Andrew Allman

• PhD, University of Minnesota, Twin Cities (2018), Chemical Engineering
  • Advisor: Prodromos Daoutidis
• BS, Pennsylvania State University (2013), Chemical Engineering
  • 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 an economic, safe, and sustainable manner. Research in process systems engineering focuses on developing new theory, methods, and algorithms that enable rapidly identifying the best possible solutions for such problems.

Professor Andrew Allman’s research team focuses on developing efficient solution approaches for solving new optimization problems emergent in sustainable chemical process systems. Our team specializes in achieving this by systematically identifying and exploiting the structure and sparsity inherent in the mathematical models underlying chemical, energy, and biological systems. Current theoretical areas of interest include (1) identifying easy-to-solve subproblems within large, complex optimization problems using network theory, (2) reducing the dimensionality of many-objective optimization problems resulting from, for example, the consideration of various environmental and social objectives for sustainability, and (3) using machine learning to accelerate the solution of optimization problems, particularly for operation and control problems which need to be solved online repeatedly.

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 always welcome learning about potential new collaborations and domains of application.

• NSF CAREER Award, 2023
• ACS PRF Doctoral New Investigator Award, 2022
• Invitee to Digital Chemical Engineering “Emerging Stars” Issue, 2022
• Invitee to AIChE Journal “Futures” Issue, 2022
• FOCAPD Best Poster Contribution Award, 2019
• AIChE CAST Director’s Student Presentation Award – First Place, 2018
• UMN CEMS Outstanding Teaching Assistant Award, 2018 and 2015

Selected Publications

• 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.

Book Chapters

• 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.
• Best Presentation in “Operation of Energy Systems” session at 2019 AIChE Annual Meeting, 2019