Fuzzy waste load allocation model: a multiobjective approach

Ghosh, Subimal; Mujumdar, P. P.

doi:10.2166/hydro.2010.028

Cited by 31 publications

(8 citation statements)

References 27 publications

(28 reference statements)

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“…A variety of mathematical programming techniques have been applied to address the issue of water quality, directly (e.g. where the objective is to improve quality [139] or to minimise the risk of poor quality [65]) or indirectly (e.g. where the solution is constrained by and must adhere to quality targets [17,42,91]).…”

Section: Water Qualitymentioning

confidence: 99%

“…Marinoni et al [140] propose a framework for planning major investment decisions and apply this to the case of a water quality enhancement program in a river catchment in Brisbane, Australia. Compromise programming is first used to score the options [42,87,118,121,126,132,139,150,174,176,208,213,224] Storm water [17] Wastewater [1,33,65,80,86,98,106,113,129,139,141,169,187,197,206,219,238] Water allocation [172,230,233] Water trading [224] Water treatment [33,206] Wetlands [39,207,225] for pollution reduction at various sites and the optimal investment problem is then formulated as a multicriteria knapsack problem. In some cases, legislation needs to be considered alongside other management strategies.…”

Section: Water Qualitymentioning

confidence: 99%

See 1 more Smart Citation

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Archibald

Marshall

2018

Environ Model Assess

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Economic development, variation in weather patterns and natural disasters focus attention on the management of water resources. This paper reviews the literature on the development of mathematical programming models for water resource management under uncertainty between 2010 and 2017. A systematic search of the academic literature identified 448 journal articles on water resource management for examination. Bibliometric analysis is employed to investigate the methods that researchers are currently using to address this problem and to identify recent trends in research in the area. The research reveals that stochastic dynamic programming and multistage stochastic programming are the methods most commonly applied. Water resource allocation, climate change, water quality and agricultural irrigation are amongst the most frequently discussed topics in the literature. A more detailed examination of the literature on each of these topics is included. The findings suggest that there is a need for mathematical programming models of large-scale water systems that deal with uncertainty and multiobjectives in an effective and computationally efficient way.

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Section: Water Qualitymentioning

confidence: 99%

Section: Water Qualitymentioning

confidence: 99%

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Archibald

Marshall

2018

Environ Model Assess

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“…Also, water pollutants discharged by industrial sectors may exceed the predetermined control targets, posing a potential violation risk for sustainable development 49 . To address the uncertainties and variations in the IWRM systems, a number of approaches (e.g., inexact optimization techniques, sensitivity analysis, statistical approaches and fuzzy sets theory) were developed 50 – 53 . Monte Carlo simulation (MCS) was widely used in data uncertainty analysis 54 – 57 .…”

Section: Introductionmentioning

confidence: 99%

Industrial water resources management based on violation risk analysis of the total allowable target on wastewater discharge

Yue

Cai

et al. 2017

Sci Rep

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To improve the capabilities of conventional methodologies in facilitating industrial water allocation under uncertain conditions, an integrated approach was developed through the combination of operational research, uncertainty analysis, and violation risk analysis methods. The developed approach can (a) address complexities of industrial water resources management (IWRM) systems, (b) facilitate reflections of multiple uncertainties and risks of the system and incorporate them into a general optimization framework, and (c) manage robust actions for industrial productions in consideration of water supply capacity and wastewater discharging control. The developed method was then demonstrated in a water-stressed city (i.e., the City of Dalian), northeastern China. Three scenarios were proposed according to the city’s industrial plans. The results indicated that in the planning year of 2020 (a) the production of civilian-used steel ships and machine-made paper & paperboard would reduce significantly, (b) violation risk of chemical oxygen demand (COD) discharge under scenario 1 would be the most prominent, compared with those under scenarios 2 and 3, (c) the maximal total economic benefit under scenario 2 would be higher than the benefit under scenario 3, and (d) the production of rolling contact bearing, rail vehicles, and commercial vehicles would be promoted.

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“…The subsequently developed modified fuzzy waste load allocation model (MFWLAM) is a stochastic model that considers the moments (mean, variance and skewness) of water quality indicators and incorporates uncertainty due to the randomness of input variables along with uncertainty due to imprecision [14]. Additionally, a two-stage fuzzy chance-constrained programming approach was developed for water resource management under dual uncertainties [15].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Optimization Model for Waste Load Allocation in a River with Total Maximum Daily Load (TMDL) Planning

Cho¹,

Lee

2020

Water

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In traditional waste load allocation (WLA) decision making, water quality-related constraints must be satisfied. Fuzzy models, however, can be useful for policy makers to make the most reasonable decisions in an ambiguous environment, considering various surrounding environments. We developed a fuzzy WLA model that optimizes the satisfaction level by using fuzzy membership functions and minimizes the water quality management cost for policy decision makers considering given environmental and socioeconomic conditions. The fuzzy optimization problem was formulated using a max–min operator. The fuzzy WLA model was applied to the Yeongsan River basin, which is located in the southwestern part of the Korean Peninsula and Korean TMDLs were applied. The results of the fuzzy model show that the pollutant load reduction should be increased in the Gwangju 1 and Gwangju 2 wastewater treatment plants (WWTPs) and in subcatchments with high pollutant load. In particular, it is necessary to perform advanced wastewater treatment to decrease the load of 932 kg ultimate biochemical oxygen demand (BODu)/day in the large-capacity Gwangju 1 WWTP and reduce the BODu emission concentration from 4.3 to 2.7 mg/L during the low-flow season. The satisfaction level of the fuzzy model is a relatively high at 0.81.

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Fuzzy waste load allocation model: a multiobjective approach

Cited by 31 publications

References 27 publications

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Industrial water resources management based on violation risk analysis of the total allowable target on wastewater discharge

Fuzzy Optimization Model for Waste Load Allocation in a River with Total Maximum Daily Load (TMDL) Planning

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