Fuzzy Optimization Model for Water Quality Management of a River System

Sasikumar, K.; Mujumdar, P. P.

doi:10.1061/(asce)0733-9496(1998)124:2(79)

Cited by 107 publications

(62 citation statements)

References 22 publications

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“…While this is a more complex task than a waste load allocation (USEPA, 1991) it also increases the management flexibility by opening the way for cost-risk-benefit calculation. This aspect requires some investigative work, although there is a substantial volume of work in the field of fuzzy optimisation (Dubois and Prade, 1994, Klir and Yuan, 1995, Sasikumar and Mujumdar, 1997. The mathematical structure of the model is unaffected by the number of premises and propositions since it is based mostly on max and min operations.…”

Section: Figurementioning

confidence: 99%

A possibilistic approach to diverse-stressor aquatic ecological risk estimation

Jooste¹

2001

WSA

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A possibilistic approach to assess the risk of co-occurring stressors in an aquatic ecosystem based on the use of fuzzy sets is illustrated at the hand of a hypothetical case study. There are two aspects of importance: a fuzzy stressor response relationship where the response may have reference to a lower level end-point, and a rule-based inference model relating the occurrence of low-level stressors to a high-level ecological goal such as sustainability. The stressor-response is expressed as a conditional possibility. The possibility and necessity measures of the disjunctive composition of the stressor-response with the possibility distribution of the stressors yield an estimate of the ecological risk. Such a possibilistic approach may well serve as a screening procedure in multiple stressor resource management when only qualitative risk assessments are needed.

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Section: Figurementioning

confidence: 99%

A possibilistic approach to diverse-stressor aquatic ecological risk estimation

Jooste¹

2001

WSA

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“…Sasikumar et al (1998) introduced the application of fuzzy optimisation for water quality management in river systems; more work on a similar topic followed by Mujumdar and Vemula (2004).…”

Section: Introductionmentioning

confidence: 99%

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

Mahlathi¹,

Siyakatshana²,

Chirwa³

2016

WSA

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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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“…Another type of uncertainty prominent in the management of water quality systems is uncertainty due to imprecision or fuzziness associated with describing the goals related to water quality and pollutant abatement. Sasikumar & Mujumdar (1998 and Mujumdar & Sasikumar (2002) have addressed the uncertainty due to imprecision as well as randomness in a multiobjective framework. Fuzzy logic has been used for water quality management to model imprecision by Zhu et al (2009) and Lermontov et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy waste load allocation model: a multiobjective approach

Ghosh

Mujumdar

2009

Journal of Hydroinformatics

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Fuzzy Waste Load Allocation Model (FWLAM), developed in an earlier study, derives the optimal fractional levels, for the base flow conditions, considering the goals of the Pollution Control Agency (PCA) and dischargers. The Modified Fuzzy Waste Load Allocation Model (MFWLAM) developed subsequently is a stochastic model and considers the moments (mean, variance and skewness) of water quality indicators, incorporating uncertainty due to randomness of input variables along with uncertainty due to imprecision. The risk of low water quality is reduced significantly by using this modified model, but inclusion of new constraints leads to a low value of acceptability level, l, interpreted as the maximized minimum satisfaction in the system. The methodology is applied to a case study of the Tunga-Bhadra river system in south India.The model results in a compromised solution of a higher value of acceptability level as compared to MFWLAM, with a satisfactory value of risk. Thus the goal of risk minimization is achieved with a comparatively better value of acceptability level.

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Fuzzy Optimization Model for Water Quality Management of a River System

Cited by 107 publications

References 22 publications

A possibilistic approach to diverse-stressor aquatic ecological risk estimation

A possibilistic approach to diverse-stressor aquatic ecological risk estimation

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

Fuzzy waste load allocation model: a multiobjective approach

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