Towards digital twin based control of water resource recovery facilities - Methods supporting the use of adaptive hybrid digital twins

基于数字孪生的水资源回收设施控制 - 支持使用自适应混合数字孪生的方法

• 批准号: RGPIN-2021-04347
• 负责人: Vanrolleghem, Peter
• 金额: $ 5.32万
• 依托单位: Université Laval
• 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=742988
• 关键词: Towards; digital; twin; based; control

Water Resource Recovery Facilities (WRRF) are subject to increasing wastewater loads and stricter objectives with respect to environmental protection and quality of recovered resources. Process control is a proven methodology to improve process performance without costly expansion of the facilities. However, the complexity of the systems and the stricter requirements ask for process control to step-up and go beyond the omnipresent on/off and PID controllers. Over the next 5 years the research program of this Discovery grant will capitalize on previous research on data quality assessment tools and WRRF models, and the unique pilot WRRF installed at Université Laval in 2014, to train 14 HQP, to develop digital twins that can be integrated in model-based process control, to take part in the digitalization of the water sector. The long-term objectives of this research program are to develop and apply new data-driven methods that leverage machine learning and AI, to WRRF operation and control. Hybrid digital twins that combine mechanistic insights and data-driven relations will be the central ingredient of this new process control approach. The short-term objectives aim at dealing with some of the deficiencies in the data pipeline through which high quality data must flow to continuously update the digital twin to keep it mimicing the WRRF and to feed the control with, for instance, information on the influent wastewater. Improving process performance and quality of treated wastewater and recovered resources will require developing: 1)Methods for real-time detection of equipment and process faults (Fault detection); 2)Characterization of the main WRRF's disturbances (Real-time influent generator); 3)Algorithms for on-line process model updating (Adaptive hybrid digital twin); 4)Efficient control systems taking advantage of predictions by the digital twin and equipped with fallback strategies in case of faults (Digital twin-based, fault-accommodating control). The proposed research program is not focusing on building the digital twin itself, but rather on methods ensuring digital twins are up-to-date, fed with proper data and can be used for model-based control. The developments will be tested on the pilot WRRF (with parallel trains for direct comparison of approaches) and two full-scale systems on which HQP can be exposed to practical implementation issues. The project will train 8 graduate students (4 PhD, 4 MSc), 5 undergraduates and a postdoctoral fellow. These HQP will acquire a wide range of skills thanks to the involvement in the different aspects of the research project, the exposure to national and international research and to practice through the work on an industry standard pilot facility and two full-scale WRRFs. The research programme includes key elements of equity, diversity, and inclusion (EDI) in HQP recruitment/training, team composition and research programming/management/impact.

水资源回收设施（WRRF）受到日益增加的废水负荷和在环境保护和回收资源质量方面更严格的目标的制约。过程控制是一种经过验证的方法，可以在不需要昂贵的设施扩建的情况下提高过程性能。然而，系统的复杂性和更严格的要求要求过程控制升级，超越无所不在的开/关和PID控制器。在接下来的5年里，Discovery资助的研究项目将利用之前对数据质量评估工具和WRRF模型的研究，以及2014年在拉瓦尔大学安装的独特的WRRF试点，培训14名HQP，开发可集成在基于模型的过程控制中的数字双胞胎，参与水务部门的数字化。该研究计划的长期目标是开发和应用新的数据驱动方法，利用机器学习和人工智能来进行WRRF的操作和控制。结合机械洞察力和数据驱动关系的混合数字孪生将成为这种新过程控制方法的核心成分。短期目标旨在处理数据管道中的一些缺陷，高质量的数据必须通过这些缺陷流动，以不断更新数字孪生体，使其保持模仿WRRF，并向控制提供有关流入废水的信息。提高处理后废水和回收资源的工艺性能和质量将需要开发：1)设备和工艺故障的实时检测方法（故障检测）；2)主要WRRF干扰表征（实时进水发生器）；3)过程模型在线更新算法（自适应混合数字孪生）；4)利用数字孪生预测并配备故障回退策略的高效控制系统（基于数字孪生的容错控制）。拟议的研究计划并不是专注于构建数字双胞胎本身，而是如何确保数字双胞胎是最新的，提供适当的数据，并可用于基于模型的控制。这些发展将在试点WRRF（采用平行列车直接比较方法）和两个全尺寸系统上进行测试，在这两个系统上，HQP可以接触到实际实施问题。本项目将培养研究生8人（博士4人，硕士4人），本科生5人，博士后1人。这些HQP将通过参与研究项目的不同方面、接触国内和国际研究以及通过在一个工业标准试点设施和两个全规模WRRFs上的工作进行实践，获得广泛的技能。研究计划包括HQP招聘/培训、团队组成和研究规划/管理/影响中的公平、多样性和包容性（EDI）的关键要素。