Decision support systems based on heterogeneous data driven models for a safe and optimal operation of industrial process systems

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Process industries including chemicals, oil and gas industries, pharmaceutics, mining, metals and pulp and paper play an important role in the Canadian economy. Today, operators are involved only in expectational circumstances because processes are autonomous under normal operating conditions. This disconnection is termed "the irony of automation". A higher level of autonomy of industrial process systems is needed to resolve this paradox. Recent advances in autonomous operations such as autonomous robots and self-driving cars have opened up completely new ways to deal with little or unstructured input information and to generate a large range of reactions in highly uncertain environments. This has in turn greatly increased public acceptance of autonomous systems. Fully autonomous operation of process systems is a complex problem because unlike autonomous driving, it requires multiple autonomous key features corresponding to multiple human roles (control room operations, field operation, process optimization and production scheduling) instead of a single one, the car driver. Some of the necessary underpinning technologies (Industrial Internet of Things, Big data, High-performance computing and novel sensing technologies) have only recently emerged. A recent survey released by Yokogawa finds two-thirds of process industry companies are anticipating fully autonomous operations by 2030. Within this context, the long-term objective of the research program is to create virtual control room assistants providing operators with early detection of plant anomalies, accurate diagnosis and suggestions of corrective actions. For this purpose, fundamental problems related to process models interpretability and online portability will be solved within this Discovery Grant with specific solutions tailored to real-world process systems and demonstrated in industrial trials via subsequent Alliance grants. Through four PhD and three MSc projects over the next five years, this research program will achieve a high impact by working closely with industrial partners to achieve the following short-term objectives: (1) Extract online portable and interpretable surrogate models from first principles models (impact: increase execution speed). (2) Predict process abnormal episodes (impact: increase robustness) (3) Integrate nominal and abnormal episodes process models into a single framework (impact: facilitate industrial uptake). The aforementioned survey indicates that 54% of respondents expect to increase their autonomy investment over the next 3 years as a direct result of COVID-19. The outcome of this research program will be instrumental in the development of online operator decision support systems. Using these systems will lead to (1) Reduced production costs through improved efficiency and higher plant availability (2) Improved end-product quality through advanced analytics (3) Safer working environments through remote operations.