(364ai) A Multiscale Modeling Framework for Sustainable Chemical Processes with Decision-Making Applications

Sustainable and integrated process systems are projected to become the future of chemical manufacturing. While the practical incorporation of renewability and sustainability into chemical production has generally implied the development of more advanced and complex technologies, computational modeling and simulation in that realm flourish and offer valuable insights with accuracy and predictability. Understanding the chemical process dynamics in different scales and their interactions and interconnectedness through computational modeling reveals a whole new perspective on productivity, resilience, and controllability. The main scientific subjects of this research consist of building and evaluating a high-fidelity and dynamic multiscale modeling framework for complex chemical systems and analyzing how they can be physically integrated under the scope of sustainable/renewable/smart manufacturing and real-time decision-making and optimization strategies.

Multiscale processes involve physical phenomena occurring at different temporal and spatial scales. Not surprisingly, most processes in chemical engineering are multiscale in nature. At smaller scales, electronic/atomic/molecular interactions dictate reaction kinetics and thermodynamics in bulk phases and surfaces. At mesoscales, transport phenomena, such as diffusion, advection, and convection, describe fluid dynamics and the formation of velocity, temperature, and concentration fields in a processing unit. Finally, at macroscales, interactions between processing units themselves in a plant through mass and energy flows are influenced by economic decisions and objectives. Although empirical dynamic data is limitedly collected and analyzed in traditional chemical processes at defined scales, the acquisition has uncertainties and lags and does not provide multiscale insights into the process. Theoretical multiscale integration of important traditional chemical processes was comprehensively investigated, ranging from heterogeneous catalytic processes to separation processes.

The dynamics of processes at different scales exhibit time disparities, which have imposed significant computational challenges and barriers to creating and resolving a unified modeling framework. Therefore, our research employs different computational platforms such as COMSOL, MATLAB, and Python to conciliate and combine methods/theories from molecular modeling (microkinetic modeling, transition state theory, and density functional theory) with traditional theories of thermodynamics, reaction engineering, and transport phenomena. Using this approach, an actual multiscale modeling framework can effectively describe the dynamics of innovative/relevant processes regarded in a sustainable chemical-energy economy centered on ammonia, hydrogen, air, biomass, and other clean material resources. Methods from machine learning have also been incorporated to allow faster communication and information flow between models through datasets. More specifically, this research includes the development of a feedback control loop for a packed-bed ammonia synthesis reactor and an integrated hydrogen-ammonia production system sustained by solar and wind energy, a surface-fluid-based model for carbon nanotube growth and high-fidelity models for water electrolysis cells/hydrogen fuel cells, biphasic fluidized reactors, bioreactors, gas adsorbers, 3D printing devices, organ-on-chip, among other technologies.

Research Interests: advanced modeling and simulation of chemical processes, sustainable process systems, reaction engineering.