Advanced Multiscale Decision-making for Process Operations and Energy Systems, and Incorporating Big Data Analytics

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Across all sectors within the energy and process industry, tremendous efforts have been devoted towards the development and operation of agile manufacturing techniques to respond to customer needs and volatile markets while at the same time control costs, improve efficiency, and reduce pollution. This has created a demand for systems to solve complex integrated planning and scheduling problems that bridge the gap between the different functional and strategic decision-making levels. These challenging multiscale decision problems require more efficient computational tools and solution strategies. The goal of this research program is to develop new techniques aimed to obtain more competitive integrated solutions in a timely manner. Although progress (including past research efforts by the principal investigator) has been made in dealing with some of the combinatorial aspects of the problems cited above, significant research efforts are still required to overcome these multiscale challenges. This research program aims at devising improved solution algorithms that integrate logic-based methods, decomposition techniques, stochastic optimization, and reformulation strategies. Since most of the existing methodologies are problem-specific or applicable only to short time horizons, clustering will be investigated as a potential tool to effectively shrink model size and achieve computational tractability while at the same time maintaining solution accuracy. Other approaches that this research program will consider is the use of continuous time scales, rolling horizon strategies, and surrogate models that employ big data analytics.Two broad application domains will be considered: one focuses on process operations and enterprise-wide integrated facilities and the other deals with advanced energy production systems. The challenges of intermittence of renewable energy will be addressed through several grid stabilization approaches, including energy storage, electrification of the process industry, and conversion of surplus electricity to useful products through Power-to-X technologies. Applications of the developed computational tools include making design or operating decisions, selection of system components, interconnections of units, or even interactions along supply chains or the ecosystem. The program will provide training for several graduate and undergraduate students, exposing them to advanced techniques in the practical solution of large multiscale decision problems, and has the potential to significantly impact tomorrow's process and energy industry with tremendous economic benefits to Canada. Students involved in the research program will be well-suited for the many positions that are currently available in the area of process systems engineering in both industry and academia.