Abstract
The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.
Highlights
The removal of pollutants from industrial process streams, groundwater, and wastewaters has an undoubtedly importance in terms of sustainability and human health protection [1, 2]
Several studies have demonstrated that ANNbased models can outperform the traditional equations for the correlation and prediction of isotherms, kinetics, and, in less extent, breakthrough curves
There are few studies on the multicomponent adsorption modeling with artificial neural networks (ANN), which are mainly related to the removal of heavy metals, dyes, and other few organic pollutants
Summary
The removal of pollutants from industrial process streams, groundwater, and wastewaters has an undoubtedly importance in terms of sustainability and human health protection [1, 2]. ANN are based on human brain structures and capable to represent the nonlinear interactions between a set of input and output variable(s) of a given system without considering a sophisticated theory [50] They have been employed to resolve engineering problems such as fault detection, prediction of materials properties, soil degradation analysis, water treatment modeling, data reconciliation, process modeling, and control [50–54]. The advantages of ANN (e.g., reliable correlation, simplicity, versatility, and prediction capabilities) to handle multivariable problems with nonlinear behavior have justified their application in the analysis and simulation of adsorption processes [50, 52, 55–58] In this direction, this review covers the ANN-based modeling of adsorption processes in dynamic and batch operating schemes. Some important guidelines concerning the parameter estimation problem to be resolved for ANN training, the selection of the input and output variables to be modeled with ANN, the details of its numerical implementation in terms of adsorption data correlation and prediction, and some challenges to be faced and resolved besides perspectives on this topic are covered in this manuscript
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