Review on wastewater treatment ponds clogging under artificial recharge: Impacting factors and future modelling
•Introduction of past and current assessment of artificial recharge.•Evaluation of past models and equations estimating Infiltration rate.•Assessment of factors affecting the clogging of recharge of treated wastewater.•Suggestion of hybridization with different meta-heuristics search algorithms. Art...
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Veröffentlicht in: | Journal of water process engineering 2021-04, Vol.40, p.101848, Article 101848 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •Introduction of past and current assessment of artificial recharge.•Evaluation of past models and equations estimating Infiltration rate.•Assessment of factors affecting the clogging of recharge of treated wastewater.•Suggestion of hybridization with different meta-heuristics search algorithms.
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. |
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ISSN: | 2214-7144 2214-7144 |
DOI: | 10.1016/j.jwpe.2020.101848 |