ENSEGID, EA4592 G&E, 1 allée Daguin, 33607 Pessac, France.
BRGM, 3 avenue Claude-Guillemin, 45060 Orléans, France.
J Contam Hydrol. 2021 Feb;237:103751. doi: 10.1016/j.jconhyd.2020.103751. Epub 2020 Dec 5.
Over more than a century of intense industrial production and associated accidental release, petroleum products (e.g., gasoline, diesel, fuel oil) have contaminated a significant portion of the world's groundwater resources. Groundwater remediation is generally a complex task, especially where aquifers and the associated contaminant distribution are highly heterogeneous. The ability to predict the efficiency of such remediation is of crucial importance, as the costs are strongly linked to the treatment design and duration. In this study, a coupled simulation-optimization (S/O) framework, consisting of a process-based reactive transport simulation model linked with particle swarm optimization (PSO) was developed. It was subsequently applied for the design of a real-world in situ bio-treatment of a BTEX contaminated aquifer in France. In the application, the optimization framework was used to simultaneously determine optimal well locations and their optimal injection rates, both constituting key elements of the enhanced biodegradation design problem. The optimization of the treatment efficiency was examined in terms of three different regulatory objectives, (1) minimization of the residual NAPL mass of the key contaminant, i.e., benzene, in the source zone, (2) reduction of the maximum concentration of benzene in groundwater, and (3) minimization of the time required to reduce the benzene concentration in groundwater to below a threshold value. Our analysis of potential, optimal remediation strategies showed that: (i) the complexity of the biodegradation behavior at real sites may favor very different remediation options as a result of varying remediation targets, (ii) the long term behavior of the contaminants after the end of the active treatment period, which is often neglected, showed to have a significant influence on remediation design that requires increased attention, (iii) PSO has shown to be a very efficient algorithm in the context of the present study. The insights that can be gained from such a framework will provide decision support to select the most suitable remediation strategy while facing different regulatory objectives.
在一个多世纪的密集工业生产和相关的意外泄漏之后，石油产品（如汽油、柴油、燃料油）已经污染了世界上相当一部分的地下水资源。地下水修复通常是一项复杂的任务，特别是在含水层和相关污染物分布高度不均匀的情况下。预测这种修复效率的能力至关重要，因为成本与处理设计和持续时间密切相关。在本研究中，开发了一个耦合的模拟-优化（S/O）框架，由一个基于过程的反应传输模拟模型与粒子群优化（PSO）相结合。随后，该框架应用于设计法国一个受 BTEX 污染的含水层的现场生物处理。在应用中，优化框架用于同时确定最优井位及其最优注入率，这两者都是增强生物降解设计问题的关键要素。优化处理效率是根据三个不同的监管目标来考察的，（1）最小化源区关键污染物苯的残留 NAPL 质量，（2）降低地下水苯的最大浓度，（3）最小化将地下水苯浓度降低到阈值以下所需的时间。我们对潜在的最优修复策略的分析表明：（i）实际场地生物降解行为的复杂性可能由于不同的修复目标而导致非常不同的修复选择，（ii）在主动处理期结束后污染物的长期行为，这通常被忽视，对修复设计有重大影响，需要更多关注，（iii）PSO 在本研究中表现出非常高效的算法。从这样的框架中获得的见解将为选择最适合的修复策略提供决策支持，同时面对不同的监管目标。
