Robust Real-Time Optimization for Refinery Process Operations
Robust Real-Time Optimization for Refinery Process Operations
批准号:
555566-2020
负责人:
Li, Zukui
金额:
$1.52万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31
中文摘要
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日益突出,化工过程行业正面临着挑战。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,并降低能耗。机器学习技术和新的数字技术正在推动过程工业公司收集和分析数据量的增长。作为一家综合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。这些挑战之一是在实时不确定性下做出稳健的过程操作决策。本提案旨在通过开发用于制造设施中的在线应用的数据驱动的鲁棒实时优化算法来解决上述挑战。实时优化是在线优化工厂操作条件的过程,并且代表了典型控制层次结构中的第一级,其中工厂的经济性被明确地解决。拟议的研究项目将使用大数据、机器学习和鲁棒优化技术来满足行业需求。拟议的项目将帮助帝国石油通过过程系统工程技术优化资源利用,以提高盈利能力。帝国石油公司将实施开发的数据驱动优化算法,以其大量的应用程序,并从中获得经济效益。拟议的研究不仅有利于帝国石油公司,而且有利于加拿大通过技术转让给其他加拿大制造公司,并通过贡献培训HQP的技术背景,需要的石油和天然气行业。
英文摘要
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.
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会议论文
Data-Driven Process Systems Optimization under Uncertain Environment
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批准号:RGPIN-2019-04584
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项目类别:Discovery Grants Program - Individual
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资助金额:$2.4万
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财政年份:2022
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负责人:Li, Zukui
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依托单位:
Data-Driven Process Systems Optimization under Uncertain Environment
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批准号:RGPIN-2019-04584
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项目类别:Discovery Grants Program - Individual
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资助金额:$2.4万
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财政年份:2021
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负责人:Li, Zukui
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依托单位:
Robust Real-Time Optimization for Refinery Process Operations
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批准号:555566-2020
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项目类别:Alliance Grants
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资助金额:$1.52万
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财政年份:2021
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负责人:Li, Zukui
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依托单位:
Data-Driven Process Systems Optimization under Uncertain Environment
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批准号:RGPIN-2019-04584
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项目类别:Discovery Grants Program - Individual
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资助金额:$2.4万
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财政年份:2020
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负责人:Li, Zukui
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财政年份:2019
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负责人:Li, Zukui
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依托单位:
Data-Driven Process Systems Optimization under Uncertain Environment
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批准号:RGPIN-2019-04584
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项目类别:Discovery Grants Program - Individual
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资助金额:$2.4万
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负责人:Li, Zukui
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负责人:Li, Zukui
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依托单位:
Systematic Management of Uncertainties in Process Operations
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批准号:435906-2013
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批准号:522294-2017
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财政年份:2017
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负责人:Li, Zukui
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Systematic Management of Uncertainties in Process Operations
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批准号:435906-2013
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项目类别:Discovery Grants Program - Individual
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资助金额:$1.82万
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财政年份:2016
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依托单位:
Systematic Management of Uncertainties in Process Operations
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批准号:435906-2013
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资助金额:$1.82万
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批准号:479777-2015
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资助金额:$1.82万
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财政年份:2015
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负责人:Li, Zukui
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依托单位:
Systematic Management of Uncertainties in Process Operations
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批准号:435906-2013
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项目类别:Discovery Grants Program - Individual
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负责人:Li, Zukui
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资助金额:$1.82万
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