Landfill leachate, especially when produced in old municipal solid waste landfills, is a challenging type of wastewater which contains a variety of toxic substances. The existence of pollutants in the landfill leachate is primarily due to inadequate solid waste separation at the source. Pretreatment on site is a must for the landfill leachate to be safely released into the environment. One-step treatment is insufficient since landfill leachate has a complex composition that spatially and temporally varies. Often, the landfill leachate and municipal wastewater are treated together. Biological treatment is a routine technique which is applied to landfill leachate less than five years old. The concentration of easily biodegradable organic matter in the young landfill leachate declines with time and, as a result, the application of physical and chemical treatment processes is required. The goal of the current work is to investigate the usefulness and capability of the most efficient and widely available technique/s for landfill leachate treatment, to identify the main challenges and strengths of each technology and seek the optimum solution.

In this work, the photochemical treatment of a real municipal wastewater using a persulfate-driven photo-Fenton-like process was studied. The wastewater treatment efficiency was evaluated in terms of total carbon (TC), total organic carbon (TOC) and total nitrogen (TN) removal. Response surface methodology (RSM) in conjunction Box-Behnken design (BBD) and multilayer artificial neural network (ANN) have been utilized for the optimization of the treatment process. The effects of four independent factors such as reaction time, pH, K₂S₂O₈ concentration and K₂S₂O₈/Fe²⁺ molar ratio on the TC, TOC and TN removal have been investigated. The process significant factors have been determined implementing Analysis of Variance (ANOVA). Both RSM and ANN accurately found the optimum conditions for the maximum removal of TOC (100% and 98.7%, theoretically), which resulted in complete mineralization of TOC at the reaction time of 106.06 min, pH of 7.7, persulfate concentration of 30 mM and K₂S₂O₈/Fe²⁺ molar ratio of 7.5 for RSM and at the reaction time of 104.93 min, pH of 7.7, persulfate concentration of 30 mM and K₂S₂O₈/Fe²⁺ molar ratio of 9.57 for ANN. On the contrary, the attempts to find the optimal conditions for the maximum TC and TN removal using statistical, and neural network models were not successful.