Data-driven Process Systems Engineering Lab
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Fani Boukouvala
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Data-driven Optimization
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Hybrid Modeling
Our aim is to develop new techniques to efficiently integrate modern data-analytics and machine learning techniques with chemical engineering fundamentals and first-principle modeling.
Relevant Publications
Reduced-order Modeling
Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques
Nonlinear Variable Selection Algorithms for Surrogate Modeling
Reduced order Discrete Element Method (DEM) modeling
Improving continuous powder blending performance using Projection to Latent Structures regression
Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 2 – Population Balance and Data-Based Methods
Dynamic Data-Driven Modeling of Pharmaceutical Processes
Surrogate Modeling
Surrogate-Based Optimization for Mixed-Integer Nonlinear Problems
Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques
Nonlinear Variable Selection Algorithms for Surrogate Modeling
Teaching data-analytics through process design
Surrogate-based optimization of expensive flowsheet models for continuous pharmaceutical manufacturing