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Beneficial reuse and appropriate disposal of water treatment residuals (WTRs) are of great concern for sustainable drinking water treatment. Using WTRs to remove phosphorus (P) is widely regarded as a feasible approach. However, the information is still limited on air-dried WTRs containing polyaluminium chloride (PAC) and anionic polyacrylamide (APAM) used to adsorb P. The objectives of this study were to construct artificial neural network (ANN) models for P adsorption onto WTRs from distilled de-ionized (DDI) water solution and stormwater, to investigate the performance of ANN in predicting phosphorous adsorption, and to model isotherm adsorption, kinetics, and thermodynamics by using the index of model performance. Batch experiments were performed with different WTRs dosage, pH, initial P concentration, temperature, and time. ANN models accurately predicted the P concentration at equilibrium. Non-linearized Langmuir model fitted the isotherm data best. Pseudo second-order kinetic model provided a better fit to experimental data. The adsorption process may be at least simultaneously controlled by surface adsorption and intraparticle diffusion. The P adsorption is a homogenous monolayer adsorption that is spontaneous, endothermic, and entropy production process. WTRs were found to be favorable and effective in removing P, but the P removals had significant differences in both solutions.

Modeling phosphorus adsorption onto polyaluminium chloride water treatment residuals