Luis Ricardez-Sandoval, PEng (He/Him)

Luis Ricardez-Sandoval is a Tier II Canada Research Chair in Multiscale Modelling and Process Systems and an Associate Professor in the Department of Chemical Engineering at the University of Waterloo.
His Chemical Process Optimization, Multiscale Modelling and Process Systems research group develops and implements theoretical and computational tools that analyze, describe and predict the behaviour of complex chemical materials, processes and systems to support optimal design, performance and operations management. The group has specific expertise in advanced CO2 capture technologies and energy systems as well as computer-aided designs for heterogeneous catalysis.
They use the multiscale modelling approach, which accounts for all phenomena, to gain a comprehensive perspective of processes. The group focusses on phenomena that evolve at multiple temporal and spatial scales, with particular focus on phenomena that take place at the fine (nano) scale.
Their computerized tools, which utilize advanced mathematics, machine learning algorithms and uncertainty analyses, can simulate how chemical systems are likely to behave in real life. These simulations can be analyzed and used to maximize economic investment and process efficiency, often before any resources are used. They provide a fast, efficient and safe way to, for example, effectively and efficiently design new chemical materials and improve chemical process operations.
Much of their work involves collaboration with government and industry to reduce carbon and greenhouse gas emissions and enhance emerging energy options.

Current research includes the development of:
• New methodologies for optimal process design and operations management of dynamic systems under uncertainty
• Computer-aided design of materials and systems that evolve at different spatial and time scales
• Modelling, advanced model-based control and optimization of conventional and emerging energy systems, CO2 capture and conversion technologies, and novel process intensification systems for clean power production
• Multiscale models for manufacturing processes, such as thin film deposition, methane cracking reactions for the production of hydrogen and carbon nano-materials, and novel CO2 conversion technologies
• Novel and efficient mathematical formulations for optimal planning and scheduling of industrial-scale facilities to improve operations management in the manufacturing and analytical service sectors.

Professor Ricardez-Sandoval’s research has been supported by the Government of Canada through its collaborative agencies (Canada Foundation for Innovation, Ontario Research Fund, Natural Sciences and Engineering Research Council of Canada, Mitacs, and CanmetENERGY); industrial partners (Activation Laboratories, Cooper-Standard, Sanofi, Sartorius, Bank of Montreal); and the Government of Ontario, through the Early Researchers Award granted by the Ministry of Research and Innovation. In addition, Ricardez-Sandoval has received graduate students and visiting scholars that have been financially supported by international agencies and institutions, such as Consejo Nacional de Ciencia y Tecnologia (CONACyT-Mexico), China Scholarship Council (CSC-China), Technical University of Denmark (DTU-Denmark), Tampere University (Finland), and Universidad de Los Andes (Colombia).

Professor Ricardez-Sandoval is member of the Editorial Board of Computers and Chemical Engineering Journal, and Associate Editor of the Canadian Journal of Chemical Engineering and Digital Chemical Engineering.

• Process Systems Engineering
• Multiscale Modelling
• Computer-aided Catalyst and Materials Design
• Chemical Process Optimization
• Optimal Integration of Process Design, Control and Scheduling
• Process Intensification
• Modelling and Simulation of Conventional and Emerging Energy and CO2 Capture Technologies
• Modelling and Simulation of Micro and Nanotechnology Systems
• Optimal Process Scheduling

Research Interests: 1) Optimal Process Integration: Develop comprehensive frameworks and formulations that can take into account decisions emerging in chemical systems at multiple levels, i.e., Planning, Scheduling, Operation & Control, Design. 2) Machine Learning: Explore the application and development of machine learning methods (e.g., Reinforcement learning) to address relevant chemical engineering problems. 3) Computer-Aided Material's Design: Accelerate the discovery of new catalyst materials for emerging chemical engineering applications, e.g., CO2 utilization. 4) Advanced Process Control & Operations Management: Develop new formulations and frameworks that can improve the operability and/or controllability of systems that may be subject to uncertainty. 5) Modelling & Optimization: Develop comprehensive mathematical models that can enhance the long-term sustainability of advanced energy systems (e.g., Chemical Looping Combustion-CLC), agriculture processes (e.g., Recirculating Aquaculture Systems-RAS), and novel intensified systems (e.g., biomass-based catalytic columns).

Process Systems Engineer/Risk Analysis Engineer: Jun-2001 to July-2004; Operation Administrator: Sept-2000 to May-2001; Jr. Balance Supervisor: Jan-2000 to Aug-2000; Engineering Assistant: Jan-1996 to March-1997

• 2008, Doctorate Chemical Engineering, University of Waterloo, Canada
• 2000, Master of Science Chemical Engineering, Instituto Tecnologico de Celaya, Mexico
• 1997, Bachelor of Science Chemical Engineering, Instituto Tecnologico de Orizaba, Mexico

• 2024 Recipient of the Canadian Society of Chemical Engineering (CSChE)’s D.G. Fisher Award
• 2024 Capstone Design Sustainable Development Award, University of Waterloo
• 2024 Feature Article, Canadian Journal of Chemical Engineering: https://doi.org/10.1002/cjce.25249
• 2022 Front Cover Article, Chemical Engineering Science: https://doi.org/10.1002/cjce.25249
• 2018 Best Presentation Award in “Advances in Computational Methods and Numerical Analysis”, 2018 AIChE Annual Meeting
• 2018 NSERC's Discovery Accelerator Supplement, Natural Sciences and Engineering Research Council of Canada (NSERC)
• 2017 Canada Research Chair (Tier II), Natural Sciences and Engineering Research Council of Canada (NSERC)
• 2017 Front Cover Article, Industrial & Engineering Chemistry Research Journal: https://doi.org/10.1021/acs.iecr.7b01718
• 2015 Best Poster Presentation Award, IFAC’s International Symposium of Advanced Control of Chemical Processes (ADCHEM)
• 2015 Early Researchers Award (ERA), Ministry of Research and Innovation (Ontario)
• 2015 2015 Engineering Research Excellence Award, University of Waterloo
• 2015 2015 Outstanding Performance Award, University of Waterloo
• 2014 2014 Teaching Excellence Award in Engineering, University of Waterloo

• CHE 341 - Introduction to Process Control
  • Taught in 2023
• CHE 500 - Special Topics in Chemical Engineering
  • Taught in 2020, 2021
• CHE 521 - Process Optimization
  • Taught in 2024
• CHE 522 - Advanced Process Dynamics and Control
  • Taught in 2022
• CHE 524 - Process Control Laboratory
  • Taught in 2022

* Only courses taught in the past 5 years are displayed.

• Liñán, D.; Ricardez-Sandoval, L. Trends and perspectives in deterministic MINLP optimization for integrated planning, scheduling, control and design of chemical processes. Reviews in Chemical Engineering. https://doi.org/10.1515/revce-2024-0064
• Rangel-Martinez, D., Nigam, K., Ricardez-Sandoval, L. Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage. Chemical Engineering Research and Design 2021, 174: 414-441. https://doi.org/10.1016/j.cherd.2021.08.013
• Rangel-Martinez, D.; Ricardez-Sandoval, L. A Recurrent Reinforcement Learning Strategy with a Parameterized Agent for Online Scheduling of a State Task Network Under Uncertainty. Industrial & Engineering Chemistry Research 2025, 64, 13, 7126–7140. https://doi.org/10.1021/acs.iecr.4c04900
• Reynoso-Donzelli, S.; Ricardez-Sandoval, L. A. A Reinforcement Learning Approach with Masked Agents for Chemical Process Flowsheet Design. AIChE Journal 2024, 71 (1), e18584. https://doi.org/10.1002/aic.18584
• Reynoso-Donzelli, S.; Ricardez-Sandoval, L. A. An Integrated Reinforcement Learning Framework for Simultaneous Generation, Design, and Control of Chemical Process Flowsheets. Computers & Chemical Engineering 2025, 194, 108988. https://doi.org/10.1016/j.compchemeng.2024.108988
• Palma-Flores, O.; Ricardez-Sandoval, L. A. Integration of Design and NMPC-Based Control for Chemical Processes under Uncertainty: An MPCC-Based Framework. Computers & Chemical Engineering 2022, 162, 107815. https://doi.org/10.1016/j.compchemeng.2022.107815
• Liñán, D. A.; Ricardez-Sandoval, L. A. Optimal Design and Dynamic Transitions of Multitask Catalytic Distillation Columns: A Discrete-Steepest Descend Framework. Chemical Engineering and Processing - Process Intensification 2022, 180, 108655. https://doi.org/10.1016/j.cep.2021.108655
• Menon, K. G.; Fukasawa, R.; Ricardez-Sandoval, L. A. Integration of Planning and Scheduling for Large-Scale Multijob Multitasking Batch Plants. Ind. Eng. Chem. Res. 2024, 63 (2), 1039–1054. https://doi.org/10.1021/acs.iecr.3c02408
• Kamali, S., Ward, V. Ricardez-Sandoval, L. Dynamic modeling of recirculating aquaculture systems: effect of management strategies and water quality parameters on fish performance. Aquaculture Engineering 2022, 99, 102294. https://doi.org/10.1016/j.aquaeng.2022.102294
• Patrón, G. D.; Ricardez-Sandoval, L. Economically Optimal Operation of Recirculating Aquaculture Systems under Uncertainty. Computers and Electronics in Agriculture 2024, 220, 108856. https://doi.org/10.1016/j.compag.2024.108856
• Toffolo, K.; Meunier, S.; Ricardez-Sandoval, L. Optimal Operation of a Large-Scale Packed Bed Chemical-Looping Combustion Process Using Nonlinear Model Predictive Control. Fuel 2024, 357, 129876. https://doi.org/10.1016/j.fuel.2023.129876
• Usas, S. A.; Ricardez-Sandoval, L. An Optimal Sustainable Planning Strategy for National Carbon Capture Deployment: A Review on the State of CO Capture in Canada. The Canadian Journal of Chemical Engineering 2024, 102 (7), 2332–2351. https://doi.org/10.1002/cjce.25249
• Patron, G., Ricardez-Sandoval, L. An integrated real-time optimization, control, and estimation scheme for post-combustion CO2 capture. Applied Energy 2022, 308: 118302. https://doi.org/10.1016/j.apenergy.2021.118302
• Yu, Y.; Xia, W.; Yu, A.; Simakov, D. S. A.; Ricardez-Sandoval, L. Transition-Metal-Doped CeO2 for the Reverse Water-Gas Shift Reaction: An Experimental and Theoretical Study on CO2 Adsorption and Surface Vacancy Effects. ChemSusChem 2024, n/a (n/a), e202400681. https://doi.org/10.1002/cssc.202400681
• Chaffart, D.; Yuan, Y.; Ricardez-Sandoval, L. A. Multiscale Physics-Informed Neural Network Framework to Capture Stochastic Thin-Film Deposition. J. Phys. Chem. C 2024, 128 (9), 3733–3750. https://doi.org/10.1021/acs.jpcc.3c07168

• Profs awarded chairs and funds from Canadian govt.
• Research team transforms CO2 into valuable chemicals