engleski

Using the PI System in Chemical Engineering Education and Research

Fast development, new paradigms, intelligent devices and large amounts of data from industrial processes are constantly bringing new challenges. This has been recognized at the University of Zagreb where The Laboratory for Automation and Measurement deals with research and development in the fields of basic and advanced measurements, monitoring and diagnostics, process control, modelling and optimization. This contribution illustrates how chemical engineering students utilize the PI process information system and predictive analytic tools in a real-plant data analysis. In addition, it describes application of the PI tools for refinery heat exchanger diagnostics. Neural network-based models are intended for fouling detection and heat exchanger performance monitoring, enabling predictive condition-based maintenance. The template and related dashboard has been built utilizing the functionalities of the PI Asset Framework and the latest PI visualization tools.

Heat exchanger ; Diagnostics ; Neural network ; Fouling

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