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Application of Data Analytics In Pipelines
Presentation Overview
In this session, you will learn more about applications of AI in different industries and specifically in pipeline industry.
In pipeline industry, there are critical equipment the health of which is of critical importance such as pumps, compressors, control valves, variable frequency drives, motors, etc. Monitoring the health and status of these equipment using analytics and predictive approaches change the way this industry is running. By being proactive towards failures, the pipeline operation downtime and unplannee outages can be significantly prevented which means more operational safety, reliability and financial return.
This session will inspire you on how similar approaches could be utilized elsewhere to solve problems of this nature. You will also gain more understanding on how your business can rely on analytics and predictive methods to increase financial return from the asset by investing in more effiecint tools using big data analytics.
Speaker Bio
Marziyeh Keshavarz
Instrumentation Engineering Specialist, Enbridge
Marziyeh is an Instrumentation engineering specialist with Enbridge and has spent over the last 6 years working with instrumentation and pressure controls. Her focus is leading investigations on failures and applying fault detection methodologies in pressure control systems and enhancing control loop performance by leveraging advanced data analytics techniques. She is currently co-leading a high profile project on asset heath performance monitoring and equipment failure prediction, leveraging AI algorithms that aim at increasing equipment reliability by identification and prediction of equipment failures in order to minimize operational downtime.
Prior to Enbridge, Marziyeh worked overseas in oil and gas for 6 years as an instrumentation and process control engineer where she identified and leveraged advanced process control and inferential sensors (AI-based) in complex control applications in various refinery and petrochemical plants.
Marziyeh has a master's degree in chemical engineering from the University of Alberta specializing in process control,and a master's and bachelor's degree in electrical engineering from Iran University of Science and Technology in Tehran, Iran, specializing in control system design. Her second Master's thesis is on advanced data analytics in change detection using Bayesian and Expectation Maximization algorithms in instrumentation measurements that has wide applications in leak detection systems. While at the University of Alberta, Marziyeh had successful industrial AI-based projects including design of inferential sensors for naphtha recovery unit in the oil sands sector.
