Luis Ricardez-Sandoval is a Chemical Engineering Assistant Professor at the University of Waterloo.
His research program is focused on the development and implementation of theoretical and computational-based tools. These tools are aimed at improving the design and operability of chemical systems and also aim to provide new insight on industrially-relevant processes.
Professor Ricardez-Sandoval’s research group has developed a set of methodologies that can be applied to a general class of chemical systems for process improvement. They have developed mathematical models that provide new insight about the mechanisms and operation of industrially-relevant processes. A key aspect in Professor Ricardez-Sandoval’s research is the implementation of uncertainty analysis to assess process variability under uncertainty. He strongly believes that model uncertainty is essential for designing efficient mathematical tools that can realistically be applied in process improvement practice.
His research has been supported by federal government collaborators (CFI, ORF, NSERC, Mitacs, CanmetENERGY, Government of Canada); industrial partners (Activation Laboratories and Angstrom Engineering); and the Ontario Government; as well as the Early Researchers Award granted by the Ministry of Research and Innovation. In addition, he has received visitor scholars that have been financially supported by different agencies such as Emerging Leaders in the Americas Program (ELAP) and Canada-Brazil without borders.
Professor Ricardez-Sandoval’s current nanotechnology interests focus on the development of multiscale models for manufacturing and catalytic processes such as thin film deposition and methane cracking reactions for the production of hydrogen and carbon nano-materials. He also aims to develop comprehensive mechanistic process models that can describe the behavior of CO2 capture technologies and new technologies for IGCC power plants for clean power production. Professor Ricardez-Sandoval’s research interests also include the development of new methodologies that can efficiently perform optimal design while considering the dynamics and controllability of the systems under uncertainty. He also develops novel and efficient mathematical formulations for optimal scheduling of large-scale facilities in order to improve operations management in the manufacturing and analytical service sectors.
- Process Control
- Process Design
- Modelling And Simulation Of Micro And Nano Systems
- Multiscale Modelling
- Process Control Applied To Micro And Nano Systems
- Computation-based Tools
- Model Uncertainty
- Development Of Multi-scale Models
- Industry-relevant Catalytic Processes
- CO2 Capture Technologies
- Process Systems Engineering
- Dynamic Optimization Under Uncertainty
- Optimal Integration Of Design Control And Scheduling
- Modelling Simulation And Optimization Of Advanced Energy And CO2 Capture Systems
- Multiscale Process Systems
- 2008, Doctorate, Chemical Engineering, University of Waterloo
- 2000, Master of Science, Chemical Engineering, Instituto Tecnologico de Celaya
- 1997, Other, Chemical Engineering, Instituto Tecnologico de Orizaba
- CHE 121 - Engineering Computation
- CHE 725 - Research Topics in Analysis of Chemical Processes
- CHE 420 - Introduction to Process Control
- CHE 720 - Special Topics in Analysis of Chemical Processes
- *Chaffart D., Ricardez-Sandoval L.(2017). Robust Optimization of a Multiscale Catalytic Reactor System with Spatially-Varying Uncertainty Descriptions using Polynomial Chaos Expansions. Canadian Journal of Chemical Engineering. (Accepted in 2017)
- A multitasking continuous time formulation for short-term scheduling of operations in multipurpose plants (Accepted in 2017)
- *Koller R., Ricardez-Sandoval L.(2017). A Dynamic Optimization Framework for Integration of Design, Control and Scheduling of Multi-product Chemical Processes under Disturbance and Uncertainty. Computers & Chemical Engineering. 106: 147-159 (Accepted in 2017)
- *Kimaev G., Ricardez-Sandoval L.(2017). A comparison of efficient uncertainty quantification techniques for stochastic multiscale systems. AIChE Journal. 63: 3361-3373 (Accepted in 2017)
- *Lagzi S., *Yeon Lee D., Fukasawa R., Ricardez-Sandoval L.(2017). A computational study of continuous and discrete time formulations for short-term scheduling of operations in multipurpose plants. Industrial & Engineering Chemistry Research. 56: 8940–8953 (Accepted in 2017)