WASTE LOAD ALLOCATION RELATED LIMITS AND OTHER TMDL IMPLEMENTATION PERMITTING ISSUES -- NEW YORK STYLE

Waste load allocation for rivers has been a topic of growing interest. Dynamic programming based algorithms are particularly attractive in this context and are widely reported in the literature. Codes developed for dynamic programming are however complex, require substantial computer resources and importantly do not allow interactions of the user. Further, there is always resistance to utilizing mathematical programming based algorithms for practical applications. There has been therefore always a gap between theory and practice in systems analysis in water quality management. This paper presents various heuristic algorithms to bridge this gap with supporting comparisons with dynamic programming based algorithms. These heuristics make a good use of the insight gained in the system's behaviour through experience, a process akin to the one adopted by field personnel and therefore can readily be understood by a user familiar with the system. Also they allow user preferences in decision making via on-line interaction. Experience has shown that these heuristics are indeed well founded and compare very favourably with the sophisticated dynamic programming algorithms. Two examples have been included which demonstrate such a success of the heuristic algorithms.

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An Optimization Model for Waste Load Allocation under Water Carrying Capacity Improvement Management, A Case Study of the Yitong River, Northeast China

Water ◽

10.3390/w9080573 ◽

2017 ◽

Vol 9 (8) ◽

pp. 573 ◽

Cited By ~ 7

Author(s):

Chong Meng ◽

Xiaolei Wang ◽

Yu Li

Keyword(s):

Carrying Capacity ◽

Optimization Model ◽

Northeast China ◽

Waste Load Allocation ◽

Waste Load ◽

Capacity Improvement

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

Water ◽

10.3390/w12092618 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2618

Author(s):

Jae Heon Cho ◽

Jong Ho Lee

Keyword(s):

Water Quality ◽

Wastewater Treatment ◽

Water Quality Management ◽

Fuzzy Optimization ◽

Fuzzy Model ◽

Low Flow ◽

Satisfaction Level ◽

Waste Load Allocation ◽

Pollutant Load ◽

Waste Load

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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Waste load allocation under uncertainty using game theory approach and simulation-optimization process

Journal of Hydroinformatics ◽

10.2166/hydro.2020.181 ◽

2020 ◽

Vol 22 (4) ◽

pp. 815-841 ◽

Cited By ~ 1

Author(s):

Behnam Andik ◽

Mohammad Hossein Niksokhan

Keyword(s):

Game Theory ◽

Sensitivity Analysis ◽

Simulation Optimization ◽

Graph Model ◽

Decision Makers ◽

Optimization Process ◽

Theory Approach ◽

Waste Load Allocation ◽

Waste Load ◽

The Status

Abstract This article aims to present a new methodology for waste load allocation (WLA) in a riverine system considering the uncertainty and achieve the lowest amount of inequity index, cost, and fuzzy risk of standard violation. To find a surface of undominated solutions, a new modified PAWN method, initially designed for sensitivity analysis, was developed and coupled with a simulation-optimization process using multi-objective particle swarm optimization (MOPSO) algorithm, to consider the uncertainty of all affecting variables and parameters by using their probability distribution. The proposed methodology applied to Sefidrood River in the northern part of Iran. Graph model for conflict resolution (GMCR) as a subset of game theory was implemented to attain a compromise on WLA among the stakeholders of a river system's quality in Iran: Department of Environment, Municipal Waste Water, and Private Sector. Some undominated solutions were used in GMCR model and modeling the conflict among decision makers reveals that their preferences and the status quo do not lead to a solely stable equilibrium; thus the intervention of a ruler as arbitrator leads them to reach a compromise on a scenario that has a median FRVS and cost. Sensitivity analysis was done using the PAWN method to assess the sensitivity of three intended objectives to all variables and parameters.

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