Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions

Wu, Jianfeng; Chen, Kewei; Chien, Calvin C.

doi:10.1016/j.jconhyd.2004.11.006

Cited by 92 publications

(55 citation statements)

References 51 publications

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“…The noisy genetic algorithm (NGA) is an effective method to determine the optimal values of parameters under uncertainty (Miller and Goldberg, 1996). The NGA has been applied for stochastic reservoir operation (Yun et al, 2010), ground water remediation (Aly and Peralta, 1999) and groundwater sampling network design (Wu et al, 2005) under uncertainty. In this research, NGA is applied to optimise the hydropower portfolio under uncertainty of flows and spot price.…”

Section: Noisy Genetic Algorithmmentioning

confidence: 99%

Portfolio optimisation for hydropower producers that balances riverine ecosystem protection and producer needs

Yin

Zhang

Liu

2014

Hydrol. Earth Syst. Sci.

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Abstract. In deregulated electricity markets, hydropower portfolio design has become an essential task for producers. The previous research on hydropower portfolio optimisation focused mainly on the maximisation of profits but did not take into account riverine ecosystem protection. Although profit maximisation is the major objective for producers in deregulated markets, protection of riverine ecosystems must be incorporated into the process of hydropower portfolio optimisation, especially against a background of increasing attention to environmental protection and stronger opposition to hydropower generation. This research seeks mainly to remind hydropower producers of the requirement of river protection when they design portfolios and help shift portfolio optimisation from economically oriented to ecologically friendly. We establish a framework to determine the optimal portfolio for a hydropower reservoir, accounting for both economic benefits and ecological needs. In this framework, the degree of natural flow regime alteration is adopted as a constraint on hydropower generation to protect riverine ecosystems, and the maximisation of mean annual revenue is set as the optimisation objective. The electricity volumes assigned in different electricity submarkets are optimised by the noisy genetic algorithm. The proposed framework is applied to China's Wangkuai Reservoir to test its effectiveness. The results show that the new framework could help to design eco-friendly portfolios that can ensure a planned profit and reduce alteration of the natural flow regime.

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Section: Noisy Genetic Algorithmmentioning

confidence: 99%

Portfolio optimisation for hydropower producers that balances riverine ecosystem protection and producer needs

Yin

Zhang

Liu

2014

Hydrol. Earth Syst. Sci.

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“…Numerous groundwater management studies have been conducted in China and elsewhere, such as management modelling based on distributed hydraulic parameters (Xu & Shao, 1988), the management of groundwater resources in the Shi-Jia-Zhuang municipality (Li & Jiao, 1987), optimal design of a groundwater quality monitoring network (Wu et al, 2005), and development of an integrated global and local optimization approach for remediation system design (Zheng & Wang, 1999) However, optimal management of groundwater resources in mining, considering mine drainage, water supply and environmental protection, is still a new research area in China and elsewhere. Here, we develop a groundwater management model based on the requirements of coal mining management in North China.…”

Section: Optimal Models Of Coal Mine Water Managementmentioning

confidence: 99%

“…The simulation was based on the three-dimensional, finite element numerical code, ELE-3D (Wu et al, 2005. Transient observation data of the study area were used to calibrate the flow model.…”

Section: Numerical Modellingmentioning

confidence: 99%

“…Since then, however, study of this issue has decreased significantly (Indrarabna & Ramanda, 1994;Mayo et al, 2000), probably due to the shift in energy sources from coal to petroleum. Since the 1980s, on the other hand, predicting and remediating groundwater contamination has become an active research area, and many studies have been conducted to determine the optimal design of groundwater monitoring or remediation systems (Loaiciga, 1989;Wu et al, 2005). Numerous management models have been proposed (Willis, 1985;Moeshed & Kaluarachchi, 1998;Zheng & Wang, 1999;Mayer et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Coal mine water management: optimization models and field application in North China

Wan

et al. 2010

Hydrological Sciences Journal

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This paper considers the complexity in resolving the conflicts between mine drainage, water supply, and environmental protection for the coal basin of North China, and presents a management optimization framework that addresses these multiple conflicting issues simultaneously in the most cost effective manner. Due to the various unpredictable accidents which may occur in the coal mining process, such as water bursts, gas leaks, fire and collapse of coal beds, the beneficial use of drainage water from the coal mines is generally low. This case study attempts to address the problem of low beneficial usage for drainage water using the Jiaozuo coal mining district in Henan Province, China. By combining a finite-element groundwater simulation model with an optimization code, the economic benefits of using the drainage water as a stable water supply is maximized, while the adverse impact of mine drainage on the environment is controlled. The results indicate that the management model developed in this study achieves an excellent economic outcome and can serve as a potentially powerful tool for solving miningrelated water management problems in the coal basin of North China.

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“…Reference [17] optimized sampling networks using geostatistical interpolation through inverse distance weighting (IDW), ordinary kriging combined with a fate-and-transport model, and genetic algorithms. Reference [25] introduced new spatial moment constraints based on [17] to obtain robust long-term monitoring optimization designs. Reference [12] designed an improved detection network for a landfill using a two-dimensional simulation model alone without mathematical optimization methods.…”

Section: Introductionmentioning

confidence: 99%

Reducing Spatial Sampling in Long-Term Groundwater Monitoring Networks Using Ant Colony Optimization

Li¹,

Hilton²

2005

IJCIR

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Groundwater long-term monitoring (LTM) isrequired to assess human health and environmental risk of residual contaminants after active groundwater remediation activities are completed. However, LTM can be costly because of the large number of sampling locations that exist at a site from previous site characterization and remediation activities. The cost of LTM may be reduced by identifying redundant sampling locations. However, care must be taken so that the elimination of specific individual wells from the monitoring network does not result in unacceptable levels of data loss and errors. An ant colony optimization (ACO) algorithm is proposed to identify optimal sampling networks that minimize the number of monitoring locations while maintaining the overall data loss below a given threshold. ACO is inspired by the ability of an ant colony to identify the shortest route between its nest and a food source through indirect communication and positive feedback. Metrics for quantifying well redundancy and overall data loss after optimization are quantified and used in the ACO heuristics. To demonstrate its effectiveness, the ACO developed for LTM optimization is applied to a case study with 30 existing monitoring wells. The LTM optimization problem was solved using different data loss thresholds to identify solutions with 27 to 21 wells remaining in the LTM network. Contour mapping of the contaminant plume using the remaining wells show that the ACO solutions are effective and practical. These results demonstrated that ACO is a promising method for solving LTM optimization problems.

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Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions

Cited by 92 publications

References 51 publications

Portfolio optimisation for hydropower producers that balances riverine ecosystem protection and producer needs

Portfolio optimisation for hydropower producers that balances riverine ecosystem protection and producer needs

Coal mine water management: optimization models and field application in North China

Reducing Spatial Sampling in Long-Term Groundwater Monitoring Networks Using Ant Colony Optimization

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