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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.

Separation of itaconic acid from aqueous solution has been explored using various carbon-based adsorbents obtained from the pyrolysis and KOH activation of coconut shell biomass. The best preparation conditions to obtain a tailored adsorbent for itaconic acid purification were identified via a Taguchi experimental design, where its adsorption properties were maximized. The best activated carbon was obtained via coconut shell pyrolysis at 750 °C for 4 h plus an activation with 0.1 KOH and a final treatment at 800 °C for 2 h. This adsorbent showed an adsorption capacity of 4.31 mmol/g at 20 °C and pH 3 with a surface area of 466 m2/g. Itaconic acid separation was exothermic and pH-dependent where electrostatic forces and hydrogen bonding were the main adsorption interactions. Calculated adsorption rate constants for itaconic acid adsorption were 0.44–1.20 h-1. Results of adsorbent characterization analysis indicated the presence of a crystallization of itaconic acid molecules onto the activated carbon surface where 3–4 molecules could interact to form the clusters. This organic acid was recovered from the adsorbent surface via desorption with water or ethanol, thus facilitating its final purification. The best activated carbon obtained in this study is a promising alternative to perform sustainable and energy-efficient downstream separation and purification of itaconic acid produced via fermentation.