2023 Schedule

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Unlocking the Potential of Predictive Maintenance for Achieving Net-Zero Emission Goals

Presenter: Sunil Vedula
CEO and Founder, Nanoprecise Sci Corp
Description:
The presentation will provide a comprehensive analysis of the transformative role of predictive maintenance in achieving net-zero emission goals. The insightful session will delve into the effective utilization of machine learning, advanced analytics, and IoT technologies within a predictive maintenance system, for improving the overall efficiency of their equipment sets and reducing their carbon footprint. Attendees will gain valuable insights into how this approach optimizes energy consumption of equipment sets, minimizes downtime, and enhances overall machine performance. Join us to discover the untapped potential of this innovative approach and learn how it can drive sustainable practices, propelling organizations towards a net-zero future.
About the Presenter:

Sunil Vedula is a professional engineer from Canada with 12 years of experience in the oil & gas sector. He is an expert in machine design, material science, finite element modelling, and data analytics. He obtained his B.Sc. in Mechanical Engineering from DCE and an MBA from the University of Alberta, Canada. He is an avid reader and extremely passionate about technology commercialization in the field of IIoT, AI, and Industry 4.0 so as to improve efficiency and reduce carbon emissions as much as possible. He won awards at MIT, and recently won start-up awards at the prestigious Society of Petroleum Engineers Startup pitch competition. At Nanoprecise, he is enabling affordable and accurate automated predictive maintenance for every rotary machine, be it in any industry. In this pursuit, his team created a complete end-to-end patent-pending solution that comprises smart hardware and AI-based software, and combines physics, material science, and data analytics to diagnose issues with machinery and predict the “Remaining Time to Failure.”

Beyond Predictive Maintenance: Accelerating the Path to Net Zero

Presenter: Sunil Vedula
CEO and Founder, Nanoprecise Sci Corp
Description:
The presentation will explore the critical role of predictive maintenance in achieving net-zero emissions. Traditional maintenance practices are not sufficient to meet the growing demand for decarbonization, which has only emphasized the need for an innovative, data-driven approach to maintenance. The discussion will highlight the pressing demand for an innovative, data-driven approach to maintenance, underscored by a comprehensive framework that integrates cutting-edge machine learning and AI techniques. The framework aims to optimize maintenance activities and reduce carbon emissions, bringing about numerous benefits such as reduced downtime and carbon footprint, as well as enhanced efficiency and cost savings. Real-world examples of successful implementation within various industries will be showcased, serving to illustrate the efficacy of this approach. The presentation will culminate with an emphasis on the importance of collaboration among industry players in accelerating the transition to a net-zero emissions future.
About the Presenter:

Sunil Vedula is a professional engineer from Canada with 12 years of experience in the oil & gas sector. He is an expert in machine design, material science, finite element modelling, and data analytics. He obtained his B.Sc. in Mechanical Engineering from DCE and an MBA from the University of Alberta, Canada. He is an avid reader and extremely passionate about technology commercialization in the field of IIoT, AI, and Industry 4.0 so as to improve efficiency and reduce carbon emissions as much as possible. He won awards at MIT, and recently won start-up awards at the prestigious Society of Petroleum Engineers Startup pitch competition. At Nanoprecise, he is enabling affordable and accurate automated predictive maintenance for every rotary machine, be it in any industry. In this pursuit, his team created a complete end-to-end patent-pending solution that comprises smart hardware and AI-based software, and combines physics, material science, and data analytics to diagnose issues with machinery and predict the “Remaining Time to Failure.”