Design of waste stabilization pond systems: A review. (15th October 2017)

• Record Type: Journal Article
• Authors: Ho, Long T.; Van Echelpoel, Wout; Goethals, Peter L.M.
• Is Part Of: Water research. Volume 123(2017)
• Page Start: 236
• Page End: 248
• Publication Date: 2017-10-15
• Subjects: Waste stabilization ponds -- Rules of thumb -- Regression equations -- First-order models -- Mechanistic models -- Uncertainty analysis; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2017.06.071
• Languages: English
• ISSNs: 0043-1354

Abstract

Abstract: A better design instruction for waste stabilization ponds is needed due to their growing application for wastewater purification, increasingly strict environmental regulations, and the fact that most of previous design manuals are outdated. To critically review model-based designs of typical pond treatment systems, this paper analyzed more than 150 articles, books, and reports from 1956 to 2016. The models developed in these publications ranged from simple rules and equations to more complex first-order and mechanistic models. From a case study on all four approaches, it appeared that rules of thumb is no longer a proper tool for pond designs due to its low design specification and very high output variability and uncertainty. On the other hand, at the beginning phase of design process or in case of low pressure over land and moderate water quality required, regression equations can be useful to form an idea for pond dimensions. More importantly, mechanistic models proved their capacity of generating more precise and comprehensive designs but still need to overcome their lack of calibration and validation, and overparameterization. In another case study, an essential but often overlooked role of uncertainty analysis in pond designs was investigated via a comparison between deterministic and uncertainty-based approaches. Unlike applying a safety factor representing all uncertainty sources, probabilistic designs quantify the uncertainty of model outputs by including prior uncertainty of inputs and parameters, which generates more scientifically reliable outcomes for decision makers. Based on these findings, we advise engineers and designers to shift from the conventional approaches to more innovative and economic tools which are suitable for dealing with large variations of natural biological systems. Graphical abstract: Image 1 Highlights: More than 150 publications related to pond designs from 1956 to 2016 were analyzed. It is difficult for data-driven models to be extrapolated to future designs. It is advised to apply process-driven models for more precise pond designs. Deterministic designs frequently produced over-sized systems. For better cost effective designs, we advised to apply probabilistic approaches.