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Data Analysis and Modeling to Optimize Thermal Treatment Cost and Performance
Author(s) -
Parker Jack C.,
Kim Ungtae,
Fortune Alyson,
Griepke Steffen,
Galligan James P.,
Bonarrigo Amber
Publication year - 2017
Publication title -
groundwater monitoring and remediation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.677
H-Index - 47
eISSN - 1745-6592
pISSN - 1069-3629
DOI - 10.1111/gwmr.12199
Subject(s) - environmental remediation , environmental science , computer science , sample (material) , volume (thermodynamics) , statistics , thermal , mathematical optimization , soil science , reliability engineering , mathematics , contamination , engineering , chemistry , ecology , physics , chromatography , quantum mechanics , meteorology , biology
The objective of in situ thermal treatment is typically to reduce the contaminant mass or average soil concentration below a specified value. Evaluation of whether the objective has been met is usually made by averaging soil concentrations from a limited number of soil samples. Results from several field sites indicate large performance uncertainty using this approach, even when the number of samples is large. We propose a method to estimate average soil concentration by fitting a log normal probability model to thermal mass recovery data. A statistical approach is presented for making termination decisions from mass recovery data, soil sample data, or both for an entire treatment volume or for subregions that explicitly considers estimation uncertainty which is coupled to a stochastic optimization algorithm to identify monitoring strategies to meet objectives with minimum expected cost. Early termination of heating in regions that reach cleanup targets sooner enables operating costs to be reduced while ensuring a high likelihood of meeting remediation objectives. Results for an example problem demonstrate that significant performance improvement and cost reductions can be achieved using this approach.