Identifying remedial solutions through optimal bioremediation design under real-world field conditions

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.