Mixture model based batch process monitoring and fault diagnosis for sustainable manufacturing

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Industrial manufacturing is facing significant challenges on sustainable resources, energy, environment and profits. Batch or semi-batch processes have been widely applied to different industries including energy, chemical, materials, semiconductor, pharmaceutical, biotechnology and food industries for producing high-value-added products. Therefore, it is critically important to develop reliable monitoring systems of batch processes with strong fault detection and diagnosis capabilities in order to ensure safe, profitable and environmentally sustainable operation in complex industrial manufacturing. Typically batch processes have inherent features including multiplicity of operating phases, phase based shifting dynamics, between-phase transient behaviors, process nonlinearity and non-Gaussianity, batch-to-batch variations, and uncertainty of phase divisions. All these characteristics constitute significant challenges for effective batch process monitoring and diagnosis in industrial practice. This research project is aimed to develop the novel kernel mixture model and Bayesian statistics based smart monitoring techniques for abnormal event detection, fault propagation identification and root cause diagnosis of multiphase batch processes. With the adaptive monitoring and diagnosis approaches, multiphase batch processes can be automatically maintained and optimized with the best safety, productivity, quality and energy efficiency as well as the lowest environmental emissions and carbon footprints. The developed technology will be demonstrated in manufacturing facilities at Dow Chemical and further be extended to different industries as generic technical solutions. It is anticipated that the research findings and inventions of this project will benefit a wide range of industries in Canada with significant economic, environmental and social values such as improved productivity, increased energy efficiency, reduced carbon emissions and mitigated safety incidents. Moreover, this research will make substantial knowledge contributions to chemical engineering and particularly process systems engineering fields with new methodologies on process monitoring, fault diagnosis and process sustainability.