Dr. Luis Ricardez-Sandoval

Viernes 16 de noviembre 2018, 13:00 hrs. Auditorio “Antonio Rodríguez” Módulo E CUCEI

Fotogalería
Constancia

Dr. Luis Ricardez-Sandoval.png

Dr. Luis Ricardez-Sandoval is a Canada Research Chair in Multiscale Modelling and Process Systems (CRC-Tier II) and an Associate Professor in the Department of Chemical Engineering at the University of Waterloo. He received his B.Sc. from Technological Institute of Orizaba (Mexico) in 1997, his M.Sc. from Technological Institute of Celaya (Mexico) in 2000, and his Ph.D. degree from the University of Waterloo in 2008. During his academic career, Dr. Ricardez-Sandoval has published more than 90 journal articles, 25 full-length peer-reviewed conference papers, 3 book chapters and 1 monograph. His current research interests include optimal design and operations management of dynamic systems under uncertainty; multiscale modelling and control of thin film deposition and heterogeneous catalytic systems under uncertainty; design and operation of CO2 capture and advanced power generation technologies with near-zero emissions and process scheduling of industrial-scale systems. Dr. Ricardez-Sandoval’s research has been supported by a network of collaborators from the federal government, private companies and the government of Ontario. Dr. Ricardez-Sandoval has received multiple awards including the 2015 Ontario Early Researchers Award (ERA) and the 2015 Engineering Research Excellence Award. Dr. Ricardez-Sandoval is an Associate Editor of the Canadian Journal of Chemical Engineering (CJChE) and is a member of the Systems and Control division of the Chemical Institute of Canada (CIC) and the American Institute of Chemical Engineers (AIChE).


Simultaneous design, control and scheduling of dynamic systems under uncertainty: A new back-off approach

Optimal process design is one of the most traditional and challenging tasks performed by Chemical Engineers. While this activity is typically conducted using steady-state calculations, it has been widely recognized that process performance under operation is significantly affected by decisions made at the earlier stages of the design. Integration of design and control, also known as simultaneous design and control, is an emerging approach that aims to take into account the dynamic performance and controllability of the system while performing the optimal process design. Identification of critical scenarios that are likely to affect the system during operation, model uncertainty and tractability of large-scale systems are key issues that need to be considered for integration of design and control. Moreover, recent studies have shown that, for certain applications, scheduling decisions will positively impact process economics if they are performed at the design stage.

This talk will present the recent efforts performed in our research group to address simultaneous design, control and scheduling of chemical systems in the presence of disturbances and model parameter uncertainty. A new approach for integration of design and control based on the concept of back-off will be presented. In the context of integration of design and control, the back-off approach aims to specify an optimal design and control scheme by moving away from the optimal steady-state design in a systematic fashion. The proposed back-off methodology involves the successive identification of Power Series Expansion (PSE) functions, which are used to represent the process constraints and cost function considered in the formulation. These PSE-based functions are used to seek for the values in the decision variables that can specify the search direction on which the optimal steady-state design needs to be moved (backed-off) to ensure dynamic feasibility in the presence of disturbances and model uncertainty. The proposed back-off methodology can consider discrete and probabilistic-based uncertainty descriptions. A dynamic optimization framework for the integration of design, control and scheduling for multi-product systems will also be presented. A decomposition algorithmic framework that involves the iterative solution of dynamic flexibility and feasibility optimization formulations in the presence of uncertainty and disturbances have been proposed and used to illustrate the benefits of taking scheduling decisions into account at the design stage. Case studies showcasing the benefits of the integrating approaches discussed in this talk will be presented and used as a motivation to develop new innovative approaches for optimal design and operations management under uncertainty.