Burcu Beykal

Assistant Professor

University of Connecticut

United States

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Dr. Burcu Beykal is an Assistant Professor at the University of Connecticut, United States. Her research primarily focuses on dynamic optimization processes, sustainable design methodologies for resource recovery, and the application of data-driven optimization techniques across various environmental and industrial contexts. Recent articles authored by Dr. Beykal explore topics such as algae cultivation optimization, lithium recovery from geothermal brines, and machine learning in food safety analysis.

Articles (19)

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2025

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UNITED STATES

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Timothy D. Phillips

UNITED STATES

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Burcu Beykal

Research Interests

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Articles (19)

Dynamic and stochastic optimization of algae cultivation process

Surrogate-Assisted Optimization of Highly Constrained Oil Recovery Processes Using Classification-Based Constraint Modeling

Bi-level data-driven enterprise-wide optimization with mixed-integer nonlinear scheduling problems

Sustainable process design for lithium recovery from geothermal brines using chemical precipitation

A mechanistic review on machine learning-supported detection and analysis of volatile organic compounds for food quality and safety

Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data

Using L. minor and C. elegans to assess the ecotoxicity of real-life contaminated soil samples and their remediation by clay- and carbon-based sorbents

Cumin and eucalyptus essential oil standardization using fractional distillation: Data-driven optimization and techno-economic analysis

Binary Classification of the Endocrine Disrupting Chemicals by Artificial Neural Networks

Process design and technoeconomic analysis for zero liquid discharge desalination via LiBr absorption chiller integrated HDH-MEE-MVR system

Process design and technoeconomic analysis for zero liquid discharge desalination via LiBr absorption chiller integrated HDH-MEE-MVR system

Comparative life cycle assessment of single-use cardiopulmonary bypass devices

A smart manufacturing strategy for multiparametric model predictive control in air separation systems

Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty

Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty

Combining Experimental Isotherms, Minimalistic Simulations, and a Model to Understand and Predict Chemical Adsorption onto Montmorillonite Clays

Combining Experimental Isotherms, Minimalistic Simulations, and a Model to Understand and Predict Chemical Adsorption onto Montmorillonite Clays

Multiobjective Optimization of Mixed-Integer Linear Programming Problems: A Multiparametric Optimization Approach

Multiobjective Optimization of Mixed-Integer Linear Programming Problems: A Multiparametric Optimization Approach

Collaborators (6)

Phanourios Tamamis

Mingyu Qiao

Yangchao Luo

Matthew Stuber

George Bollas

Timothy D. Phillips

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