Chemical and Biological Engineering Spring 2022 Seminar: Hybrid Machine Learning and Physics-based Optimization

Wednesday, February 23, 2021 Online Via Zoom
3:15–4:30 p.m.
Meeting ID: 812 0390 8516
Passcode: 863417

“Hybrid Machine Learning and Physics-based Optimization”


Dr. Hedengren received a Ph.D. degree in Chemical Engineering from the University of Texas at Austin and is an Associate Professor at Brigham Young University. Previously, he previously worked with ExxonMobil on Advanced Process Control. His primary research focuses on accelerating machine learning and automation technology across industries. Other research interests include fiber optic monitoring, Intelli-fields, reservoir optimization, drilling automation, nuclear hybrid energy systems, and unmanned aerial systems. He is a leader of the Center for Unmanned Aircraft Systems (C-UAS), applying UAV automation and optimization technology to energy infrastructure.

A convergence of several key technologies creates an opportunity to use sophisticated mathematical models within automation. A significant challenge is the size of the physics-based models that have too many adjustable parameters or are too slow in simulation to extract actionable information. This presentation shows how fit-for-purpose models can be used directly in automation and optimization solutions. These fit-for-purpose models have unlocked new ways of thinking. Hybrid modeling uses the strengths of both physics-based and data-informed modeling approaches. A hybrid approach uses a priori knowledge in the form of a nonlinear physics-based model with empirical model elements. The challenges and opportunities for combining physics-based and data-driven elements are discussed. The methods are demonstrated on two applications: mineral processing and drilling automation.

Machine learning adapts using data to gain experience. It is a convergence of linear algebra, statistics, optimization, and computational methods for computer systems to infer relationships and make decisions from data. Examples of machine learning are now common and are expected to further influence transportation, entertainment, retail, and energy industries. This presentation shares several resources for engineers to apply Python for data science and machine learning.