Beykal, Burcu

Burcu Beykal

Assistant Professor, Chemical and Biomolecular Engineering

Email beykal@uconn.edu
Phone (860) 486-2756
Mailing Address Department of Chemical &
Biomolecular Engineering Engineering II, Room 204 191 Auditorium Road, Unit 3222 University of Connecticut Storrs, CT 06269-3222
Link Research Website
Google Scholar Link

Brief Bio

Dr. Burcu Beykal is an Assistant Professor in the Department of Chemical & Biomolecular Engineering at University of Connecticut. She earned her B.S. in Chemical and Biological Engineering from Koç University, her M.S. in Chemical Engineering from Carnegie Mellon University, and her Ph.D. in Chemical Engineering from Texas A&M University. Her research focuses on process design for large-scale lithium extraction from marginal sources and on developing computational frameworks for data-driven optimization and reinforcement learning of complex simulation-based problems. She also works broadly on machine learning methods for chemical, environmental, and biological systems. Dr. Beykal’s honors include the ACS Petroleum Research Fund Doctoral New Investigator Award, the 2020 AIChE CAST Directors’ Award, and recognition as a Rising Star in Chemical Engineering by MIT. In May 2025, she was appointed the Eversource Energy Assistant Professor in Environmental and Sustainability Education by the UConn College of Engineering, in recognition of her achievements in research, education, and service.

  • Process Systems Engineering
  • Process Design for Energy, Water, & Critical Minerals Recovery
  • Artificial Intelligence & Machine Learning for Process Systems Engineering
  • Process Operations & Supply Chain Management
  • Data-Driven Optimization & Algorithms
  • Data-driven Modeling of Environmental & Biological Systems
  • Techno-economic Analysis/Life Cycle Assessment
  • Constrained Reinforcement Learning for Waterflooding Control Optimization in Oil Reservoirs (ACS PRF)

  • Hybrid Modeling of Intensified Continuous Power-to-X Processes (Satell-C2E2)

  • Hyperparameter Optimization of Machine Learning Models using Bi-level Programming (UConn OVPR)

  • Comprehensive tools and models for addressing exposure to mixtures during environmental emergency-related contamination events (NIH)

  • Microalgae-based Biomanufacturing of Methionine for Organic Poultry Diets (NSF)

  • CHEG 4147: Process Dynamics & Control
  • CHEG 5330: Applied Machine Learning in Chemical Engineering
  • CHEG 5336: Optimization

Selected Publications

  1. H Nikkhah, Z Aghayev, A Shahbazi, VM Charitopoulos, S Avraamidou, B Beykal. Bi-level data-driven enterprise-wide optimization with mixed-integer nonlinear scheduling problems, Digital Chemical Engineering, 2025, 100218.
  2. H Nikkhah, A Di Maria, G Granata, B Beykal. Sustainable process design for lithium recovery from geothermal brines using chemical precipitation, Resources, Conservation and Recycling, 2025, 212, 107980.
  3. H Nikkhah, D Ipekci, W Xiang, Z Stoll, P Xu, B Li, JR McCutcheon, B Beykal. Challenges and opportunities of recovering lithium from seawater, produced water, geothermal brines, and salt lakes using conventional and emerging technologies, Chemical Engineering Journal, 2024, 498, 155349.
  4. BG Cohen, B Beykal, GM Bollas. Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data, Journal of Computational Physics, 2024, 514, 113261.
  5. H Nikkhah, B Beykal, MD Stuber. Comparative life cycle assessment of single-use cardiopulmonary bypass devices. Journal of Cleaner Production, 2023, 425, 138815.
  6. Z Aghayev, AT Szafran, A Tran, HS Ganesh, F Stossi, L Zhou, MA Mancini, EN Pistikopoulos, B Beykal. Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data. Chemical Engineering Science, 2023, 119086.
  7. H Nikkhah, B Beykal. Process Design and Technoeconomic Analysis for Zero Liquid Discharge Desalination via LiBr Absorption Chiller Integrated HDH-MEE-MVR System, Desalination, 2023, 116643.

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