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

• 批准号: 556066-2020
• 负责人: Ray, AjayKumar
• 金额: $ 1.82万
• 依托单位: University of Western Ontario
• 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
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=706152
• 关键词: Deep; Transfer; Learning; Data; Operational

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.

化学过程行业正在使用人工智能采用数字技术，以提高效率。新的“大数据分析”时代（由使用智能数字测量设备爆炸的数据爆炸，由于云计算，功能强大的硬件和软件技术和通信平台的提高数据存储能力的驱动，随着将大量数据变成洞察力的新旅程，以获得更好的运营和业务决策。大多数制造设施，包括石油和天然气行业，这对于加拿大经济的福祉至关重要，目前正在经历数字化转型以保持全球竞争。为了实现这一数字革命，政府，学术界和行业的下一个里程碑，需要合作地专注于创新，以创新和数字专业知识的训练有素的劳动力的价值产生和发展。 使用数据科学，可以可靠，准确地智能地关联大量的过程数据。机器学习和深度学习算法能够自动从数据中收集见解，并做出预测，并提供手段以极高的准确性来指出过程干扰的根本原因，并预测过程不稳定性和失败，然后才有机会影响生产。西方大学与帝国石油之间的合作项目旨在应对具有相关技能的训练有素的人员对人员，过程安全，实施和可用性的影响。拟议的研究计划将为开发创新的数据驱动建模方法提供新的见解，以更好地控制和理解（a）在炼油厂和优化其运营时（a）拆卸运营，以及（b）水力处理运营单元，目的是优化整个炼油厂单元。结果将使帝国石油有效地改善其炼油厂运营，以实现可持续性，安全性和盈利能力。该项目的主要交付是开发新知识，工具和受过高度训练的合格人员，以推动数字技术并促进经济增长。