Advanced Multiscale Decision-making for Process Operations and Energy Systems, and Incorporating Big Data Analytics

针对流程操作和能源系统的高级多尺度决策，并结合大数据分析

• 批准号: RGPIN-2018-04108
• 负责人: Elkamel, Ali
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=687273
• 关键词: Advanced; Multiscale; Decision; making; Process

Across all sectors within the energy and process industry, tremendous efforts have been devoted towards the development and operation of agile manufacturing techniques to respond to customer needs and volatile markets while at the same time control costs, improve efficiency, and reduce pollution. This has created a demand for systems to solve complex integrated planning and scheduling problems that bridge the gap between the different functional and strategic decision-making levels. These challenging multiscale decision problems require more efficient computational tools and solution strategies. The goal of this research program is to develop new techniques aimed to obtain more competitive integrated solutions in a timely manner. Although progress (including past research efforts by the principal investigator) has been made in dealing with some of the combinatorial aspects of the problems cited above, significant research efforts are still required to overcome these multiscale challenges. This research program aims at devising improved solution algorithms that integrate logic-based methods, decomposition techniques, stochastic optimization, and reformulation strategies. Since most of the existing methodologies are problem-specific or applicable only to short time horizons, clustering will be investigated as a potential tool to effectively shrink model size and achieve computational tractability while at the same time maintaining solution accuracy. Other approaches that this research program will consider is the use of continuous time scales, rolling horizon strategies, and surrogate models that employ big data analytics.******Two broad application domains will be considered: one focuses on process operations and enterprise-wide integrated facilities and the other deals with advanced energy production systems. The challenges of intermittence of renewable energy will be addressed through several grid stabilization approaches, including energy storage, electrification of the process industry, and conversion of surplus electricity to useful products through Power-to-X technologies. Applications of the developed computational tools include making design or operating decisions, selection of system components, interconnections of units, or even interactions along supply chains or the ecosystem. The program will provide training for several graduate and undergraduate students, exposing them to advanced techniques in the practical solution of large multiscale decision problems, and has the potential to significantly impact tomorrow's process and energy industry with tremendous economic benefits to Canada. Students involved in the research program will be well-suited for the many positions that are currently available in the area of process systems engineering in both industry and academia.*****

在能源和加工行业的所有部门，人们都在努力开发和运营敏捷制造技术，以响应客户需求和市场波动，同时控制成本，提高效率并减少污染。这就产生了对系统的需求，以解决复杂的综合规划和调度问题，弥合不同的功能和战略决策水平之间的差距。这些具有挑战性的多尺度决策问题需要更有效的计算工具和解决策略。该研究计划的目标是开发旨在及时获得更具竞争力的集成解决方案的新技术。虽然在处理上述问题的一些组合方面取得了进展（包括主要研究者过去的研究工作），但仍需要大量的研究工作来克服这些多尺度挑战。该研究计划旨在设计改进的解决方案算法，集成基于逻辑的方法，分解技术，随机优化和重构策略。由于大多数现有的方法是特定于问题或仅适用于短时间范围，聚类将被调查作为一个潜在的工具，以有效地缩小模型的大小，并实现计算的易处理性，同时保持解决方案的准确性。本研究计划将考虑的其他方法是使用连续时间尺度，滚动范围策略和采用大数据分析的代理模型。将考虑两个广泛的应用领域：一个侧重于过程操作和企业范围的综合设施，另一个涉及先进的能源生产系统。将通过几种电网稳定方法来解决可再生能源的挑战，包括能源储存、加工工业电气化以及通过Power-to-X技术将剩余电力转化为有用的产品。 所开发的计算工具的应用包括设计或操作决策、系统组件的选择、单元的互连，甚至是沿着供应链或生态系统的交互。该计划将为一些研究生和本科生提供培训，使他们接触到大型多尺度决策问题实际解决方案的先进技术，并有可能对明天的过程和能源行业产生重大影响，为加拿大带来巨大的经济效益。参与研究计划的学生将非常适合目前在工业和学术界过程系统工程领域提供的许多职位。