Review on wastewater treatment ponds clogging under artificial recharge: Impacting factors and future modelling

Abdalrahman, Ghada A. M. and Lai, Sai Hin and Snounu, Ismael and Kumar, Pavitra and Sefelnasr, Ahmed and Sherif, Mohsen and El-shafie, Ahmed (2021) Review on wastewater treatment ponds clogging under artificial recharge: Impacting factors and future modelling. Journal of Water Process Engineering, 40. ISSN 2214-7144, DOI https://doi.org/10.1016/j.jwpe.2020.101848. (In Press)

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Abstract

Artificial recharge (AR) of treated wastewater (TWW) has been widely applied in arid areas as a promising technique to replenish groundwater and control the depletion of aquifers. Consequently, surface clogging and reduction of infiltration rate (IR) in infiltration basin problems have appeared on the surface. Clogging generally arises as a result of various chemical, physical and biological processes through the infiltration of TWW. The primary concern in this study is based on the factors influencing the development of soil clogging, thus contributing to the reduction of IR. These factors were classified into major categories, namely, factors related to soil characteristics, factors related to TWW, operation process and hydraulic loading rates. Furthermore, this study presents a review of the traditional models used in evaluating the IR and a comparison between these traditional models and artificial neural networks using various statistical criteria. The uncertainty remains in the precise impact of the quality of water and soil parameters on the clogging of basins. Thus, arousing a need to establish an integrated ideation for the factors impacting the clogging of infiltration basins, and to develop an artificial neural network model that can simulate all conditions and yield accurate results to the field value.

Item Type: Article
Funders: University of Malaya Research Grant (UMRG), Malaysia [Grant No: RP025A-18SUS]
Uncontrolled Keywords: Treated wastewater; Artificial recharge; Infiltration rate; Soil clogging; Artificial neural network modelling
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 21 Apr 2022 06:58
Last Modified: 21 Apr 2022 06:58
URI: http://eprints.um.edu.my/id/eprint/28826

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