Grant: Canada Research Chairs

Climate change is a major global issue affecting the environment and our day-to-day activities. Global warming is particularly alarming in Canada since the average temperature is rising at twice the global average. Combined with this effect, the world’s energy demands are expected to continuously increase thus requiring the development of new attractive and clean energy production systems that do not generate Carbon Dioxide (CO2) as a by-product, which is a greenhouse gas that contributes to the rise in average global temperatures. Dr. Ricardez-Sandoval’s Canada Research Chair (CRC) research program aims to further advance the technological development of Chemical Looping Combustion (CLC), which, in contrast to the existing energy intensive and expensive CO2 capture technologies (e.g. post-combustion systems), is an emerging technology that can produce energy and capture CO2 emissions simultaneously. This process requires a material that can transport oxygen within the system (i.e. oxygen carrier-OC). Despite the benefits, this technology imposes a series of challenges that must be addressed before its global acceptance and eventual commercialization. This CRC program pursues two specific goals: 1) to introduce novel and efficient process designs for a particular type of CLC system: multistacked packed bed reactors (i.e. reactors that contain multiple layers of OC); and 2) to develop a new computational framework that can accelerate the design of OC materials. The first goal will be accomplished through a new optimization framework that can solve problems involving ordered set of discrete decisions combined with highly nonlinear expressions in acceptable computational times. To achieve the second goal, a machine learning framework based on few-shot learning (a method that requires limited data for training) will be developed and used to make fast prediction of the energies associated with those elementary reactions that limit the performance of a set of OC materials. While the first goal will produce novel, economically attractive designs never considered before for CLC, the second goal will return a machine learning tool that could be used to accelerate computer-aided material design not only for CLC applications, but also for heterogeneous systems. This research will thus accelerate commercialization of CLC as a viable technology for clean power generation. Outcomes from this research will also be attractive to chemical manufacturing and energy sectors.