A multi-level method for groundwater remediation management accommodating non-competitive objectives

•This study develops a new multi-level nonlinear optimization model.•The multilevel contains the residential, energy and environmental concerns.•An improved possibilistic algorithm is introduced with parameter uncertainty.•The model can simultaneously deal with multiple remediation objectives.•The m...

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Vydané v:Journal of hydrology (Amsterdam) Ročník 570; s. 531 - 543
Hlavní autori: Lu, Hongwei, Li, Jing, Chen, Yizhong, Lu, Jingzhao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.03.2019
Predmet:
energy
energy costs
groundwater
Groundwater remediation
Health risks
human health
issues and policy
Multi-level programming
Optimization
remediation
risk
Saskatchewan
Saskatchewan
Multi-level programming
Groundwater remediation
Optimization
Health risks
ISSN:0022-1694, 1879-2707
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Shrnutí:•This study develops a new multi-level nonlinear optimization model.•The multilevel contains the residential, energy and environmental concerns.•An improved possibilistic algorithm is introduced with parameter uncertainty.•The model can simultaneously deal with multiple remediation objectives.•The model can handle the leader-follower relationships among multiple levels. No attempts have been found on dealing with a set of non-competitive (or leader-follower-interactive) objectives when performing optimal groundwater remediation management. This study presents a multi-level nonlinear simulation-optimization (ML-NSO) model for groundwater remediation management when objectives should be satisfied at multiple levels. This model is formulated by integrating health-risk assessment (at the residential concern level), energy assessment (at the energy concern level) and contamination forecasting (at the environmental concern level) within a general framework. The capabilities and effectiveness of the developed model are illustrated through a real-world case located at Cantuar, Saskatchewan in Canada. Results facilitate (a) generating non-compromised solutions in association with the optimal strategies regarding groundwater injection and extraction, (b) displaying the distribution of contaminant concentration and carcinogenic risks for human health, and estimating the corresponding energy consumption, (c) resolving of conflicts and interactions among residential, energy, and environmental requirements. Moreover, the performance of ML-NSO model is enhanced by comparing with the single-level and multi-objective (SL-NSO and MO-NSO) models. Results show that ML-NSO model would assign higher priority on the residential and environmental concerns by tolerating a slight rise in energy cost. The ML-NSO model would provide more comprehensive and systematic policies with considering the leader-follower relationship within system.
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content type line 23
ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2019.01.018