Model Development, Application and Optimization of Chlorination Breakpoint in Wastewater Treatment

Abstract

Chlorination in wastewater treatment is regarded as a complicated process due to its ammonia and nitrite content. Chlorine added to such systems reacts with ammonia undergoing episodes of complex reactions, resulting in the chlorination breakpoint behavior. Most of the available chlorination mechanistic models are not easily applied which have restricted their practical utilization in treatment plants. In this study, a new mechanistic model for the chlorination breakpoint in an ammonia-nitrite system was developed with a user-friendly interface designed to be applicable to conditions occurring in wastewater treatment plants. The model was validated against laboratory studies reported in the literature and was also applied to forecast the chlorine residue in a wastewater treatment plant in the region. The model simulated both experimental and field data with high precision. Using the devised model, a full 43 factorial design was carried out to investigate the main effects of ammonia, nitrite, contact time, and their interactions. The outcome of the factorial designs has shown that as the ammonia proportion increases in the system, its effect prevails and diminishes that of nitrite. The carried out studies showed that this phenomenon occurs at ammonia/nitrie (A/N) ratio of 3. Artificial Neural Network modelling (ANN) was also applied to forecast the doses at which maximum and minimum total residual chlorine (TRC) of the breakpoint curve occur based on data generated using the developed model. ANN modelling was then integrated with fuzzy logic control (FLC) to optimize the chlorination process by minimizing its cost and maximizing its efficiency while operating within the plant’s budget. The developed FLC platform was applied to the Jebel Ali wastewater treatment plant and was able to improve the disinfection quality and reduce chlorine gas consumption by 18.18 %.