University of Minnesota, Twin Cities
PhD in Chemical Engineering, 2018
Advisor: Prodromos Daoutidis
Pennsylvania State University
BS in Chemical Engineering, 2013
With High Distinction
Energy and Fuels Focus, Chemistry Minor
Decision making occurs at every level of chemical process engineering. Computational tools for quickly screening a complex space of feasible decisions to find the optimal one are essential for ensuring chemical systems are designed, operated, and controlled in a economic, safe, and sustainable manner. Research in process systems engineering focuses on developing new theory, methods, and algorithms that enable computationally efficient solutions for such problems.
Professor Andrew Allman’s research team focuses on identifying and exploiting the structure and sparsity inherent in the mathematical models underlying chemical, energy, and biological systems to enable computationally efficient decision making. Current theoretical areas of interest include (1) identifying easy-to-solve subproblems within large, complex optimization problems, (2) developing new solution approaches which better exploit a given problem’s structure, (3) enhancing data-driven decision making methods through a priori dimensionality reduction in data collection, and (4) reducing the complexity of many-objective optimization problems by identifying subsets of objectives which have the strongest tradeoffs.
A critical application of interest of our team is addressing the challenges associated with the next generation of chemical manufacturing, which is expected to mirror the shift in energy production towards facilities that are small scale and distributed in nature, with time-varying, rather than steady state, operation. We are also interested in applications in sustainable engineering, plant-wide control, supply chain management, game theory, and systems biology. Overall, our optimization tools are highly generalizable and we are always seeking new collaborations and domains of application.
University of Michigan
Chemical Engineering Department
Ann Arbor, Michigan
Assistant Professor, 2020-
University of Minnesota, Twin Cities
Postdoctoral Associate, 2019-2020
Advisor: Qi Zhang
Best Presentation in “Operation of Energy Systems” session at 2019 AIChE Annual Meeting, 2019
ACS Editors’ Choice Article, 2019
FOCAPD Best Poster Contribution Award, 2019
AIChE CAST Director’s Student Presentation Award – First Place, 2018
Outstanding Teaching Assistant Award – for CHEN 8301, 2018
Best Presentation in “Advances in Optimization I” session at 2017
AIChE Annual Meeting, 2017
Best Presentation in “Distributed Chemical and Energy Processes for Sustainability” session at 2016 AIChE Annual Meeting, 2016
Outstanding Teaching Assistant Award – for CHEN 8201, 2015
Also see Google Scholar
Allman, A., Zhang, Q. Dynamic location and relocation of modular manufacturing facilities. European Journal of Operations Research (286), 2020, 494-507.
Allman, A., Zhang, Q. Distributed cooperative industrial demand response. Journal of Process Control (86), 2020, 81-93.
Allman, A., Daoutidis, P, Arnold, W., Cussler, E.L. Efficient water pollution abatement. Industrial & Engineering Chemistry Research (58), 2019, 22483-22487. (ACS Editor’s Choice Article)
O’Brien, C., Allman, A., Daoutidis, P., Hu, W.-S. Kinetic model optimization and its application to mitigating the Warburg effect through multiple enzyme alterations. Metabolic Engineering (56), 2019, 154-164.
Allman, A., Tang, W., Daoutidis P. DeCODe: A community-based algorithm for generating high-quality decompositions of optimization problems. Optimization and Engineering (20), 2019, 1067-1084.
Allman, A., Palys, M.J., Daoutidis, P. Scheduling-informed optimal design of systems with time-varying operation: A wind-powered ammonia case study. AIChE Journal (65), 2019, e16434.
Daoutidis, P., Allman, A., Palys, M.J. Smart manufacturing: A sustainable energy perspective. In Smart Manufacturing: Concepts and Methods. Soroush, M., Baldea, M., Edgar, T., eds. Elsevier, 2019.