• To what extent do you see the deployment of digital approaches to maintain and operate facilities while leveraging artificial intelligence (AI) in the restructured operations of the petrochemical industry?



  • Geannie Gardner, KBC (A Yokogawa Company), geannie.gardner@kbc.global

    Digital technologies are significant enablers of smart manufacturing throughout petrochemical enterprises, from plant floor to boardroom. Consider some examples:
    • Looking from the bottom up IIoT sensors, coupled with robotics, drone technology, and AI/ML, enable plant managers to change how rotating equipment is monitored and maintained. Now, operators are freed from daily inspection rounds because the sensors plus AI flag early warnings of trouble, allowing robotics to guide visual inspections.

    Reliability improves with continual monitoring. Combining predictive AI algorithms with better data directs maintenance efforts to where it is needed, which leads to fewer unplanned shutdowns.

    • Operator roles change Additional sensor information and AI guidance are accessible via dashboards and 3D visualisations, readily available simulations (first principles, ML-based or hybrid), plant knowledge bases, and so on. This data provides rich insight into plant performance. Therefore, operators work with true digital twins of their plant, and their role evolves to monitoring, reviewing, and approving the outputs of the various AI/ML applications.

    • More flexibility to operations scheduling AI-driven algorithms married to new analytic techniques can be applied in plant control systems, allowing for faster switches between product grades.

    • Looking top-down Digitalisation gives organisations consolidated access to information, cutting through traditional silos. This, in turn, allows more automated workflows and hence increased business agility. For example, common scenarios around supply and demand opportunities can be examined by AI-enabled workflows, with planners and schedulers reviewing recommendations rather than running the analyses themselves. Accepted recommendations can be implemented automatically from the ERP system to plant floor, increasing responsiveness.

    • Travelling the industrial autonomy journey The opportunity to be fully achieved involves digitising the human experience and knowledge, then coding this data to analyse key decisions, assess the effectiveness of human machine interfaces, and capture key learnings. Manufacturers that embrace these rapidly developing digital technologies and techniques, including AI/ML, are likely to survive and thrive in this volatility, uncertainty, complexity, and ambiguity (VUCA) world. As a result, they should be able to operate more nimbly, with more empowered workforces and report greater returns on capital.



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