2018 - PI World - Barcelona - Academic Symposium
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.
- Oil & Gas
- Pharmaceuticals & Life Sciences
University of Zagreb, Faculty of Chemical Engineering
Prof. Nenad Bolf, PhD, is The Head of The Department of Measurement and Process Control at The Faculty of Chemical Engineering and Technology, University of Zagreb. He is the holder of the Measurement and Process Control, Process modelling and control, Plant automation and Advanced process control courses. His areas of interest include modelling, diagnostics, process control, and optimization, with special interest in advanced process control and artificial intelligence methods. He is an advisor for process control and control loop optimization in numerous projects with the process industry. For the last four year he is editor-in-chief of Chemistry in Industry magazine.
University of Zagreb, Faculty of Chemical Engineering and Technology
Željka Ujević Andrijić
Ms. Željka Ujevic Andrijic, PhD, is a senior assistant at the University of Zagreb, Faculty of Chemical Engineering and Technology. Areas of her scientific research include: first-principle and data-driven modelling, process optimization, and statistical analysis. In 2012 she defended her PhD on soft sensor models for refinery application using system identification and global optimization methods. She participates in teaching courses in Computer programming and Process modelling and helps during student work and doctoral thesis. She is an author of a larger number of scientific and professional papers and case studies.