Nick Larson, Manager, Asset Management, Ontario Clean Water Agency (OCWA)
As a Crown Agency of the Province of Ontario, OCWA is committed to ensuring all Ontario communities have access to a provider of safe and reliable water and wastewater services. This session will go over vision and needs of advanced data analytics for OCWA’s huge portfolio of assets and associated data. The objectives of the session are to give the audience a deeper dive into ways that Industry 4.0 is effecting and changing the world of asset management.
Janani Mohankirshnan, VP Product Innovation and Delivery, Banyan Water
Carl Sharkey, Business Development Director, Valor Water Analytics
In this presentation, speakers will provide a case study from a mid-sized agency regarding how they applied machine learning for a near real-time apparent loss software tool that uses a “bottoms up” method to identify meters that are under performing due to degradation, not linked to weather or conservation trends. This technology allowed the agency to prioritize their meter replacement program based on revenue lost/meter/month, optimizing their limited operational resources to recover maximum revenue.
Participants in this session will learn how to detect apparent losses and adopt new forms of reducing apparent loss that complement the AWWA standards on Apparent Losses and Water Loss Reduction Planning. Results of this tool and programmatic approach to apparent loss resolution are being embraced as a better practice, producing energy savings, and top-line revenue delivered to water utilities.
Kiran Gokal, Project Engineer, WSP
Matthew Gallagher, Project Engineer, WSP Digital
Khoa Nguyen, Project Engineer, WSP
WSP and Logan City Council, as part of the Logan Water Infrastructure Alliance recently conducted a machine learning trial, in partnership with WSP Digital, of its wastewater main CCTV footage. The project assessed the viability and efficacy of utilizing image classification with artificial neural networks and deep learning. The project was deemed to be a success, identifying issues at a “defect type” level with >90% accuracy. Machine learning of wastewater CCTV footage has, however, a deeper impact. Its influence spans across the asset owner’s broader business, CCTV operators, suppliers of cameras, inspection codes and standards, intellectual property and open source libraries for industry usage.
Registration fee is $50 + HST per computer
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