CAREER: Research and Educational Advances in Optimal Groundwater Remediation Design

Minsker 9734076本研究的目标是：1）开发解决现场规模地下水修复设计问题的改进方法; 2）提高从业人员和学生获得最佳地下水修复设计工具的机会; 3）使用主动学习技术来提高各级学生的教育水平; 4）教授研究生如何开发和应用耦合优化和仿真模型。 多尺度优化方法将开发，以提高计算效率的最优控制模型的好氧原位生物修复设计在以前的工作。这将使异构，现场规模的网站的准确建模。将开发一种混合遗传算法，以解决局部最优的困难，并以有效的方式考虑固定费用。 一旦多尺度方法和混合遗传算法的最佳计算效率已被确定，现场规模的原位生物修复设计的综合模型将开发，将多尺度方法到一个混合遗传算法。 本科研究助理将编程图形用户界面，以方便学生和行业从业者使用本研究中开发的设计模型。然后，这些模型将被用作教育游戏的核心，教导学生最佳设计工具的效用，以设计具有成本效益的补救解决方案。该游戏将在本科生和研究生课程中进行，并在推广项目中使用小型学生团队之间的竞赛形式。一个新的研究生课程耦合优化和模拟建模也将创建教学生的复杂性与开发和应用这些模型。该课程将作为展示研究成果和开发改进方法的论坛。 ***

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. ***