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，提供了实现成本、能源和可持续性的阶段性改进的潜力。然而，由于设计复杂性、流程集成和不确定操作方面的关键障碍，此类工艺技术的商业应用仍然有限。该项目旨在开发一种计算机辅助策略，以增强流程集约化综合、可操作性优化和模块化集群。拟议的办法将是第一次系统地确定在基层设计或改装作业中最佳选择和整合模块和/或强化工艺单元的方法，目前这些设计或改造作业依赖于人类工程经验。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，这些过程是国内工业部门中最大的能源使用者和碳排放者之一。由陶氏化学公司、德克萨斯A&M大学和西弗吉尼亚大学的研究人员组成的行业-大学项目团队处于独特的地位，可以通过这个GALI项目在工业实践中加速MCPI。这些方法的发展将在与工业相关的案例研究中得到展示，并与最先进的专利工艺进行比较。项目成果将纳入在线学习单元和实践讲习班，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挑选学术和工业研究机会，共同培养下一代MCPI工程领导者。该项目将开发先进的计算方法和系统框架，以基于模块化过程强化原则设计优化、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，使用基于热力学的一般驱动力约束来定量地确定系统级别的最佳模块强化机会(例如，质量/热传递增强、多功能任务集成)，同时创造机会来发现创新的单元和流程图设计，这些设计对于当前的工业实践可能是新的。这项研究还将从根本上理解模块化集约化对不确定性下的可操作性的影响。由此产生的方法将通过系统地解决工艺效率、经济性和可操作性之间的相互作用和权衡，来提供最佳和可操作的模块化/强化工艺设计。研究计划的主要特点是：(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，以高效搜索组合设计空间；(Ii)与数据驱动的灵活性和可控性相结合的综合，以生成具有保证操作性能的最佳模块化化工过程强化(MCPI)设计；以及(Iii)基于相似性的聚类算法，以自动将基于现象的解决方案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与工业有关的案例研究中得到展示。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收益实例，通过MCPI设计原则通过经济、能源和可持续发展的双赢组合来改进现有流程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.