Water quality modelling and optimisation of wastewater treatment network using mixed integer programming

Abstract

Instream water quality management encompasses field monitoring and utilisation of mathematical models. These models can be coupled with optimisation techniques to determine more efficient water quality management alternatives. Among these activities, wastewater treatment plays a crucial role. In this work, a Streeter-Phelps dissolved oxygen model (DO) is implemented in a semi-hypothetical Upper Olifants River system to forecast instream dissolved oxygen profiles in response to different wastewater discharge scenarios. A mixed integer programming (MIP) numerical approach was used in the simulation and determination of the best treatment regimen to meet the instream DO standard at the minimum cost for the chosen river catchment. The Olifants River catchment modelled in this study features 9 wastewater treatment plants. Three treatment levels were evaluated for biochemical oxygen demand (BOD) and the impact was evaluated at specific measuring points (checkpoints) within the river system. Using this model, it was demonstrated that water quality standards can be met at all monitoring points at a minimum cost by simultaneously optimising treatment levels at each treatment plant.

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“…Data sources for river modelling in South Africa include both free and paid options from public and independent sources. The South African Department of Water Affairs (DWA) water quantity and quality monitoring network is a primary data source for water quality models, as reported and utilised in studies by Dabrowski (2014), Mahlathi et al (2016) and . It contains measuring station data for 19 water management areas, as shown in Figure 1.…”

“…In addition to the detailed display of station data, a graph that plots the time series of flow data over the range of the station data is displayed, which allows for quick observations of the time periods with gaps (Figure 3). The spatial and temporal resolution of the data vary for different sections, for example the Olifants River, which has more high-resolution data because of the number of studies (Ashton & Dabrowski 2011;Baker et al 2011;Dabrowski 2014;Mahlathi et al 2016;Udall 2018) and projects conducted in the region.…”

River water quality modelling in South Africa: Considerations, sourcing and accessing of input data

“…Data sources for river modelling in South Africa include both free and paid options from public and independent sources. The South African Department of Water Affairs (DWA) water quantity and quality monitoring network is a primary data source for water quality models, as reported and utilised in studies by Dabrowski (2014), Mahlathi et al (2016) and . It contains measuring station data for 19 water management areas, as shown in Figure 1.…”

A comprehensive review of water quality indices for lotic and lentic ecosystems

“…However, there are increasing concerns about water security and quality, especially in arid and semi-arid regions of the world (Aragaw & Gnanachandrasamy, 2021 ; Busico et al, 2020 ). The concerns emanate from the continuous pollution of water resources from anthropogenic, industrial, and agricultural sources which have become a serious environmental issue, requiring serious strategies for constant monitoring and enforcement of regulatory policies to sustain such ecosystems (Gupta et al, 2009 ; Mahlathi et al, 2016 ; El-Batrawy et al, 2018 ; Nagy-Kovacs et al, 2019 ; Sandhu et al, 2019 ). It is also important to realise that the degrading water quality decreases the portion of available safe and clean water, while the demand and dependence on water by humans and animals remain the same (Young & Beck, 1974 ; Peters & Meybeck, 2000 ; Kanakoudis & Tsitsifli, 2020 ).…”