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

模块化化工过程强化（MCPI）通过开发创新的设备和加工方案，提供了在成本、能源和可持续性方面实现阶跃式改进的潜力。然而，这种工艺技术的商业应用仍然受到限制，这是由于设计复杂性、流程集成和不确定性下的操作的关键障碍。本计画旨在发展一电脑辅助策略，以加强制程强化合成、可操作性最佳化及模组化丛集。所提出的方法将是第一个系统地确定基层设计或改造操作中模块化和/或强化过程单元的最佳选择和集成的方法，目前依赖于人类工程学经验。这项研究特别感兴趣的是工厂规模的大宗化学品生产过程，这是国内工业部门最大的能源用户和碳排放者之一。由陶氏化学公司、德克萨斯州农工大学和西弗吉尼亚大学的研究人员组成的产学研项目团队具有独特的优势，可以通过这个GOALI项目加速工业实践中的MCPI。方法的发展将在工业相关的案例研究中得到证明，并与最先进的专利工艺进行比较。该项目的研究结果将纳入在线学习模块和实践讲习班，以便及时向工业界传播方法和工具。该项目团队还将通过从不同的本科生和研究生群体中选择的学术和工业研究机会，联合培训下一代MCPI工程领导者。该项目将开发先进的计算方法和系统框架，以设计基于模块化过程强化原理的优化，强化和高度可操作的散装化学过程。该框架以基于现象的表示为中心，该表示采用基于通用驱动力约束的驱动力约束来定量识别系统级别的最佳模块化集约化机会（例如，质量/热传递增强、多功能任务集成），同时创造机会发现对当前工业实践来说可能是新的创新单元和流程图设计。该研究还将对模块化强化对不确定性下可操作性的影响产生根本性的理解。由此产生的方法将提供最佳的和可操作的模块化/强化工艺设计，系统地解决过程效率，经济性和可操作性的相互作用和权衡。研究计划的主要支柱功能：（i）基于现象的过程综合，协同物理定律、数学优化和机器学习，以有效地搜索组合设计空间，（ii）具有数据驱动的灵活性和可控性的集成综合，以生成具有保证的可操作性能的最佳模块化化学过程强化（MCPI）设计，以及（iii）基于相似性的聚类算法，用于将基于现象的解决方案自动转换为基于单元操作的流程图。方法学的发展将在包括乙二醇和甲基丙烯酸甲酯生产在内的工业相关案例研究中得到证明。由此产生的方法，软件和工业案例研究将产生设计工具和具体的例子，他们的好处，改善现有的过程与经济，能源和可持续性的双赢组合通过MCPI设计原则。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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.