A Stepwise-Inference-Based Optimization System for Supporting Remediation of Petroleum-Contaminated Sites

Groundwater contamination by leakage and spill of petroleum hydrocarbons from underground storage tanks has been a major environmental concern. Among various remediation alternatives, the vacuum-enhanced free product recovery (VFPR) is an important technology to extract light nonaqueous-phase liquids (LNAPLs) from subsurface. However, efficient design of a VFPR system was challenging to practitioners, since the process of hydrocarbon removal is costly and time consuming. To address such a problem, an integrated study system for optimizing the VFPR process was developed through coupling a numerical modeling system, a multivariate regression technique and nonlinear optimization model into a general framework. A two-dimensional multiphase flow simulation system was provided for modeling VFPR processes. An iterative stepwise-inference regression (ISIR) method was advanced for establishing a linkage between remediation actions and system responses. A nonlinear optimization model embedded with ISIR was then established for generating desired operating conditions. The results from a case study demonstrated that the established optimization model could effectively analyze tradeoffs between various environmental and economical considerations, and provide effective decision supports for site remediation practices. Compared with the conventional stepwise-cluster analysis method, the proposed ISIR method was more efficient and reliable in approximating relationships between remediation actions and system responses, and could significantly enhance the robustness of optimization solutions.