The use of NARX neural network for modeling of adsorption of zinc ions using activated almond shell as a potential biosorbent.
In this study, nonlinear autoregressive model processes with exogenous input (NARX) are applied for the prediction of percentage adsorption efficiency for the removal of zinc ions from wastewater by activated almond shell. The effect of operational parameters such as pH, dosage, particle size and initial metal ions concentration are studied to optimize the conditions for maximum removal of zinc ions. The model is first developed using a two layer NARX network. A comparison between the model results and experimental data showed that the NARX model is able to predict the removal of zinc ions from wastewater. The outcomes of suggested NARX modeling were then compared to batch experimental studies. The results show that activated almond shell is an efficient sorbent and NARX network, which is easy to implement and is able to model the batch experimental system.
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D000327:Adsorption; D001673:Biodegradation, Environmental; D006863:Hydrogen-Ion Concentration; D007477:Ions; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010316:Particle Size; D027861:Prunus / Q000737:chemistry*; D013696:Temperature; D014866:Waste Products; D015032:Zinc / Q000302:isolation & purification*
D007477:Ions; D014866:Waste Products; D015032:Zinc