Advanced Computational Models for Multistage Stochastic Optimization of Process Systems with Renewable Resources

可再生资源过程系统多级随机优化的高级计算模型

• 批准号: 0521769
• 负责人: Ignacio Grossmann
• 金额: $ 27.2万
• 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: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0521769&HistoricalAwards=false
• 关键词: Advanced; Computational; Models; Multistage; Stochastic

ABSTRACTPI: Ignacio E. Grossmann Institution: Carnegie Mellon UniversityProposal Number: 0521769Title: Advanced Computational Models for Multistage Stochastic Optimization of Process Systems with Renewable ResourcesHandling uncertainties in the design of process systems under multiperiod operation is becoming an increasingly important issue, particularly when dealing with systems such as bioprocesses where there are significant uncertainties in the availability and quality of the feedstocks and in the yields of the process units. A major objective of this project is to develop novel computational models for the stochastic optimization of process systems that involve exogenous and endogenous uncertainties. Examples of the former include demands or feed compositions, whereas examples of the latter include yields or other process parameters. The specific goal of the research is to develop novel models and effective solution methods for multistage stochastic optimization where the structure of scenario trees are functions of design decisions given endogenous uncertainties. To overcome the computationally challenging computations the PI intends to investigate a novel disjunctive programming formulation that expresses in closed form the dependency of the scenario tree with the design decisions. Based on that model, he intends to investigate a computational procedure based on a Lagrangean branch and cut method for solving linear stochastic problems. The method will rely on the use of grid computing using master-worker algorithms to exploit the subproblems that can be solved independently as part of the decomposition. The extension of this computational method to bilinear models will also be investigated.This computational technique will be applied to two problems. The first one deals with the design of biorefineries (biomass conversion systems) in which there are uncertainties in the availability and quality of food residues (raw materials) and in the yields of conversion in the various processes that are involved. The second application deals with the synthesis of integrated process water systems in which there are uncertainties in the concentration of contaminants and in the recoveries of treatment units. The first application will be modeled as a linear stochastic programming problem, while the second one is a nonlinear stochastic problem that involves bilinearities.Broader impact:This research has the potential not only of expanding the scope and significance of stochastic optimization, but also for greatly improving the design of bioprocesses and process water systems. The research results and computational tools will be made available through the internet. The PI also intends to develop two design case studies that will be disseminated to process design instructors through CACHE. He believes that these case studies will have significant impact in undergraduate education as they will expose the students to biorefineries, process water systems and techniques for handling uncertainties. Finally, in order to promote interest in high schools in applied mathematics and processes based on renewable resources, he plans to perform outreach activities through the Steinbrenner Institute for Environmental Education at Carnegie Mellon where students can be exposed to simplified versions of the case studies.

摘要PI：Ignacio E. Grossmann机构：卡内基梅隆大学港口编号：0521769TITLE：用于多通道随机性优化过程系统的高级计算模型，具有可再生资源的不可再生能源的不确定性在过程中，在多种多样操作下的设计中，尤其是在越来越多的重要问题时，例如，生物处理在原料的可用性和质量以及工艺单元的产量中存在明显的不确定性。 该项目的一个主要目的是开发用于涉及外源和内源性不确定性的过程系统随机优化的新型计算模型。前者的例子包括需求或饲料组成，而后者的示例包括收率或其他过程参数。该研究的具体目标是开发新型模型和有效的解决方案方法，用于多阶段随机优化，其中场景树的结构是内源性不确定性的设计决策的函数。为了克服计算具有挑战性的计算，PI打算研究一种新型的脱节编程公式，该公式以封闭形式表达了场景树的依赖性，并具有设计决策。基于该模型，他打算根据Lagrangean分支和解决线性随机问题的剪切方法研究计算程序。 该方法将依赖于使用Master-Worker算法使用网格计算来利用可以独立解决作为分解的一部分的子问题。 该计算方法向双线性模型的扩展也将进行研究。该计算技术将应用于两个问题。第一个涉及生物精制（生物量转换系统）的设计，其中食品残留物的可用性和质量（原材料）（原材料）以及所涉及的各种过程中的转换产量。第二个应用涉及综合工艺水系统的综合，其中污染物浓度不确定和治疗单位的回收率中存在不确定性。第一个应用程序将被建模为线性随机编程问题，而第二个应用程序是涉及双线性的非线性随机问题。Broader的影响：这项研究不仅有可能扩大随机优化的范围和意义，而且还具有极大的影响改善生物处理和工艺水系统的设计。研究结果和计算工具将通过互联网提供。 PI还打算开发两个设计案例研究，这些案例研究将通过缓存进行处理以处理设计讲师。他认为，这些案例研究将对本科教育产生重大影响，因为他们将使学生接触生物工会，处理水系统和处理不确定性的技术。最后，为了基于可再生资源的数学和流程对高中的兴趣，他计划通过Carnegie Mellon的Steinbrenner环境教育研究所进行外展活动，在那里可以将学生暴露于案例研究的简化版本中。