Optimal design, scheduling and control of chemical processes and emerging energy systems for sustainable and flexible operations under uncertainty

化学过程和新兴能源系统的优化设计、调度和控制，以实现不确定性下的可持续和灵活运行

• 批准号: RGPIN-2018-03812
• 负责人: RicardezSandoval, Luis
• 金额: $ 2.84万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=747185
• 关键词: Optimal; design; scheduling; control; chemical

In today's very competitive and highly interconnected society, companies are required to operate at near-optimal conditions, where operations management is driven primarily by external factors and system uncertainty. With the continuous rise in energy demands, and the urgent need to reduce CO2 emissions, fossil-fired power systems are required to meet tight safety, environmental and economic constraints. However, there are challenges to maintaining sustainable and flexible operation in the presence of changes in external factors. Power plants, for example, struggle to adapt to imminent penetration of clean renewable energy conversion systems in the market. Thus, there is great incentive to improve the design and operations management in existing power plants and, in parallel, develop new clean, affordable and commercially-viable fossil fuel systems. This research program aims to develop novel theoretical and computational tools that can be used for optimal design and operations management in chemical systems emerging in the manufacturing and energy sectors, both areas of strategic importance to Canada. Studies have shown that scheduling decisions can improve system economy and sustainability when they are combined with process design and control decisions. Due to their large computational costs, the very few methods currently available to optimize these three factors cannot be implemented in large-scale applications where scheduling decisions are truly key. This program will advance the development of a new method that addresses optimal design, control and scheduling for large-scale applications under uncertainty using state-of-the-art discretization schemes. Novel uncertainty quantification methods based on an new sampling method, i.e. Multi-level Monte Carlo (never before considered for process optimization), will also be developed. In parallel, this program will also advance chemical looping combustion (CLC), an emerging carbon capture and storage (CCS) technology with the potential to replace conventional (energy intensive) CCS systems. Specifically, we will develop a new dynamic model for a pilot-scale CLC application at pressurized conditions. This model will be instrumental to conduct optimal design and operations management studies using the advanced computational tools described above for optimal design, control and scheduling. Also, the performance of the pressurized CLC system will be compared to other CLC applications to evaluate CO2 capture costs, plant's efficiency, process scale-up and eventually, their potential integration to advanced power generation systems. Ultimately, this research will produce advanced algorithmic frameworks for optimal design and operations management in large-scale systems emerging in the manufacturing and energy sectors and pave the way to reveal the potential of CLC to replace existing CCS technologies.

在当今竞争激烈且高度相互联系的社会中，公司必须在近距离的条件下运营，在这种情况下，运营管理主要由外部因素和系统不确定性驱动。随着能源需求的不断增加，迫切需要减少二氧化碳排放，因此需要化石燃烧的电力系统才能满足严格的安全性，环境和经济限制。但是，在外部因素发生变化的情况下，保持可持续和灵活的操作面临挑战。例如，发电厂很难适应市场上清洁可再生能源转换系统的即将渗透。因此，有很大的动力来改善现有发电厂的设计和运营管理，并同时开发新的清洁，负担得起且在商业上可行的化石燃料系统。该研究计划旨在开发新颖的理论和计算工具，这些工具可用于制造和能源领域的化学系统中的最佳设计和操作管理，这是对加拿大的战略重要性。研究表明，计划决策与过程设计和控制决策相结合时可以改善系统经济和可持续性。由于其巨大的计算成本，目前几乎没有用于优化这三个因素的方法无法在调度决策确实是关键的大规模应用程序中实现。该程序将推动开发一种新方法，该方法使用最新的离散化方案解决了在不确定的大规模应用程序中的最佳设计，控制和计划。还将开发基于新的抽样方法的新型不确定性定量方法，即多级蒙特卡洛（从未考虑过进行过程优化）。同时，该程序还将推进化学循环燃烧（CLC），这是一种新兴的碳捕获和存储（CCS）技术，有可能取代常规（能源密集型）CCS系统。具体而言，我们将在加压条件下为试验尺度的CLC应用开发一个新的动态模型。该模型将使用上述高级计算工具进行最佳设计，控制和调度。此外，将加压CLC系统的性能与其他CLC应用程序进行比较，以评估CO2捕获成本，植物的效率，过程扩大，并最终将其潜在的集成到高级发电系统中。最终，这项研究将生成高级算法框架，以在制造和能源领域出现的大规模系统中的最佳设计和运营管理，并为揭示CLC替代现有CCS技术的潜力铺平了道路。