GOALI: Integrated Design and Operability Optimization of Industrial-Scale Modular Intensified Systems

GOALI：工业规模模块化强化系统的集成设计和可操作性优化

• 批准号: 2401564
• 负责人: Efstratios Pistikopoulos
• 金额: $ 40.03万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2401564&HistoricalAwards=false
• 关键词: GOALI; Integrated; Design; Operability; Optimization

Modular chemical process intensification (MCPI) offers the potential to achieve step-change improvements in cost, energy, and sustainability by developing innovative equipment and processing schemes. However, the commercial applications of such process technologies remain limited due to key barriers in design complexity, flowsheet integration, and operation under uncertainty. This project aims to develop a computer-aided strategy to augment process intensification synthesis, operability optimization, and modularization clustering. The proposed approaches will be the first of their kind to systematically identify the optimal selection and integration of modular and/or intensified process units in grassroots design or retrofit operations, which currently rely on human engineering experience. Of particular interest to this study are plant-scale bulk chemical production processes, which are among the largest energy users and carbon emitters in the domestic industrial sector. The industry-university project team with researchers from Dow Chemical Company, Texas A&M University, and West Virginia University is uniquely positioned to accelerate MCPI in industrial practice through this GOALI project. The methodological developments will be demonstrated in industrially relevant case studies and compared to state-of-the-art patented processes. The project findings will be incorporated into online learning modules and hands-on workshops to disseminate the methods and tools to the industrial community in a timely manner. The project team also will jointly train next-generation MCPI engineering leaders via academic and industrial research opportunities chosen from a diverse group of undergraduate and graduate students. This project will develop advanced computational methods and a systematic framework to design optimal, intensified, and highly operable bulk chemical processes based on modular process intensification principles. The framework centers on a phenomena-based representation which employs general thermodynamic-based driving force constraints to quantitatively identify the optimal modular intensification opportunities at the systems level (e.g., mass/heat transfer enhancement, multi-functional task integration), while creating the opportunity to discover innovative unit and flowsheet designs that may be new to current industrial practice. The research also will generate a fundamental understanding of the impact of modular intensification on operability under uncertainty. The resulting methodology will deliver optimal and operable modular/intensified process designs by systematically addressing the interactions and trade-offs of process efficiency, economics, and operability. Key pillars of the research plan feature: (i) phenomena-based process synthesis synergizing physical laws, mathematical optimization, and machine learning to efficiently search the combinatorial design space, (ii) integrated synthesis with data-driven flexibility and controllability to generate optimal modular chemical process intensified (MCPI) designs with guaranteed operability performance, and (iii) a similarity-based clustering algorithm to automate the translation of phenomena-based solutions to unit operation-based flowsheets. The methodological developments will be demonstrated on industrially relevant case studies including ethylene glycol and methyl methacrylate production. The resulting methods, software, and industrial case studies will produce design tools and concrete examples of their benefits, improving existing processes with a win-win combination of economic, energy, and sustainability through MCPI design principles.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

模块化化学过程强化（MCPI）通过制定创新设备和处理方案，提供了实现成本，能源和可持续性逐步改善的潜力。但是，由于设计复杂性，流程表集成和不确定性下的操作，此类过程技术的商业应用仍然限制。该项目旨在制定计算机辅助策略，以增强过程强化综合，可操作性优化和模块化聚类。所提出的方法将是系统地确定基层设计或改造操作中模块化和/或加强过程单元的最佳选择和集成，这些方法目前依赖于人类工程经验。这项研究特别感兴趣的是植物规模的散装化学生产过程，这些过程是国内工业部门最大的能源用户和碳发射器之一。由DOW化学公司，得克萨斯州农工大学和西弗吉尼亚大学的研究人员组成的行业大学项目团队，可以通过这个守门员项目在工业实践中加速MCPI。方法学的发展将在工业相关的案例研究中得到证明，并将其与最先进的专利过程进行比较。该项目的发现将纳入在线学习模块和实践研讨会中，以及时将方法和工具传播给工业社区。项目团队还将通过从多元化的本科生和研究生中选择的学术和工业研究机会共同培训下一代MCPI工程领导者。该项目将开发高级计算方法和系统框架，以根据模块化过程强化原理设计最佳，加强且高度可操作的散装化学过程。该框架集中在基于现象的代表上，该表示采用一般基于热力学的驱动力限制来定量确定系统级别（例如，质量/热传递增强，多功能任务集成）的最佳模块化强度机会，同时创造了发现创新单位和流动性单位设计的机会，这些单位和流动性设计可能是当前工业实践的新功能。这项研究还将对模块化强化对不确定性操作性的影响产生基本理解。最终的方法将通过系统地解决过程效率，经济性和可操作性的交互和权衡，来提供最佳和可操作的模块化/加强过程设计。研究计划的关键支柱特征：（i）基于现象的过程综合，辅助物理定律，数学优化和机器学习，以有效地搜索组合设计空间，（ii）与数据驱动的灵活性和可控性的集成合成，以生成最佳的模块化化学过程（MCPI），以确保基于自动的操作性（MCPI），以确保运算性能（III）（III II）（III II），并激发（IIIII），并激发（IIIII）。基于现象的基于单位操作的流程表的解决方案。方法论发展将在工业相关的案例研究中证明，包括乙二醇和甲基丙烯酸甲酯的产生。由此产生的方法，软件和工业案例研究将通过MCPI设计原则通过双赢的经济，能源和可持续性来改善现有流程的设计工具和具体示例。该奖项反映了NSF的法定任务，并通过该基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被视为值得通过评估的支持。