Algorithmic Advances for Large-Scale Dynamic Process Optimization

大规模动态过程优化的算法进步

• 批准号: 0314647
• 负责人: Lorenz Biegler
• 金额: $ 30.39万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0314647&HistoricalAwards=false
• 关键词: Algorithmic; Advances; Large; Scale; Dynamic

Research:Dynamic optimization is an essential component of many process design activities. In previous work the PI developed an efficient simultaneous approach for dynamic optimization that handles both unstable and path-constrained differential-algebraic systems. He developed an improved nonlinear programming strategy based on interior point or barrier methods, which leads to significant gains in performance. For example, on his dynamic process optimization case, nonlinear programs with over 1.2 million variables can be solved in approximately two hours of CPU time on a 1.0 GHz computer. In this project he will extend his simultaneous optimization strategies to large systems. To do this, he will consider several improvements in the formulation and algorithm for dynamic optimization problems as well as extending the scope of the dynamic optimization problems to include discrete decisions that involve phase equilibriums and other complementary relationships. Because these features are exploited in a novel way by the interior point algorithm, difficult optimization problems that include a class of discrete decisions can be treated with little additional computational cost.He plans to incorporate these into a modeling framework that will combine well-developed user interfaces and object-oriented software as well as demonstrate the application of the algorithms within a state-of-the-art parallel computing environment on a wide variety of process optimization problems drawn from process control, operations and design.Broader Impact:The Chemical Process Industries (CPI) could greatly benefit from the development of more efficient dynamic optimization methodologies.

研究：动态优化是许多过程设计活动的重要组成部分。 在先前的工作中，PI开发了一种有效的同时方法进行动态优化，该方法可以处理不稳定和路径约束的差分 - 代数系统。 他基于内部或障碍方法制定了改进的非线性编程策略，从而导致绩效的显着提高。 例如，在他的动态过程优化案例中，可以在1.0 GHz计算机上大约两个小时的CPU时间求解超过120万个变量的非线性程序。 在这个项目中，他将把他的同时优化策略扩展到大型系统。 为此，他将考虑动态优化问题的公式和算法的几个改进，并扩大动态优化问题的范围，以包括涉及相位平衡和其他互补关系的离散决策。 由于这些功能是通过内点算法以新颖方式利用的，因此可以将包括一类离散决策的困难优化问题与几乎没有其他计算成本进行处理。控制，操作和设计。BRODER的影响：化学过程行业（CPI）可以从更有效的动态优化方法的发展中受益匪浅。