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- Deschaine, Larry M.
(author)
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Decision support for complex planning challenges - Combining expert systems, engineering-oriented modeling, machine learning, information theory, and optimization technology
- 2014
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Doctoral thesis (other academic/artistic)abstract
- This thesis develops an approach for addressing complex industrial planning challenges. The approach provides advice to select and blend modeling techniques that produce implementable optimal solutions. Industrial applications demonstrate its effectiveness. Industries have a need for advanced analytic techniques that encompass and reconcile the full range of information available regarding a planning problem. The goal is to craft the best possible decision in the time allotted. The pertinent information can include subject matter expertise, physical processes simulated in models, and observational data. The approach described in this paper assesses the decision challenge in two ways: first according to the available knowledge profile which includes the type, amount, and quality of information available of the problem; and second, according to the analysis and decision-support techniques most appropriate to each profile. We use model-mixing techniques such as machine learning and Kalman Filtering to combine analysis methods from various disciplines that include expert systems, engineering-oriented numerical and symbolic modeling, and machine learning in a graded, principled manner. A suite of global and local optimization methods handle the range of optimization tasks arising in the demonstrated engineering projects. The methods used include the global and local nonlinear optimization algorithms. The thesis consists of four appended papers. Paper I uses subject matter expertise modelling to provide decision analysis regarding the environmental issue of mercury retirement. Paper II provides the framework for developing optimal remediation designs for subsurface groundwater monitoring and contamination mitigation using numerical models based on physical understanding. Paper III provides the results of a machine learning study using the Compiling Genetic Programming System (CGPS) on multiple industrial data sets. This study resulted in a breakthrough for identifying underground unexploded ordnance (UXO) and munitions and explosives of concern (MEC) from inert buried objects. Paper IV develops and uses the model mixing and optimization approach to expound on understanding the MEC identification technique. It uses the methods in the first three papers along with additional technology. Each thesis paper includes complimentary citations and web links to selected publications that further demonstrate the value of this approach; either via industrial application or inclusion in US government guidance documents.
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| 2. |
- Deschaine, Larry M., et al.
(author)
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Groundwater remediation design using physics-based flow, transport, and optimization technologies
- 2013
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In: Environmental Systems Research. - : Springer Science and Business Media LLC. - 2193-2697. ; 2
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Journal article (peer-reviewed)abstract
- BackgroundThe purpose of this work was to demonstrate an approach to groundwater remedial design that is automated, cost-effective, and broadly applicable to contaminated aquifers in different geologic settings. The approach integrates modeling and optimization for use as a decision support framework for the optimal design of groundwater remediation systems employing pump and treat and re-injection technologies. The technology resulting from the implementation of the methodology, which we call Physics-Based Management Optimization (PBMO), integrates physics-based groundwater flow and transport models, management science, and nonlinear optimization tools to provide stakeholders with practical, optimized well placement locations and flow rates for remediating contaminated groundwater at complex sites.ResultsThe algorithm implementation, verification, and effectiveness testing was conducted using groundwater conditions at the Umatilla Chemical Depot in Umatilla, Oregon, as a case study. This site was the subject of a government-sponsored remedial optimization study. Our methodology identified the optimal solution 40 times faster than other methods, did not fail to perform when the physics-based models failed to converge, and did not require human intervention during the solution search, in contrast to the other methods. The integration of the PBMO and Lipschitz Global Optimization (LGO) methods with standalone physically based models provides an approach that is applicable to a wide range of hydrogeological flow and transport settings.ConclusionsThe global optimization based solutions obtained from this study were similar to those found by others, providing method verification. Automation of the optimal search strategy combined with the reliability to overcome inherent difficulties of non-convergence when using physics models in optimization promotes its usefulness. The application of our methodology to the Umatilla case study site represents a rigorous testing of our optimization methodology for handling groundwater remediation problems.
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