Abstract
Domestic wastewater contains a high concentration of ammonium that causes eutrophication of water-receiving bodies. Numerous studies have been carried out for ammonium removal, such as biological and chemical methods. Among them, removing ammonium by adsorption process has been received great attention. It should be noted that biochar produced from agricultural wastes is considered a potential low-priced adsorptive material to remove ammonium in wastewater. During experiments, several factors could affect the removal efficiency. Therefore the development of new method to predict ammonium removal, and the factors affecting the adsorption process would be important for the optimization of the treatment system. In recent years, artificial neural networks (ANNs) have been progressively applied in wastewater performance prediction. In particular, the adsorption of ammonium has been modeled by using the ANN. This review gives a basic description of the ammonium removal by low-cost biochar. The work also summarizes the state of the art of the ANNs used for the prediction of ammonium removal by biochar adsorption. This work aims to review the potential of ANNs applied for predicting ammonium removal. The perspectives and disadvantages of the ANN are discussed as well.
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