Model predictive control for in situ bioremediation system
This study describes development of a dynamic predictive control system for the in situ bioremediation process. This automated control system not only balances the complex physical, chemical, and biological processes involved in the remediation process, it also minimizes overall cost of the entire remediation process. The control system is on-line, dynamic, and built based on a predictive model. It includes an optimization tool that consists of a simulation model and an optimization function. The numerical simulation model describes the fate and transport of the subsurface contaminants, while the optimization function is a constrained nonlinear function that has been implemented using genetic algorithms. The system was applied to a lab experiment, and tested with data from a real world site. The results indicated that the dynamic, simulation model-based control system can generate an appropriate control strategy and adjust control actions dynamically. This helps to improve efficiencies of the in situ bioremediation process at petroleum-contaminated groundwater systems.
in situ bioremediation simulation model genetic algorithm nonlinear optimization objective function