John Hedengren

Dr. John Hedengren is an Associate Professor at Brigham Young University in the Chemical Engineering Department. He leads the BYU Process Research and Intelligent Systems Modeling (PRISM) group with a current focus on structured machine learning for optimization of energy systems, unmanned aircraft, and drilling. Prior to BYU he worked in industry for 7 years on nonlinear estimation and predictive control for polymers. His work includes the APMonitor Optimization Suite with a recent extension to the Python GEKKO language. He led the development of the Arduino-based Temperature Control Lab that is currently used by 70 universities for process control education. His 60 publications span topics of oil production, drilling automation, smart grid optimization, unmanned aerial systems, and nonlinear predictive control.

His professional service includes an appointment as an adjunct professor at the University of Utah, and member of the AIChE CAST Executive Committee (Webinar Editor). In 2005, he received a Ph.D. (Ch.E.) from the University of Texas at Austin for contributions to control and estimation of large-scale dynamic systems. He served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models". He completed a sabbatical in 2020 to collaboratively develop combined physics-based and machine learned methods for optimization and automation.

Prof. Hedengren has consulting experience with Facebook, Apache, ENI Petroleum, HESS, SABIC Ibn Zahr, TOTAL, and other companies on machine learning and automation solutions. He worked full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. Automation software that he developed has been applied in over 100 industrial applications world-wide in refineries, chemical plants, and offshore oil platforms.