Juan Manuel Restrepo-Flórez - Modeling, Theory, and Simulation

We develop mathematical theories, AI-based algorithms, and computational simulations across the atomistic, particle, and continuum levels to model chemical engineering processes, with the aims of gaining fundamental scientific knowledge and devising next-generation applications in in-space manufacturing, renewable energy, drug delivery, geological formation, electrochemical impedance spectroscopy, and membrane-based separation.


Photo of Juan Manuel Restrepo-Flórez

Juan Manuel Restrepo-Flórez

Assistant Professor
Work Office: 215 ChE 1006 Center Drive Gainesville Florida 32611 Work Phone: (352) 392-6591 Website: Restrepo PSE Lab


In my group, we leverage our expertise in optimization and multhyphysics simulations to formulate mathematical models enabling the identification of new, sustainable, and innovative processes, and materials. We are motivated by the grand-challenges in sustainability: (1) the need to develop carbon-neutral processes to produce energy and chemicals, (2) the need to minimize waste generation, and (3) the urgency to find mitigation strategies to alleviate the damage already done. We focus our efforts into two research thrusts: (1) the design of tools and the formulation of models to support the synthesis, analysis, and optimization of sustainable energy systems and processes that use waste materials as feedstock, and (2) the development of methods for analyzing and enabling new separation technologies.

  1. At the energy systems level, we are interested in the synthesis of biorefineries, their integration with electro/photo-catalytic processes that produce fuels and chemicals from CO2, and the incorporation of renewable energy sources into these processes. At the waste management level, we are concerned with designing and analyzing facilities to process/upgrade plastic waste. Our efforts are devoted to addressing four questions instrumental for the deployment of these technologies: What pathways should we use to upgrade biomass or plastics into fuels and chemicals? How can we identify them considering economic and environmental criteria? How can we tailor them to obtain products with similar or better properties than those currently available? And how can we design their supply chains? To address these questions, we leverage the use of optimization tools (e.g., mixed-integer non-linear programming (MINLP), superstructure-based optimization, stochastic programming), life cycle analysis (LCA), and techno-economic analysis (TEA).
  2. At the separations level, we focus on developing tools for the synthesis and analysis of membranes and adsorption processes and on the design of new materials for the precise control of mass diffusion. Designing energy-efficient separations is fundamental to mitigate the environmental impact of industrial processes. Both membranes and adsorption appear as energy-efficient alternatives. The understanding of these technologies from the material to the process scale enables their widespread implementation. In this field, my research addresses the following questions: How can we use multiphysics simulations to inform the design of adsorption and membrane processes? How can we automate the synthesis of membrane separation cascades? How can we exploit recent advances in diffusion theory to design new materials and devices? To tackle these questions, we will rely on tools such as multiphysics simulations, data-driven surrogate models, and superstructure-based optimization.


Post-doctoral Training, 2019-2022, Chemical Engineering, University of Wisconsin-Madison
Ph.D., 2019, Chemical Engineering and Biomolecular Engineering, Georgia Institute of Technology