Integrated simulation-optimization approach for real-time dynamic modeling and process control of surfactant-enhanced remediation at petroleum-contaminated sites

Surfactant-enhanced remediation is an effective approach for dealing with subsurface-contaminated sites. However, in studying and controlling the related processes, difficulties exist in incorporating a complicated numerical simulation model that is needed for process forecasting within a real-time nonlinear optimization framework that is critical for supporting process control. In this study, an integrated simulation-optimization approach was developed for supporting real-time dynamic modeling and process control of surfactant-enhanced remediation at petroleum-contaminated sites. Subsurface modeling is combined with a dual-response surface method to develop a system for generating optimum operation conditions under various site conditions, through the support of a nonlinear optimization model. The development methodology was applied to a real-world case study in western Canada. Using the developed three-dimensional multiphase and multicomponent model, the surfactant-enhanced remediation process that is being implemented on the study site was simulated. The results provide useful information for further dual-response surface analysis to support the development of an optimization model to determine optimum process operation conditions. Under each initial contaminant concentration, optimum operation conditions can then be identified through this combined dual-response surface method optimization approach. Thus, a decision support system can then be produced to guide decisions of remediation process control under various site conditions.