Performance monitoring and root-cause diagnosis of industrial model predictive control systems for sustainable energy production

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Industries are facing significant challenges on sustainable profits, resources, energy and environment. As the most popular advanced control technology, model predictive control (MPC) has been widely applied to different industries including energy, chemical, materials, mining, power, pulp & paper, and automotive industries. Significant economic profits, energy savings and environmental pollution reductions have been achieved from industrial MPC technology. However, MPC performance often encounters significant degradation, which can cause serious safety incidents, profit losses, reduced energy efficiency and increased environment pollutions in industrial productions. Currently, there are no comprehensive and effective solutions to monitor and particularly diagnose industrial MPC performance. This research project is aimed to develop the novel state-of-the-art techniques for monitoring complex MPC systems and further diagnosing the root causes of performance degradation. With the accurate and reliable diagnosis findings, corrective actions can be automatically taken to optimize the process operation with the best profitability and energy efficiency as well as the lowest environmental emissions and carbon footprints. Moreover, the MPC monitoring and diagnosis system will be integrated into MPC life cycle to facilitate and improve MPC design, commissioning and maintenance in industrial processes. The developed technology will be demonstrated in energy production processes at Shell and further be extended to different kinds of industries as generic technical solutions. It is anticipated that the research inventions of this project will significantly benefit a wide range of industries in Canada with considerable economic, environmental and social values, which include higher productivity, boosted energy efficiency, mitigated carbon emissions and reduced safety incidents. In addition, this research will make substantial knowledge contributions to chemical engineering and particularly process control field with the new findings on monitoring and control theories as well as complex system sustainability.