Decision support systems based on heterogeneous data driven models for a safe and optimal operation of industrial process systems

基于异构数据驱动模型的决策支持系统，用于工业过程系统的安全和优化运行

• 批准号: RGPIN-2021-02929
• 负责人: Chioua, Moncef
• 金额: $ 2.4万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742062
• 关键词: Decision; support; systems; based; heterogeneous

Process industries including chemicals, oil and gas industries, pharmaceutics, mining, metals and pulp and paper play an important role in the Canadian economy. Today, operators are involved only in expectational circumstances because processes are autonomous under normal operating conditions. This disconnection is termed "the irony of automation". A higher level of autonomy of industrial process systems is needed to resolve this paradox. Recent advances in autonomous operations such as autonomous robots and self-driving cars have opened up completely new ways to deal with little or unstructured input information and to generate a large range of reactions in highly uncertain environments. This has in turn greatly increased public acceptance of autonomous systems. Fully autonomous operation of process systems is a complex problem because unlike autonomous driving, it requires multiple autonomous key features corresponding to multiple human roles (control room operations, field operation, process optimization and production scheduling) instead of a single one, the car driver. Some of the necessary underpinning technologies (Industrial Internet of Things, Big data, High-performance computing and novel sensing technologies) have only recently emerged. A recent survey released by Yokogawa finds two-thirds of process industry companies are anticipating fully autonomous operations by 2030. Within this context, the long-term objective of the research program is to create virtual control room assistants providing operators with early detection of plant anomalies, accurate diagnosis and suggestions of corrective actions. For this purpose, fundamental problems related to process models interpretability and online portability will be solved within this Discovery Grant with specific solutions tailored to real-world process systems and demonstrated in industrial trials via subsequent Alliance grants. Through four PhD and three MSc projects over the next five years, this research program will achieve a high impact by working closely with industrial partners to achieve the following short-term objectives: (1) Extract online portable and interpretable surrogate models from first principles models (impact: increase execution speed). (2) Predict process abnormal episodes (impact: increase robustness) (3) Integrate nominal and abnormal episodes process models into a single framework (impact: facilitate industrial uptake). The aforementioned survey indicates that 54% of respondents expect to increase their autonomy investment over the next 3 years as a direct result of COVID-19. The outcome of this research program will be instrumental in the development of online operator decision support systems. Using these systems will lead to (1) Reduced production costs through improved efficiency and higher plant availability (2) Improved end-product quality through advanced analytics (3) Safer working environments through remote operations.

包括化工、石油和天然气工业、制药、采矿、金属、纸浆和造纸在内的加工工业在加拿大经济中发挥着重要作用。如今，由于流程在正常操作条件下是自主的，因此作业者只会在预期的情况下参与操作。这种脱节被称为“自动化的讽刺”。解决这个矛盾需要更高层次的工业过程系统自治。自主机器人和自动驾驶汽车等自主操作的最新进展开辟了全新的方式来处理少量或非结构化的输入信息，并在高度不确定的环境中产生大范围的反应。这反过来又大大提高了公众对自治系统的接受程度。过程系统的完全自主运行是一个复杂的问题，因为与自动驾驶不同，它需要多个自主关键特征，对应于多个人类角色（控制室操作、现场操作、过程优化和生产调度），而不是一个单一的人，即汽车驾驶员。一些必要的基础技术（工业物联网、大数据、高性能计算和新型传感技术）最近才出现。横河电机（Yokogawa）最近发布的一项调查发现，三分之二的流程工业公司预计到2030年将实现全自动运营。在此背景下，研究计划的长期目标是创建虚拟控制室助手，为操作员提供工厂异常的早期检测，准确诊断和纠正措施建议。为此，与过程模型可解释性和在线可移植性相关的基本问题将在该发现基金中通过针对现实世界过程系统的特定解决方案得到解决，并通过随后的联盟资助在工业试验中得到证明。通过未来五年的四个博士和三个硕士项目，该研究计划将通过与行业合作伙伴密切合作，实现以下短期目标，从而实现高影响：(1)从第一原理模型中提取在线可移植和可解释的代理模型（影响：提高执行速度）。(2)预测过程异常事件（影响：增加鲁棒性）(3)将名义和异常事件过程模型整合到一个框架中（影响：促进工业吸收）。上述调查显示，54%的受访者预计，由于新冠疫情的直接影响，未来3年将增加对自动驾驶汽车的投资。这项研究计划的结果将有助于在线运营商决策支持系统的发展。使用这些系统将导致(1)通过提高效率和更高的工厂可用性来降低生产成本；(2)通过先进的分析来提高最终产品的质量；(3)通过远程操作来提高工作环境的安全性。