An intelligent decision support system for management of petroleum-contaminated sites

Groundwater and soil contamination resulted from LNAPLs (light nonaqueous p hase liquids) spills and leakage in petroleum industry is currently one of the major environmental concerns in North America. Numerous site remediatio n technologies have been developed and implemented in the last two decades. They are classified as ex-situ and in-situ remediation techniques. One of the problems associated with ex-situ remediation is the cost of operation. In recent years, in-situ techniques have acquired popularity. However, the selection of the optimal techniques is difficult and insufficient expertise in the process may result in large inflation of expenses. This study prese nts an expert system (ES) for the management of petroleum contaminated site s in which a variety of artificial intelligence (AI) techniques were used t o construct a support tool for site remediation decision-making. This paper presents the knowledge engineering processes of knowledge acquisition, con ceptual design, and system implementation. The results from some case studi es indicate that the expert system can generate cost-effective remediation alternatives to assist decision-makers. (C) 2001 Elsevier Science Ltd. All rights reserved.