Robust Real-Time Optimization for Refinery Process Operations

炼油厂工艺操作的稳健实时优化

• 批准号: 555566-2020
• 负责人: Li, Zukui
• 金额: $ 1.52万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721372
• 关键词: Robust; Real; Time; Optimization; Refinery

The chemical process industry is facing challenges with the intensification of global market competition, more stringent limits in product specifications, pricing pressure and environmental problems. To address those challenges, many large manufacturers are starting to use data analysis to optimize plant operations, improve equipment efficiency and product quality, and reduce energy consumption. Machine learning techniques and new digital technology is driving the process industry companies to collect and analyze the growth of the amount of data. As an integrated energy company and Canada's largest refiner of petroleum products, Imperial Oil is facing challenges in improving overall profitability. One of those challenges is related to making robust process operations decisions under uncertainty in real-time. The present proposal aims to address the above challenge by developing a data-driven robust real-time optimization algorithm for online application in manufacturing facilities. Real-time optimization is the process of optimizing the plant operating conditions online and represents the first level in a typical control hierarchy where the economics of the plant is addressed explicitly. The proposed research project will use big data, machine learning, and robust optimization technology to meet the industry needs. The proposed project will help Imperial Oil optimize the utilization of resources through process systems engineering techniques, so as to improve profitability. Imperial Oil will implement the developed data driven optimization algorithm to its plethora of applications and benefit economically from it. The proposed research will not only benefit Imperial Oil but also benefit Canada through technology transfer to other Canadian manufacturing companies, and through contribution to training HQPs with technique backgrounds that are needed by the oil and gas industry.

随着全球市场竞争的加剧、产品规格限制的日益严格、价格压力和环境问题的加剧，化工过程行业面临着挑战。为了应对这些挑战，许多大型制造商开始使用数据分析来优化工厂运营，提高设备效率和产品质量，并降低能源消耗。机器学习技术和新的数字技术正在推动流程工业企业收集和分析增长的数据量。作为一家综合性能源公司和加拿大最大的石油产品炼油商，帝国石油在提高整体盈利能力方面面临挑战。其中一个挑战是在不确定的情况下实时做出稳健的过程操作决策。本提案旨在通过开发一种数据驱动的稳健实时优化算法来解决上述挑战，该算法适用于制造设施的在线应用。实时优化是在线优化工厂运行条件的过程，代表了典型控制体系中的第一级，其中明确阐述了工厂的经济性。拟议的研究项目将使用大数据、机器学习和稳健优化技术来满足行业需求。拟议中的项目将通过过程系统工程技术帮助帝国石油优化资源利用，从而提高盈利能力。帝国石油将实施开发的数据驱动优化算法，以满足其大量应用，并从经济上受益。拟议的研究不仅将使帝国石油公司受益，还将通过向其他加拿大制造公司转让技术，以及通过为培训具有石油和天然气行业所需技术背景的HQP做出贡献，使加拿大受益。