean square error of RMSE = 0.029, and a mean absolute deviation of AAD (%) = 1.810 during the generalisation phase. Furthermore, a sensitivity analysis was also conducted using the inverse artificial neural network method to study the effect of all the inputs on the dynamic adsorption. Also in this work, the traceability of the estimated results was conducted by developing a graphical user interface.
Highlights
Industrialization is vital to sustainable development, and water resource management is essential in protecting the environment
The Artificial Neural Network (ANN) model was developed with back-propagation learning algorithms to predict the dynamic adsorption of a complex system
The results showed that the artificial neural network (ANN) with one hidden layer was the best
Summary
Industrialization is vital to sustainable development, and water resource management is essential in protecting the environment. Several studies and research have been conducted on the removal of these environmental organic pollutants using various physical and chemical methods These methods include chemical precipitation, oxidation, ion exchange, coagulation/flocculation, solvent extraction, membrane separation, and adsorption.[6,7] Dynamic and/ or fixed bed adsorption is one of the most widely used processes in the environmental applications of chemical industries, especially for the separation and purification of effluents, due to its high efficiency, low cost, and easy operation.[8,9] The dynamic conduct of a fixed-bed column is described according to the breakthrough curve resulting from the adsorbent–adsorbate system analysis.[8,10]
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