Model identification and control of chlorine residual for disinfection of wastewater. (December 2019)

• Record Type: Journal Article
• Authors: Khawaga, Rehab I.; Abdel Jabbar, Nabil; Al-Asheh, Sameer; Abouleish, Mohamed
• Is Part Of: Journal of water process engineering. Volume 32(2019)
• Publication Date: 2019-12
• Subjects: Chlorination -- Breakpoint -- Optimization -- ANN -- Fuzzy; Water-supply engineering -- Periodicals; Saline water conversion -- Periodicals; Seawater -- Distillation -- Periodicals; Sanitary engineering -- Periodicals; Sewage -- Purification -- Periodicals; 627
• DOI: 10.1016/j.jwpe.2019.100936
• Languages: English
• ISSNs: 2214-7144

Abstract

Abstract: In this study, Artificial Neural Network modelling (ANN) was applied to forecast the chlorination behaviour in the secondary wastewater effluent containing ammonia and nitrite. A novel control scheme of chlorination disinfection was then developed through an integration of ANN model 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 a local wastewater treatment plant and was able to improve disinfection quality and reduce chlorine gas consumption by 18%.