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

Water ◽  
2017 ◽  
Vol 9 (8) ◽  
pp. 573 ◽  
Author(s):  
Chong Meng ◽  
Xiaolei Wang ◽  
Yu Li
Keyword(s):  
Carrying Capacity ◽  
Optimization Model ◽  
Northeast China ◽  
Waste Load Allocation ◽  
Waste Load ◽  
Capacity Improvement

Optimal water and waste load allocation in reservoir–river systems: a case study

2013 ◽  
Vol 71 (9) ◽  
pp. 4127-4142 ◽  
Author(s):  
Mohammad Reza Nikoo ◽  
Reza Kerachian ◽  
Akbar Karimi ◽  
Ali Asghar Azadnia ◽  
Keighobad Jafarzadegan
Keyword(s):  
Waste Load Allocation ◽  
River Systems ◽  
Waste Load

scholarly journals Fuzzy waste load allocation model: a multiobjective approach

2009 ◽  
Vol 12 (1) ◽  
pp. 83-96 ◽  
Author(s):  
Subimal Ghosh ◽  
P. P. Mujumdar
Keyword(s):  
Water Quality ◽  
Goal Programming ◽  
Optimization Model ◽  
River System ◽  
Base Flow ◽  
Risk Minimization ◽  
Waste Load Allocation ◽  
Allocation Model ◽  
Waste Load ◽  
Input Variables

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, λ, interpreted as the maximized minimum satisfaction in the system. To improve this value, a new model, which is a combination of FWLAM and MFWLAM, is presented, allowing for some violations in the constraints of MFWLAM. This combined model is a multiobjective optimization model having the objectives, maximization of acceptability level and minimization of violation of constraints. Fuzzy multiobjective programming, goal programming and fuzzy goal programming are used to find the solutions. For the optimization model, Probabilistic Global Search Lausanne (PGSL) is used as a nonlinear optimization tool. 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.

Optimal waste load allocation using graph model for conflict resolution

2017 ◽  
Vol 75 (6) ◽  
pp. 1512-1522 ◽  
Author(s):  
Leila Saberi ◽  
Mohammad Hossein Niksokhan
Keyword(s):  
Conflict Resolution ◽  
Oxygen Demand ◽  
Graph Model ◽  
Waste Load Allocation ◽  
Swarm Optimization ◽  
Penalty Costs ◽  
Waste Load ◽  
Total Treatment ◽  
Stability Definitions

In this paper, a new methodology is proposed for waste load allocation in river systems using the decision support system (DSS) for the graph model for conflict resolution II (GMCRII), multi-criteria decision making (MCDM) analysis and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Minimization of total treatment and penalty costs and minimization of biological oxygen demand violation of standards at the check point are considered as the main objectives of this study. At first, the water quality along the river was simulated using the Streeter-Phelps (S-P) equation coupled with the MOPSO model. Thereby a trade-off curve between the objectives is obtained and a set of non-dominated solutions is selected. In the next step, the best alternative is chosen using MCDM techniques and the GMCRII DSS package and non-cooperative stability definitions. The applicability and efficiency of the methodology are examined in a real-world case study of the Sefidrud River in the northern part of Iran.

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