Minsker 9734076 The objectives of this research are to: 1) develop improved methods for solving field-scale groundwater remediation design problems; 2) improve access to optimal groundwater remediation design tools by practitioners and students; 3) use active learning techniques to improve education of students at all levels; and 4) teach graduate students how to develop and apply coupled optimization and simulation models. Multiscale optimization methods will be developed to improve the computational efficiency of an optimal control model for aerobic in situ bioremediation design developed in previous work. This will enable accurate modeling of heterogeneous, field-scale sites. A hybrid genetic algorithm will be developed to address difficulties with local optima and to allow fixed costs to be considered in an efficient manner. Once the multiscale method and hybrid genetic algorithm with the best computational efficiency have been identified, an integrated model for field-scale in situ bioremediation design will be developed that will incorporate multiscale methods into a hybrid genetic algorithm. Undergraduate research assistants will program graphical user interfaces to facilitate use of the design models developed in this research by students and practitioners from industry. The models will then be used as the core for an educational game to teach students the utility of optimal design tools for designing cost-effective remediation solutions. The game will be played in undergraduate and graduate courses and in outreach programs using a contest format between small student teams. A new graduate course on coupled optimization and simulation modeling will also be created to teach students the complexities associated with developing and applying such models. The course will serve as a forum for presenting research results and developing improved methods. ***
