Refreshments will be served in the atrium outside of MoSE G011 at 2:30 p.m. Seminar will be held in Molecular Science & Engineering Building Room G011 at 3:00 p.m.
Andrew Allman, Department of Chemical Engineering and Materials Science, University of Minnesota
“Enabling efficient computational decision making for chemical and energy systems”
Computational optimal decision making tools are essential for ensuring that systems are designed, operated, and controlled in an economic and sustainable manner. Using optimization to make decisions for chemical and energy systems is particularly challenging due to the inherent presence of nonlinear process physics, both integer and continuous decisions, uncertainties in important parameters, and multiple relevant time scales. In this talk, two recent advances are discussed which enable solving optimal decision making problems more efficiently. First, an algorithmic framework for automatically identifying subproblems within an optimization problem using community detection, a concept from network theory, is presented. The superiority of using community-based decompositions to solve optimization problems, compared to other intuition-based decompositions, is showcased through an optimal model predictive control case study. The ability of the algorithm to identify subproblems when an intuitive decomposition does not exist is also demonstrated. In the second part of the talk, a new method for simultaneously determining the optimal design and operation of time-varying systems is presented. This method utilizes parallel processing to quickly generate optimal schedules for many different designs and fits a surrogate model to the results. Through a case study of wind-powered ammonia production, the ability of this method to more accurately determine optimal operating costs, better predict the effects of uncertainty in renewable energy production, and reduce solution times by orders of magnitude is shown.
Andrew Allman is a post-doctoral associate at the University of Minnesota working under the guidance of Professor Qi Zhang. He received his Ph.D. in chemical engineering in 2018 from the University of Minnesota with a thesis entitled “Enabling distributed renewable energy and chemical production through process systems engineering” under the guidance of Professor Prodromos Daoutidis. He received a B.S. with high honors in chemical engineering in 2013 from Penn State University. His research interests include developing theory and methods for the optimal design, control, planning, and scheduling of chemical and energy systems. He is particularly interested in applications which support sustainable water and energy usage and in decision making over multiple time scales.