Optimal design, scheduling and control of chemical processes and emerging energy systems for sustainable and flexible operations under uncertainty

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In today's very competitive and highly interconnected society, companies are required to operate at near-optimal conditions, where operations management is driven primarily by external factors and system uncertainty. With the continuous rise in energy demands, and the urgent need to reduce CO2 emissions, fossil-fired power systems are required to meet tight safety, environmental and economic constraints. However, there are challenges to maintaining sustainable and flexible operation in the presence of changes in external factors. Power plants, for example, struggle to adapt to imminent penetration of clean renewable energy conversion systems in the market. Thus, there is great incentive to improve the design and operations management in existing power plants and, in parallel, develop new clean, affordable and commercially-viable fossil fuel systems. ******This research program aims to develop novel theoretical and computational tools that can be used for optimal design and operations management in chemical systems emerging in the manufacturing and energy sectors, both areas of strategic importance to Canada. Studies have shown that scheduling decisions can improve system economy and sustainability when they are combined with process design and control decisions. Due to their large computational costs, the very few methods currently available to optimize these three factors cannot be implemented in large-scale applications where scheduling decisions are truly key. This program will advance the development of a new method that addresses optimal design, control and scheduling for large-scale applications under uncertainty using state-of-the-art discretization schemes. Novel uncertainty quantification methods based on an new sampling method, i.e. Multi-level Monte Carlo (never before considered for process optimization), will also be developed. ******In parallel, this program will also advance chemical looping combustion (CLC), an emerging carbon capture and storage (CCS) technology with the potential to replace conventional (energy intensive) CCS systems. Specifically, we will develop a new dynamic model for a pilot-scale CLC application at pressurized conditions. This model will be instrumental to conduct optimal design and operations management studies using the advanced computational tools described above for optimal design, control and scheduling. Also, the performance of the pressurized CLC system will be compared to other CLC applications to evaluate CO2 capture costs, plant's efficiency, process scale-up and eventually, their potential integration to advanced power generation systems. Ultimately, this research will produce advanced algorithmic frameworks for optimal design and operations management in large-scale systems emerging in the manufacturing and energy sectors and pave the way to reveal the potential of CLC to replace existing CCS technologies.