Simulation‐based optimization of dual‐phase vacuum extraction to remove nonaqueous phase liquids in subsurface
Journal Articles
Overview
Research
Identity
Additional Document Info
View All
Overview
abstract
An integrated simulation‐optimization system was developed for supporting decisions of the dual phase vacuum extraction (DPVE) processes. The system coupled a DPVE process simulator, a multivariate regression tool and a nonlinear optimization model into a general framework. A stepwise‐cluster‐analysis technique was provided for establishing a DPVE process forecasting system for describing the relationships between remediation actions and system responses (i.e., total extracted volume of oil/water, elevation distribution of water table, and specific volume of oil). The forecasting system was then embedded into a multiobjective optimization framework, where the objectives were to minimize the operation cost and maximize the remediation efficiency. The constraints include environmental, hydraulic and technical restrictions to the DPVE processes. A case study was conducted for a petroleum‐contaminated site in western Canada. The results from the stepwise cluster analysis indicated that the generated cluster trees could be used for predicting system responses of the DPVE process, given inputs of the operating conditions. The prediction accuracies of the generated cluster trees were verified using randomly generated data sets. The optimum operating conditions could vary significantly under different cost‐efficiency targets. When a stricter environmental target (i.e., the amount of pollutants in subsurface) was concerned, a higher system cost had to be paid; when the cost became a critical factor, the performance of contaminant removal would have to be compromised. The developed system could be used to analyze tradeoffs between system cost and process efficiency in the DPVE operations; it could also support the formulation of an on‐site process‐control system with vacuum levels and extraction rates being the main control variables.
