Information Technology Infrastructure for Augmented Problem Solving in Process Systems

***智能决策支持系统（DSSs）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）倡导加拿大扩大信息和通信技术（ICT）的发展和采用，以推动所有经济部门的生产力提高。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。2017年在瑞士达沃斯举行的世界经济论坛上，IBM现任首席执行官吉尼·罗睿兰（Ginni Rometty）在接受CNN采访时，将人工智能在人与机器之间的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能力，使人类能够更高效地“做得更好”（http://machinelearning）。ai/artificial intelligence/davos-2017-ibm-ceo-ginni-rometty-on-artificial-intelligence/，检索于2017年7月23日)。在构建支持人类解决问题的智能决策支持系统时，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式本质[Breuker， 2013]。******本研究的目标是建立一个知识获取（KA）的资讯科技基础架构，以支援建立智慧型决策支持系统，以增强人类操作员在工程过程系统中遇到操作问题时解决问题的能力。本文将采用规则提取方法，因为它可以从训练良好的人工神经网络模型中生成可解释的规则。显性知识将与人类专业知识相结合，构建智能决策支持系统。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，同时也克服了KA瓶颈。KA基础设施将应用于稠油开采的二氧化碳捕集工艺系统和循环溶剂注入工艺。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，重油是维持加拿大经济未来几十年的重要关键资源。KA方法、算法和决策支持系统的研究成果将使工业工程师和操作人员受益。在研究项目中所获得的见解和经验，有助他们更了解复杂的工程过程系统，而决策支持系统则有助他们及时解决运作问题，避免停工。研究成果和HQP培训将有助于加拿大经济中与能源生产和温室气体减少有关的工业操作的信息技术进步。********************************************

***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.********************************************