Mahlathi, ChristopherSiyakatshana, NjabuloChirwa, E2017-05-162017-05-162016-10Mahlathi, C., Siyakatshana, N. and Chirwa, E. 2016. Water quality modelling and optimisation of wastewater treatment network using mixed integer programming. Water SA, vol. 42(4): 650-6580378-4738http://dx.doi.org/10.4314/wsa.v42i4.16http://hdl.handle.net/10204/9037http://www.ajol.info/index.php/wsa/article/view/147003Copyright: WRC 2016. Published under a Creative Commons Attribution LicenceInstream 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.enCC0 1.0 UniversalInstream water qualityMixed integer optimisationWastewater treatment levelsStreeter-Phelps DO modelUpper Olifants RiverArticleMahlathi, C., Siyakatshana, N., & Chirwa, E. (2016). Water quality modelling and optimisation of wastewater treatment network using mixed integer programming. http://hdl.handle.net/10204/9037Mahlathi, Christopher, Njabulo Siyakatshana, and E Chirwa "Water quality modelling and optimisation of wastewater treatment network using mixed integer programming." (2016) http://hdl.handle.net/10204/9037Mahlathi C, Siyakatshana N, Chirwa E. Water quality modelling and optimisation of wastewater treatment network using mixed integer programming. 2016; http://hdl.handle.net/10204/9037.TY - Article AU - Mahlathi, Christopher AU - Siyakatshana, Njabulo AU - Chirwa, E AB - 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. DA - 2016-10 DB - ResearchSpace DO - 10.4314/wsa.v42i4.16 DP - CSIR KW - Instream water quality KW - Mixed integer optimisation KW - Wastewater treatment levels KW - Streeter-Phelps DO model KW - Upper Olifants River LK - https://researchspace.csir.co.za PY - 2016 SM - 0378-4738 T1 - Water quality modelling and optimisation of wastewater treatment network using mixed integer programming TI - Water quality modelling and optimisation of wastewater treatment network using mixed integer programming UR - http://hdl.handle.net/10204/9037 ER -