A Multiobjective Optimisation Model for Groundwater Remediation Design at Petroleum Contaminated Sites

This study proposes a fuzzy multi-objective model for groundwater remediation in petroleum-contaminated aquifers. The optimisation system is designed based on the PAT technology, and includes two objectives (i.e. total pumping rate and average post-remedial contaminant concentration). The relationship between pumping rates and contamination concentrations at all monitoring wells after remediation are determined by a proxy model, which integrates simulation, inference, and optimisation technologies and is composed of intercept, linear, interactive, and quadratic options. Fuzzy algorithms are used to solve the formulated multi-objective optimisation problem to find optimal solutions. The model is then applied to a petroleum-contaminated aquifer in western Canada. The trade-off and λ analyses of the results indicate that the fuzzy multi-objective model has great potential in groundwater remediation applications as it can: (1) provide reliable groundwater remediation strategies, (2) reduce computational costs in the optimization processes, and (3) balance the trade-off between remediation costs and remediation outcomes.     

基于可变环境标准约束的地下水修复优化模型

提出了一个集成的模拟-优化方法用于设计不确定条件下受石油污染的地下水最优修复方法。该方法有以下4个优点:1通过建立的代理回归模型给修复策略(井的抽注速率)和修复性能(污染物浓度)之间提供了直接且响应迅速的桥梁;2减轻优化过程中产生的计算工作量;3提供一个能够揭示满足环境质量标准程度大小的满意度水平;4防止过于乐观或悲观的优化策略的制定。将该方法应用于位于安徽省某发电厂受石油污染的地下水含水层区域,用来识别最佳的修复策略。结果表明:在较宽松的、对修复效果影响不是很大的环境标准或者较长的修复周期条件下,大多数抽注修复井的抽注速率将会减小,伸缩指数的增加对修复优化结果并没有产生严重影响。     

A New Groundwater Management Model by Coupling Analytic Element Method and Reverse Particle Tracking with Cat Swarm Optimization

The performance of groundwater management models mostly depends upon the methodology employed to simulate flow and transport processes and the efficiency of optimization algorithms. The present study examines the effectiveness of cat swarm optimization (CSO) for groundwater management problems, by coupling it with the analytic element method (AEM) and reverse particle tracking (RPT). In this study, we propose two coupled simulation-optimization models, viz. AEM-CSO and AEM-RPT-CSO by combining AEM with RPT and CSO. Both the models utilize the added advantages of AEM, such as precise estimation of hydraulic head at pumping location and generation of continuous velocity throughout the domain. The AEM-CSO model is applied to a hypothetical unconfined aquifer considering two different objectives, i.e., maximization of the total pumping of groundwater from the aquifer and minimization of the total pumping costs. The model performance reflects the superiority of CSO in comparison with other optimization algorithms. Further, the AEM-RPT-CSO model is successfully applied to a hypothetical confined aquifer to minimize the total number of contaminant sources, within the time related capture zone of the wells, while maintaining the required water demand. In this model, RPT gets continuous velocity information directly from the AEM model. The performance evaluation of the proposed methodology, illustrates its ability to solve groundwater management problems.     

Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management

This study integrates an artificial neural network (ANN) and constrained differential dynamic programming (CDDP) to search for optimal solutions to a nonlinear time-varying groundwater remediation-planning problem. The proposed model (ANN-CDDP) determines optimal dynamic pumping schemes to minimize operating costs and meet water quality requirements. The model uses two embedded ANNs, including groundwater flow and contaminant transport models, as transition functions to predict groundwater levels and contaminant concentrations under time-varying pumping. Results demonstrate that ANN-CDDP is a simplified management model that requires considerably less computation time to solve a fine mesh problem. For example, the ANN-CDDP computing time for a case involving 364 nodes is 1/26.5 that of the conventional optimization model.     

Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation

We herein propose a simulation-optimization model for groundwater remediation, using PAT (pump and treat), by coupling artificial neural network (ANN) with the grey wolf optimizer (GWO). The input and output datasets to train and validate the ANN model are generated by repetitively simulating the groundwater flow and solute transport processes using the analytic element method (AEM) and random walk particle tracking (RWPT). The input dataset is the different realization of the pumping strategy and output dataset are hydraulic head and contaminant concentration at predefined locations. The ANN model is used to approximate the flow and transport processes of two unconfined aquifer case studies. The performance evaluation of the ANN model showed that the value of mean squared error (MSE) is close to zero and the value of the correlation coefficient (R) is close to 0.99. These results certainly depict high accuracy of the ANN model in approximating the AEM-RWPT model. Further, the ANN model is coupled with the GWO and it is used for remediation design using PAT. A comparison of the results of the ANN-GWO model with solutions of ANN-PSO (ANN-Particle Swarm Optimization) and ANN-DE (ANN-Differential Evolution) models illustrates the better stability and convergence behaviour of the proposed methodology for groundwater remediation.

     

Optimal Groundwater Remediation Under Uncertainty Using Multi-objective Optimization

A methodology is developed for optimal remediation of groundwater aquifers under hydraulic conductivity uncertainty. A multi-objective management method based on a pump-and-treat remediation technology, is proposed. The pumping rates and well locations are the decision variables and two objectives are chosen: minimization of contaminated groundwater in the aquifer and minimization of remediation cost. A Monte Carlo simulation method is used to cope with hydraulic conductivity uncertainty. A number of equally probable realizations of hydraulic conductivity are created and a Pareto front is obtained using a modified multi-objective Genetic Algorithm. A penalty function is utilized to maintain the algebraic sum of pumping and recharging rates equal to zero. Since Monte Carlo simulations are CPU time consuming, a method is proposed to identify the few significant realizations which have an effect on the optimal solution (critical realizations). A Pareto front with an assigned probability is derived, so that the decision maker can make decisions with specified reliability. In a case study with 100 realizations, only 11 realizations were found critical and need be considered. The remaining 89 realizations consistently obtain low ranks for all designs considered and do not affect decisions at 95% reliability level. Thus these realizations need not be considered which implies a 89% savings in computer time. The designs obtained using the critical realizations, retain a similar reliability for new realizations not considered in the design process.     

Multiobjective management of a contaminated aquifer for agricultural use

Planned utilization of groundwater from a contaminated aquifer requires development of management strategies that determine the spatial distribution of withdrawal for allocation, as well as for control of water quality. Minimization of groundwater allocation for different purposes, and the control of contamination in the aquifer by a specified pumping strategy constitute a management problem with two conflicting objectives. In order to demonstrate possible tradeoffs between water quality control objective and minimum groundwater withdrawal objective, a multiobjective optimization model is formulated. The solution of the model specifies a strategy to control pollution distribution in the aquifer as per agricultural needs, and also evolve an optimal allocation policy to statisfy agricultural demands. Pareto-optimal solutions representing the tradeoff between the two noncommensurate objectives are established. The formulated model uses the embedding technique for simulating the flow and the transport processes in the aquifer. The constraint method is used to transform the multiobjective optimization model into a single objective optimization model. The resulting model is solved using the exterior penalty function method in conjunction with the Hooke-Jeeves method. The proposed model is easily adoptable for various agroclimatic regions and cropping patterns. For illustrative purposes, the model is applied to a specified study area. Although solutions of the model are presented and discussed as per agricultural requirements in terms of both quality and quantity, solutions for other kinds of water demands can be obtained using the same model with minor modifications. Results show that an optimal pumping strategy can be effectively utilized for controlling contamination in the aquifer.     

Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design

Obtaining optimal solutions for time-varying groundwater remediation design is a challenging task. A novel procedure first employs input/output data sets obtained by constrained differential dynamic programming (CDDP). Then the Adaptive-Network-Based Fuzzy Inference System (ANFIS), which is a fuzzy inference system (FIS) implemented in the adaptive network framework, is applied to acquire time-varying pumping rates. Results demonstrate that the FIS is an efficient way of groundwater remediation design.     

Distributed Simulation‐optimization Model for Conjunctive Use of Groundwater and Surface Water Under Environmental and Sustainability Restrictions

Evolving optimal management strategies are essential for the sustainable development of water resources. A coupled simulation-optimization model that links the simulation and optimization models internally through a response matrix approach is developed for the conjunctive use of groundwater and surface water in meeting irrigation water demand and municipal water supply, while ensuring groundwater sustainability and maintaining environmental flow in river. It incorporates the stream-aquifer interactions, and the aquifer response matrix is generated from a numerical groundwater model. The optimization model is solved by using MATLAB. The developed model has been applied to the Hormat-Golina valley alluvial stream-aquifer system, Ethiopia, and the optimal pumping schedules were obtained for the existing 43 wells under two different scenarios representing with and without restrictions on stream flow depletion, and satisfying the physical, operational and managerial constraints arising due to hydrological configuration, sustainability and ecological services. The study reveals that the total annual optimal pumping is reduced by 19.75?% due to restrictions on stream flow depletion. It is observed that the groundwater pumping from the aquifer has a significant effect on the stream flow depletion and the optimal conjunctive water use plays a great role in preventing groundwater depletion caused by the extensive pumping for various purposes. The groundwater contribution in optimal conjunctive water use is very high having a value of 92?% because of limited capacity of canal. The findings would be useful to the planners and decision makers for ensuring long-term water sustainability.

     

Multi-Criteria Decision Analysis for the Purposes of Groundwater Control System Design

The best way for an engineer or scientist to express their knowledge, experience and opinions is day-to-day verbal communication. When a decision needs to be made about an optimal groundwater control system, the decision-making criteria need not always be numerical values. If fuzzy logic is used in multi-criteria decision-making, the criteria are described by linguistic variables that can be represented through fuzzy membership and expert judgment is used to describe such a system. Prior hydrodynamic modeling of the aquifer regime defines the management scenarios for groundwater control and provides an indication of their effectiveness. In this paper, the fuzzy analytic hierarchy process is applied to deal with a trending decision problem such as the selection of the optimal groundwater management system. Linguistic variables are used to evaluate all the criteria and sub-criteria that influence the final decision and the numerical weights of each alternative are determined by mathematical calculations. The paper presents a part of the algorithm – fuzzy optimization in hydrodynamic analysis, which leads to the selection of the optimal groundwater control system. The proposed method is applied in a real case study of an open-cast mine.     

Optimal Groundwater Management Using Genetic Algorithm in El-Farafra Oasis,Western Desert,Egypt

Groundwater is the unique source of fresh water in El-Farafra Oasis, western desert, Egypt. The increasing demand of groundwater in El-Farafra Oasis has resulted in an indiscriminate exploitation of this source causing environmental hazards such as decline of groundwater levels and well interference. In this paper, the study of these problems is conducted. The methodology introduced in this paper includes application of mathematical and Genetic Algorithm (GA) techniques. This situation has led to a growing realization that through good management, use of groundwater can be made more productive and sustainable. The proposed model of optimization is based on the combination of the MODFLOW with GA. The performance of the proposed model is tested on groundwater management problem (maximization of total pumping rate from an aquifer at steady-state). The results show that the GA solutions nearly agree with the solutions of other methods of previous works. Thus, it can be used to solve the management problems in groundwater. This model is used to develop the optimal pumping rate and number of wells in El-Farafra Oasis under different scenarios. The results show that under the current situation, the optimal pumping rate is 183023 m³/day. The second scenario assumes an increase of number of wells by 20%, the optimal rate reaches 220016 m³/day. The third scenario proposes pumping rate 254484 m³/day which equalizes an increase in the cultivated area by 4000 acres, the optimal rate reaches 258007 m³/day.     

基于和声搜索算法的地下水污染源与未知含水层参数的同步反演研究

地下水污染源反演问题和含水层参数反演问题都是典型的地下水逆问题。在未知含水层参数(渗透系数、弥散度等)等先决信息的情况下进行地下水污染源反演计算时,需要根据已有的监测数据(水位和浓度等)对地下水污染源和未知含水层参数进行同步反演。在同步反演优化问题中,决策变量包括污染源位置、强度以及待求的含水层参数。论文首先介绍同步反演模型的框架组成(包括污染物迁移模型和反演优化模型),然后在对已有的各种和声搜索改进算法进行研究的基础上结合同步反演模型提出一种改进的和声搜索算法,最后将同步反演模型和改进的和声搜索算法应用于具体的算例研究。研究表明,改进的和声搜索算法具有算法稳定高效、求解精度高等特点,能够广泛应用于复杂的地下水污染源和含水层参数反演问题。     

In situ source zone sediment mixing coupled to groundwater biostimulation to enhance phenol natural attenuation

Phenol is an industrially key compound that has a wide range of applications and also one of the most commonly found toxic pollutants in wastewaters and groundwater. This paper demonstrates the applicability of in situ remediation at a deactivated industrial site using source zone excavation and sediment mixing associated with nutrients delivery into groundwater. Sediment excavation and mixing displaced the entrapped source zone enhancing mass transfer into groundwater and contaminant bioavailability. A nutrient solution prepared with nitrate, phosphate, sodium hydroxide and hydrogen peroxide was continuously delivered into groundwater to stimulate biodegradation and restrict plume migration. The observed correlation between phenol-dependent Enterobacteriaceae concentrations throughout the remediation time frame supported circumstantial evidence of biodegradation. Phenol concentration in groundwater (up to 1,300 mg/L) was reduced >99% after 5 months following remediation and remained under the established site specific target level (4 mg/L). Nitrate and phosphate concentrations returned to background concentrations levels at the end of the remediation. Overall, the proposed in situ remediation scheme was effective to remediate this particular aquifer contaminated with phenol for over 20 years.     

Brackish groundwater resources in Bahrain: Current exploitation,numerical evaluation and prospect for utilization

In Bahrain, where water resources available for direct use are finite and the best of its quality has a salinity of over 2.5 g L^–1, utilization of brackish groundwater is an essential part in the management of the country's water resources. Bahrain's brackish water occurs in the Rus-Umm Er Radhuma formations in the form of a lens of a finite lateral extent, with a salinity ranges between 8 and 15 g L^–1. Planning for utilization of brackish groundwater for desalination purposes in Bahrain was based on simulation modeling of the aquifer system using a mixing cell model developed originally in 1983. The model was used to predict the aquifer response to pumping from the proposed wellfield in terms of changes of TDS over a period of 20 years. Construction and operation of the wellfield in 1984 was based on the predicted salinity changes. Over the past 9 uears of wellfield operation (1984–1993), and through continuous monitoring of the aquifer response to pumping, the collected data is used to post-audit the original model by history matching. The calibration process adopted has resulted in a statisfactory agreement between the model output and the observed data. The model is then used to predict the wellfield salinity changes and the aquifer potentiometric levels. The expected life span for the brackish groundwater utilization by the wellfield is redefined through constrained utilization that takes into account salinity deterioration coupled with the effect of head decline on hydraulic interaction between the brackish water and the upper fresh water aquifer. The results suggest that the operation of the wellfield should cease by the year 2007. Construction of a new model that enables testing and evaluating different development scenarios is recommended to aid future management decisions regarding the utilization of brackish groundwater.     

Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty

Combined simulation-optimization approaches have been used as tools to derive optimal groundwater management strategies to maintain or improve water quality in contaminated or other aquifers. Surrogate models based on neural networks, regression models, support vector machies etc., are used as substitutes for the numerical simulation model in order to reduce the computational burden on the simulation-optimization approach. However, the groundwater flow and transport system itself being characterized by uncertain parameters, using a deterministic surrogate model to substitute it is a gross and unrealistic approximation of the system. Till date, few studies have considered stochastic surrogate modeling to develop groundwater management methodologies. In this study, we utilize genetic programming (GP) based ensemble surrogate models to characterize coastal aquifer water quality responses to pumping, under parameter uncertainty. These surrogates are then coupled with multiple realization optimization for the stochastic and robust optimization of groundwater management in coastal aquifers. The key novelty in the proposed approach is the capability to capture the uncertainty in the physical system, to a certain extent, in the ensemble of surrogate models and using it to constrain the optimization search to derive robust optimal solutions. Uncertainties in hydraulic conductivity and the annual aquifer recharge are incorporated in this study. The results obtained indicate that the methodology is capable of developing reliable and robust strategies for groundwater management.     

Simulation Modeling for Efficient Groundwater Management in Balasore Coastal Basin,India

The Balasore coastal groundwater basin in Orissa, India is under a serious threat of overdraft and seawater intrusion. The overexploitation resulted in abandoning many shallow tubewells in the basin. The main intent of this study is the development of a 2-D groundwater flow and transport model of the basin using the Visual MODFLOW package for analyzing the aquifer response to various pumping strategies. The simulation model was calibrated and validated satisfactorily. Using the validated model, the groundwater response to five pumping scenarios under existing cropping conditions was simulated. The results of the sensitivity analysis indicated that the Balasore aquifer system is more susceptible to the river seepage, recharge from rainfall and interflow than the horizontal and vertical hydraulic conductivities and specific storage. Finally, based on the modeling results, salient management strategies are suggested for the long-term sustainability of vital groundwater resources of the Balasore groundwater basin. The most promising management strategy for the Balasore basin could be: a reduction in the pumpage from the second aquifer by 50% in the downstream region and an increase in the pumpage to 150% from the first and second aquifer at potential locations.     

Reconstructing the release history of a groundwater contaminant based on AT123D

The reconstruction of the source release history for groundwater contamination provides useful environmental forensic information in identifying the responsible parties for a contaminant plume at a known source location if its owner changed several times in the past. The objective of this study is to develop a novel method consisting of function-fitting technique, simulated annealing, and a fundamental solution of the contaminant transport equation given in AT123D (Yeh, 1981) to recover the source release history of a groundwater contamination. AT123D is an analytical model for simulating transient, one-, two-, and three-dimensional groundwater contaminant transport in aquifer systems. The method developed herein is for recovering the release history of a continuous and finite release duration source and therefore can handle a great variety of realistic problems. Cases with various types of source geometry and aquifer configuration are considered. The influence of contaminant biodegradation, degree of dispersion, location of monitoring well, use of temporal concentration data or spatial concentration data, and presence of two contaminated sources are also investigated. Finally, a few guidelines for the optimal sampling strategy on the reconstruction of the source release history are recommended.     

地下水环境修复工艺优化设计研究进展

介绍了3种应用相对广泛的地下水修复技术,即抽出处理法、可渗透反应墙和自然降解法,重点回顾和评述了地下水修复系统优化设计的方法,探讨了结合健康风险评价体系和不确定性研究的地下水修复系统优化设计,旨在为地下水资源的修复和管理提供理论依据。     

Modeling Groundwater Flow and Seawater Intrusion in the Coastal Aquifer of Wadi Ham,UAE

Groundwater pumping from Kalbha and Fujairah coastal aquifer of the United Arab Emirates (UAE) has increased significantly during the last two decades to meet the agriculture water demands. Due to the lack of natural replenishment from rainfall and the excessive pumping, groundwater levels have declined significantly causing an intrusion of seawater in the coastal aquifer of Wadi Ham. As a result, many pumping wells in the coastal zone have been terminated and a number of farms have been abandoned. In this paper, MODFLOW was used to simulate the groundwater flow and assess the seawater intrusion in the coastal aquifer of Wadi Ham. The model was calibrated against a five-year dataset of historical groundwater levels and validated against another eleven-year dataset. The effects of pumping on groundwater levels and seawater intrusion were investigated. Results showed that reducing the pumping from Khalbha well field will help to reduce the seawater intrusion into the southeastern part of the aquifer. Under the current groundwater pumping rates, the seawater will continue to migrate inland.     

Current Practice and Future Challenges in Coastal Aquifer Management: Flux-Based and Trigger-Level Approaches with Application to an Australian Case Study

The control of groundwater abstraction from coastal aquifers is typically aimed at minimizing the risk of seawater intrusion, excessive storage depletion and adverse impacts on groundwater-dependent ecosystems. Published approaches to the operational management of groundwater abstraction from regulated coastal aquifers comprise elements of “trigger-level management” and “flux-based management”. Trigger-level management relies on measured groundwater levels, groundwater salinities and/or ecosystem health indicators, which are compared to objective values (trigger levels), thereby invoking management responses (e.g. pumping cut-backs). Flux-based management apportions groundwater abstraction rates based on estimates of aquifer recharge and discharge (including environmental water requirements). This paper offers a critical evaluation of coastal aquifer management paradigms using published coastal aquifer case studies combined with a simple evaluation of the Uley South coastal aquifer, South Australia. There is evidence that trigger-level management offers advantages over flux-based approaches through the evaluation of real-time resource conditions and trends, allowing for management responses aimed at protecting against water quality deterioration and excessive storage depletion. However, flux-based approaches are critical for planning purposes, and are required to predict aquifer responses to climatic and pumping stresses. A simplified modelling analysis of the Uley South basin responses to different management strategies demonstrates the advantages of considering a hybrid management approach that includes both trigger-level and flux-based controls. It is recommended that where possible, trigger-level and flux-based approaches be adopted conjunctively to minimize the risk of coastal groundwater degradation and to underpin strategies for future aquifer management and well-field operation.