Deep Transfer Learning from Data for Operational Excellence in Refineries

从数据中进行深度迁移学习以实现炼油厂的卓越运营

基本信息

  • 批准号:
    556066-2020
  • 负责人:
  • 金额:
    $ 1.82万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Alliance Grants
  • 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

项目摘要

Chemical Process industries are adopting digital technologies using artificial intelligence for improved efficiency. The new 'big data analytics' era (driven by the explosion of data using smart digital measurement devices, improved data storage capacity due to cloud computing, powerful hardware and software technology and communication platforms) is emerging as the new journey to turn voluminous data into insights for better operational and business decisions. Most manufacturing facilities, including the Oil and Gas industry, which is paramount to the well-being of the Canadian economy, are currently experiencing a digital transformation to stay globally competitive. To reach the next milestone with this digital revolution, government, academia, and industry need to collaboratively focus on innovation for value generation and development of well-trained workforce with digital expertise. Using data science, vast process data can be intelligently correlated reliably and accurately. Machine learning and deep learning algorithms are capable of automatically gathering insights from data and making predictions and provide means to pinpoint the root cause of process disturbances with extreme accuracy, and predict process instabilities and failures before they have the chance to affect production. This collaborative project between Western University and Imperial Oil aims to address the challenges of digital technology implications on personnel, process safety, implementation and availability of trained personnel with relevant skills. The proposed research program will lead to new insights in developing innovative data-driven modeling approaches for better control and understanding of (a) de-salter operation at refinery and in optimizing its operation, and (b) hydro-processing operating units with the intention of optimization of the entire refinery unit. Results will allow Imperial Oil to effectively improve their refinery operations for sustainability, safety, and profitability. The key deliverable of the project are development of new knowledge, tools, and highly trained, qualified personnel to advance digital technologies and catalyze economic growth.
化工行业正在采用数字技术,利用人工智能提高效率。新的“大数据分析”时代(由使用智能数字测量设备的数据爆炸式增长、云计算、强大的硬件和软件技术以及通信平台提高的数据存储容量驱动)正在兴起,成为将海量数据转化为洞察力的新旅程,以实现更好的运营和业务决策。大多数制造业,包括对加拿大经济至关重要的石油和天然气行业,目前正在经历数字化转型,以保持全球竞争力。为了实现这场数字革命的下一个里程碑,政府、学术界和工业界需要共同关注创新,以创造价值,并培养训练有素的数字专业人才。 使用数据科学,可以智能地可靠和准确地关联大量过程数据。机器学习和深度学习算法能够自动从数据中收集见解并进行预测,并提供极准确地查明过程干扰的根本原因的方法,并在有机会影响生产之前预测过程不稳定性和故障。西部大学和帝国石油之间的这个合作项目旨在解决数字技术对人员,过程安全,实施和具有相关技能的训练有素的人员的可用性的影响的挑战。拟议的研究计划将导致新的见解,在开发创新的数据驱动的建模方法,以更好地控制和理解(a)在炼油厂的脱盐操作,并优化其操作,和(B)加氢处理操作单元的意图,优化整个炼油厂的单位。结果将使帝国石油公司能够有效地改善其炼油厂的可持续性,安全性和盈利能力。该项目的主要交付成果是开发新知识、工具和训练有素的合格人员,以推进数字技术并促进经济增长。

项目成果

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Ray, AjayKumarAK其他文献

Ray, AjayKumarAK的其他文献

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{{ truncateString('Ray, AjayKumarAK', 18)}}的其他基金

Comprehensive investigation of mine-impacted water treatment using cryo-purification: Bench-scale and pilot-scale stages with the aid of artificial intelligence application
使用低温净化对受矿井影响的水处理进行全面研究:借助人工智能应用进行小规模和中试规模阶段
  • 批准号:
    567160-2021
  • 财政年份:
    2022
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Alliance Grants

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具有时序迁移能力的Spiking-Transfer learning (脉冲-迁移学习)方法研究
  • 批准号:
    61806040
  • 批准年份:
    2018
  • 资助金额:
    20.0 万元
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    青年科学基金项目

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