Optimal Waste Load Allocation Research Articles

Optimal waste load allocation in river systems based on a new multi-objective cuckoo optimization algorithm.

Water pollution escalates with rising waste discharge in river systems, as the rivers' limited pollution tolerance and constrained self-cleaning capacity compel the release of treated pollutants. Although several studies have shown that the non-dominated sorting genetic algorithm-II (NSGA-II) is an effective algorithm regarding the management of river water quality to reach water quality standards, to our knowledge, the literature lacks using a new optimization model, namely, the multi-objective cuckoo optimization algorithm (MOCOA). Therefore, this research introduces a new optimization framework, including non-dominated sorting and ranking selection using the comparison operator densely populated towards the best Pareto front and a trade-off estimation between the goals of discharges and environmental protection authorities. The suggested algorithm is implemented for a waste load allocation issue in Jajrood River, located in the North of Iran. The limitation of this research is that discharges are point sources. To analyze the performance of the new optimization algorithm, the simulation model is linked with a hybrid optimization model using a cuckoo optimization algorithm and non-dominated sorting genetic algorithms to convert a single-objective algorithm to a multi-objective algorithm. The findings indicate that, in terms of violation index and inequity values, MOCOA's Pareto front is superior to NSGA-II, which highlights the MOCOA's effectiveness in waste load allocation. For instance, with identical population sizes and violation indexes for both algorithms, the optimal Pareto front ranges from 1.31 to 2.36 for NSGA-II and 0.379 to 2.28 for MOCOA. This suggests that MOCOA achieves a superior Pareto front in a more efficient timeframe. Additionally, MOCOA can attain optimal equity in the smaller population size.

Evaluating robustness of waste load allocation under climate change using multi-objective decision making

In recent years, uncertainties in climate change have made the allocation of waste load a severe challenge. This research develops a new model framework integrating a soil and water assessment tool (SWAT) for hydrological simulation of the river basin, Borg many-objective evolutionary algorithm (Borg MOEA) for optimization, and a software for many-objective robust decision making (OpenMORDM) to minimize these uncertainties and identifying the most robust waste load allocation plans resisting the impact of system uncertainties. This framework has been applied to the Golgol River basin, Iran, which is the main source of the Ilam dam water supply. In this study, first, temperature and precipitation were predicted in 2080–2099. Then, The SWAT model was used to simulate runoff and biochemical oxygen demand (BOD). Afterward, by specifying the objective functions, the Borg MOEA produced the most optimal waste load allocation scenarios in the driest month of the future (August), So that the amount of BOD in the dam reservoir remains less than 5 mg/L. In the next step, the robustness of the solutions analyzed using OpenMORDM. Then, the results were analyzed using the patient rule induction method (PRIM) and classification and regression tree (CART) methods to identify the system vulnerability domains and minimizing false positives. Through the analysis, four climate scenarios have been used to explain how the methodological selection of them affects the resulting candidate planning solutions. The results indicated that individual design selections from the water managers or researchers could influence the efficacy of the model framework, which in turn results in failure of the most robust waste-load allocation strategies under future climate change.

Optimal waste load allocation using graph model for conflict resolution.

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.

Multiple pollutant discharge permit markets, a challenge for wastewater treatment plants

This study focuses on the challenge of using a multiple pollutant transferable discharge permit market for operating wastewater treatment plants. It uses an analytical case of Sefidrud River in Iran with two checkpoints. It shows that the operating limitations for simultaneous biochemical oxidation demand (BOD) and total nitrogen (TN) removal may convert the economically optimal waste load allocation (WLA) to a framework with lack of incentives. Therefore, water quality trading (WQT) may lose its economical advantages. In this case, a third parameter, such as dissolved oxygen is recommended as an index for assigning market interactions. In spite of economical and practical success, this approach made WLA become a more complicated and uncertain policy. It was totally concluded that using single parameter WQT is only recommended for areas with small agricultural activities or lakes. Otherwise, the integrated discharged permit and reclaimed water market is proposed instead for simultaneous BOD and TN management.

Enhancement of surface water quality using trading discharge permits and artificial aeration

Water quality trading (WQT) is a strategy for surface water quality management. This study focused on finding the optimal waste load allocation by WQT framework based on the concentrations of BOD and dissolved oxygen (DO) in Sefidrud River. It also assessed the consequences of using artificial aeration on trading discharge permits. For these purposes, river was simulated by Qual2kw. Two checkpoints were considered for monitoring the upstream (Sangar) and downstream (Caspian). Sensitivity analysis was carried out to find the impact factors of each emission sources. The required total maximum daily loads (TMDLs) were determined through simulation results. It predicted that the concentrations of ultimate BOD in both Sangar and Caspian stations may decrease from 3.6 and 8.1 mg/L, respectively, to 2.1 mg/L. Conversely, DO may increase from 5.8 and 0.9 mg/L to 7 and 3.7 mg/L, respectively. This could solely save 7 % of total treatment costs in comparison with the conventional command and control (C&C) policy. WQT may increase the revenues to 21 %. However, this could not provide enough flexibility and robustness in regard to the permit pricing and profits attained by stakeholders. Artificial aeration was considered as an improvement solution in the second scenario. The experimental results of hydroponic systems and hydraulic river simulation by CCHE2D showed that 0.25 day−1 can be added to the aeration coefficients. This modified TMDLs and impact factors thoroughly. Results verified that this approach may increase the revenues of TMDLs and discharge permit market, respectively, to 17 and 30 %. Permits can also be sold with higher prices and stakeholders may attain considerable profits. Consequently, the integration of artificial aeration with WQT was recommended for surface water quality enhancement.

A seasonal waste load allocation policy in an integrated discharge permit and reclaimed water market

This paper intends to assess the seasonal demand for nitrogen discharge permits and its influence on waste load allocation (WLA) strategies. For this purpose, the Sefidrud catchment is analyzed, where the agricultural demands for discharge permits and reclaimed water (RW) are significant and also intermittent. The paper discusses using variable total maximum daily loads and a transferable discharge permit (TDP) with respect to the farming and non-farming seasons to achieve annual economic benefits. However, an integrated TDP and RW market can considerably increase these revenues. Moreover, the latter may provide the flexibility required for practical dynamic WLA and makes it compatible with seasonal demands. The optimal WLA also determines the required wastewater treatment processes. Here, some should be designed and equipped with flexible operational processes. As a result, the novel integrated management approach saves 25% and 13% in comparison with the conventional TDP market in the short and long term, respectively. This framework also recommends the pricing for permits in a year to enhance stakeholders' motivation for active participation. It is concluded that the integrated TDP and RW market, using the recommended treatment processes and dynamic pricing, is an economically attractive and practical policy for seasonal WLA.

A simulation-optimization approach for assessing optimal wastewater load allocation schemes in the Three Gorges Reservoir, China

Abstract Background The Three Gorges Reservoir (TGR) has been facing deteriorated water quality issues since the construction of the Three Gorges Dam (TGD) in 1994. However, no previous studies have used a simulation-optimization assessment framework to examine the waste-load allocation patterns in the TGR area for alleviating its water pollution problem. In this study, a simulation-optimization modeling approach was developed for addressing this issue, through combining an environmental fluid dynamic code (EFDC)-based water quality simulation model and a waste-load allocation optimization model into a general framework. Results The approach was applied to a TGR section (Changshou-Fuling section) for identifying the optimal waste-load allocation schemes among its 11 wastewater discharge outlets. Firstly, the EFDC model was run to simulate the water quality response in the receiving water body under a single discharge load scenario, and the simulated COD and NH4 +-N concentrations were used to calculate the pollution mixing zone (PMZ), the pollution mixing zone per unit load (PMZPL), and sensitivity index (SI) pertaining to that outlet. These values were then used in the formulation of the waste-load allocation optimization model, with its objective being to maximize the environmental performance under constraints that existing waste discharge loads in terms of total wastewater amount, total pollutant mass, and existing PMZ size can’t be exceeded. Conclusions Modeling results give an optimal waste-load allocation ratio for each discharge outlet within the study section, and its implications to the reservoir water quality management were analyzed. It is anticipated that the develop approach can be extended to the entire TGR area for better water quality management studies and practices.

An assessment of optimal waste load allocation and assimilation characteristics in the Arroyo Colorado River watershed, TX along the US–Mexico border

Effective water quality management requires careful consideration of pollutant fate and transport, proper estimation of non-point source loadings, and maximum allowable allocation of point source discharges. A decision support system (DSS) that addresses all these issues is developed in this study by embedding mass-balance expressions, GIS, and a remote-sensing-based non-point source loading scheme into a hybrid goal-programming approach and is applied to the rapidly growing Arroyo Colorado River watershed along the US–Mexico border. The model components were favorably evaluated against field data and previous studies. The DSS was used to evaluate the carrying capacity of the river, defined based on the water quality standards for biochemical oxygen demand, dissolved oxygen, and minimum in-stream flow requirements. The results indicated that on a macro-scale, the current stresses utilize about 40 % of the maximum carrying capacity. However, the most upstream and downstream sub-watersheds are currently over stressed and need to reduce their loadings. The assimilative capacity of the river is not sufficient to carry current flows at their permitted discharge concentrations implying an inequity among discharges with regard to treatment cost burden. Sensitivity analyses indicated that the carrying capacity was more affected by policy choices made for water quality standards then where they were to be enforced (i.e., compliance locations). Urban areas currently cover 13 % of the watershed but contribute nearly 45 % of the total non-point source loadings. Therefore, the urbanization in this watershed must be carefully planned with emphasis on stormwater treatment and management to sustain this valuable resource for future generations.

Waste load allocation using non-dominated archiving multi-colony ant algorithm

Abstract The optimal Waste Load Allocation (WLA) problem, in general, is a mathematical model incorporating a water quality simulator within the framework of multi objective optimization. The goals are defined to improve the water quality and minimize the total treatment cost of dischargers. The fraction removal levels of dischargers are the decision variables. This paper explores the capabilities of Ant Colony algorithms to solve the optimal Waste Load Allocation as a multi-objective optimization problem. We use QUAL2K as a water quality simulator and a powerful ant colony algorithm known as Non-dominated Archiving Multi-colony Ant Algorithm (NA-ACO). The applicability of the model is demonstrated through a hypothetical case example. Results’ location near global optima demonstrates the suitable performance of applied algorithm.

Watershed-based point sources permitting strategy and dynamic permit-trading analysis

Permit-trading policy in a total maximum daily load (TMDL) program may provide an additional avenue to produce environmental benefit, which closely approximates what would be achieved through a command and control approach, with relatively lower costs. One of the important considerations that might affect the effective trading mechanism is to determine the dynamic transaction prices and trading ratios in response to seasonal changes of assimilative capacity in the river. Advanced studies associated with multi-temporal spatially varied trading ratios among point sources to manage water pollution hold considerable potential for industries and policy makers alike. This paper aims to present an integrated simulation and optimization analysis for generating spatially varied trading ratios and evaluating seasonal transaction prices accordingly. It is designed to configure a permit-trading structure basin-wide and provide decision makers with a wealth of cost-effective, technology-oriented, risk-informed, and community-based management strategies. The case study, seamlessly integrating a QUAL2E simulation model with an optimal waste load allocation (WLA) scheme in a designated TMDL study area, helps understand the complexity of varying environmental resources values over space and time. The pollutants of concern in this region, which are eligible for trading, mainly include both biochemical oxygen demand (BOD) and ammonia-nitrogen (NH 3-N). The problem solution, as a consequence, suggests an array of waste load reduction targets in a well-defined WLA scheme and exhibits a dynamic permit-trading framework among different sub-watersheds in the study area. Research findings gained in this paper may extend to any transferable dynamic-discharge permit (TDDP) program worldwide.

Non-Uniform Flow Effect on Optimal Waste Load Allocation in Rivers

In this study, the effects of non-uniform flow due to: (i) inflow from tributaries and (ii) the presence of a downstream control structure (such as a weir or a barrage), on the optimal waste load allocation decision and the resulting cost-equity trade-off relationships, have been investigated. These effects are illustrated with in the framework of a typical cost-equity multi-objective optimization model for optimal waste load allocation in rivers. This framework consists of an embedded river water quality simulator with gradually varied flow and transport (BOD-DO) modules and a cost-equity multi-objective optimization model. A multi-objective evolutionary algorithm known as Non-dominated Sorting Genetic Algorithm-II is used for solving the optimization problem. The optimal fraction removal levels, the treatment cost and the system inequity measure are under predicted in certain reaches of the river, if the uniform flow assumption is made, while actually non-uniform flow conditions exist. This effect is quite pronounced when the flow non-uniformity results from a downstream control structure such as a weir.

Multiobjective Optimal Waste Load Allocation Models for Rivers Using Nondominated Sorting Genetic Algorithm-II

A multiobjective optimization framework for optimal waste load allocation in rivers is proposed, considering (1) the total treatment cost, (2) the equity among the waste dischargers, and (3) a comprehensive performance measure that reflects the dissolved oxygen (DO) violation characteristics. This framework consists of an embedded river water quality simulator that has a gradually varied flow module and a pollutant transport module, which simulates the transport process including reaction kinetics (in terms of biochemical oxygen demand-DO). The outer shell of the framework consists of the two nonseasonal, deterministic, multiobjective waste load allocation planning models, namely, cost-performance model and cost-equity-performance model. These models are solved using a powerful and recently developed multiobjective genetic algorithm technique known as the Nondominated Sorting Genetic Algorithm-II. The practical utility of the multiobjective framework in decision-making is illustrated through a realistic example of the Willamette River in the state of Oregon.

Two Outfalls in an Estuary: Optimal Wasteload Allocation

When two outfalls are discharging wastewater into a narrow (rapidly mixed) estuary within a tidal excursion of each other, the pollutant concentrations experienced at the two outfall sites are strongly inter-dependent. It is shown how a given total tidally integrated effluent load can be allocated optimally between the two outfalls so that the peak concentration (in time and position) of the principal contaminant species is minimized. Graphical results show the dependence of the wasteload allocation and of the peak concentration upon the pollutant decay rate, the separation between the outfalls and the fresh water flow along the estuary. Optimization with respect to any one of a mixture of pollutants is close to optimal for a wide range of other pollutants.

Bi-objective analysis of waste load allocation using fuzzy linear programming

Because of its complexity from both a legal and economic standpoint, the problem of optimal waste load allocation is multiobjective by nature and should be treated accordingly. To perform this task, an optimization technique known as fuzzy linear programming is utilized in solving a multiple-discharge, two-objective waste load allocation problem. The two objectives considered are: (1) the maximization of waste discharge and (2) the minimization of the largest difference in equity measure between the various dischargers. Results from this study reveal that fuzzy linear programming is a valuable tool for solving the multiple-objective water quality management problems. Moreover, it is shown that the selection of a linear or logistic membership function in providing preference criteria between the two objects, has no effect on the ‘best compromising solution’.