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

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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.