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互联的社会中，公司需要在接近最佳的条件下运营，其中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长以及减少二氧化碳排放的迫切需要，化石燃料发电系统需要满足严格的安全、环境和经济约束。然而，在外部因素发生变化的情况下，保持可持续、灵活的运营面临着挑战。例如，发电厂正在努力适应清洁可再生能源转换系统即将渗透到市场的情况。因此，人们有很大的动力去改进现有发电厂的设计和运营管理，同时开发新的清洁、负担得起和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，可用于制造和能源领域新兴的化学系统的优化设计和运营管理，这两个领域对加拿大具有战略重要性。研究表明，当调度决策与流程设计和控制决策相结合时，可以提高系统的经济性和可持续性。由于计算成本巨大，目前可用于优化这三个因素的方法很少，无法在调度决策真正关键的大规模应用中实现。该计划将推进一种新方法的开发，该方法使用最先进的离散化方案，在不确定性下解决大规模应用的优化设计、控制和调度问题。还将开发基于新采样方法的新颖不确定性量化方法，即多级蒙特卡罗（以前从未考虑过过程优化）。与此同时，该计划还将推进化学循环燃烧（CLC），这是一种新兴的碳捕获和储存（CCS）技术，有可能取代传统（能源密集型）CCS系统。具体来说，我们将为加压条件下的中试规模 CLC 应用开发一种新的动态模型。该模型将有助于使用上述先进计算工具进行优化设计和运营管理研究，以实现优化设计、控制和调度。此外，加压 CLC 系统的性能将与其他 CLC 应用进行比较，以评估二氧化碳捕获成本、工厂效率、工艺规模扩大以及最终其与先进发电系统集成的潜力。最终，这项研究将产生先进的算法框架，用于制造和能源领域新兴的大型系统的优化设计和运营管理，并为揭示 CLC 取代现有 CCS 技术的潜力铺平道路。