Information Technology Infrastructure for Augmented Problem Solving in Process Systems

用于增强过程系统问题解决的信息技术基础设施

• 批准号: RGPIN-2018-04549
• 负责人: Chan, Christine
• 金额: $ 2.48万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=713037
• 关键词: Information; Technology; Infrastructure; Augmented; Problem

Intelligent decision support systems (DSSs) and applications of artificial intelligence (AI) play a key role in improving productivity of Canadian industries. The Information Technology Association of Canada (ITAC) advocated that Canada expands her development and adoption of information and communications technology (ICT) so as to drive stronger productivity across all sectors of the economy. It is estimated that the Canadian market for the "internet of things" solutions would grow from $5 billion in 2013 to $21.1 billion in 2018 [ITAC report, Feb 16, 2015]. Speaking to CNN (F. Zakaria) at the World Economic Forum at Davos Switzerland in 2017, Ginni Rometty, current IBM CEO, described AI's role in the partnership between humans and machines as “augmented cognition”. In other words, AI not only supports but augments human cognition so that humans can be more efficient and “do a better job” (http://machinelearning.ai/artificial-intelligence/davos-2017-ibm-ceo-ginni-rometty-on-artificial-intelligence/, retrieved on July 23, 2017). In building intelligent DSSs that support human problem solving, a fundamental and most difficult step is knowledge acquisition, mainly due to the implicit nature of knowledge [Breuker, 2013]. The objective of the proposed research is to build an information technology infrastructure for knowledge acquisition (KA), which will support building intelligent DSSs that augment the problem solving abilities of human operators when they encounter operational problems in engineering process systems. The rule extraction approach will be adopted because it can generate interpretable rules from a well-trained artificial neural network (ANN) model. The explicit knowledge will be integrated with human expertise for building intelligent DSSs. This approach can effectively make use of the vast amount of data generated by the Canadian process industries while also overcoming the KA bottleneck. The KA infrastructure will be applied to the carbon dioxide capture process system and the cyclic solvent injection process for heavy oil recovery. The former is a key technology for cutting harmful carbon dioxide emissions from coal-fired power generation, and the latter system is a recovery method of heavy oil, a key resource important for sustaining the Canadian economy for many decades to come. The research results of the KA method, algorithms, and DSSs will benefit industrial engineers and operators. The insights and lessons learned in the research project would enable them to better understand the complex engineering process systems, and the DSSs would support them in solving operational problems in a timely manner and avoid downtime. The research results and HQP trained will contribute to information technology advancement of industrial operations related to energy production and greenhouse gas reduction in the Canadian economy.

智能决策支持系统（DSS）和人工智能（AI）的应用在提高加拿大工业生产力方面发挥着关键作用。加拿大信息技术协会（ITAC）主张加拿大扩大信息和通信技术（ICT）的发展和采用，以推动经济所有部门的生产力。 据估计，加拿大的“物联网”解决方案市场将从2013年的50亿美元增长到2018年的211亿美元[ITAC报告，2015年2月16日]。 CNN（F。Zakaria）在2017年瑞士达沃斯世界经济论坛上，IBM现任CEO Ginni Zakartty将AI在人类与机器伙伴关系中的作用描述为“增强认知”。换句话说，人工智能不仅支持而且增强了人类的认知，使人类能够更有效率，“做得更好”（http：//machinelearning.ai/artificial-intelligence/达沃斯-2017-ibm-ceo-ginni-rometty-on-artificial-intelligence/，2017年7月23日检索）。在构建支持人类解决问题的智能DSS时，最基本也是最困难的一步是知识获取，这主要是由于知识的隐含性质[Breuker，2013]。 拟议的研究的目标是建立一个信息技术基础设施的知识获取（KA），这将支持建设智能DSS，增强人类操作员的问题解决能力，当他们遇到的操作问题，在工程过程系统。 规则提取方法将被采用，因为它可以从一个训练有素的人工神经网络（ANN）模型生成可解释的规则。 明确的知识将与人类的专业知识相结合，用于构建智能DSS。 这种方法可以有效地利用加拿大流程工业产生的大量数据，同时克服KA瓶颈。 KA基础设施将应用于二氧化碳捕集工艺系统和稠油开采的循环溶剂注入工艺。前者是减少燃煤发电有害二氧化碳排放的关键技术，后者是重油的回收方法，重油是未来几十年维持加拿大经济的重要资源。KA方法，算法和DSS的研究成果将有益于工业工程师和操作人员。在研究项目中获得的见解和经验教训将使他们能够更好地了解复杂的工程流程系统，DSS将支持他们及时解决运营问题并避免停机。研究成果和HQP培训将有助于信息技术进步的工业运作有关的能源生产和温室气体减少在加拿大经济。