Early detection of process disturbances and prediction of malfunctions in process equipment improve the safety of the process, minimize the time and resources needed for maintenance, and increase the uniform quality of the products. The objective of online-monitoring is to trace the state of the process and the condition of process equipment in real-time, and to detect faults as early as possible.In this article the different properties of the online-monitoring methods applied in the process industries are first reviewed. A description of the systematic development of the online-monitoring system for an industrial dearomatization process, specifically for flash point and distillation curve analysers, is then presented. Finally, the results of offline and online tests of the monitoring system using real industrial data from the Fortum Naantali Refinery in Finland, are described and discussed. The developed onlinemonitoring application was successful in real-time process monitoring and it fulfilled the industrial requirements. PACS: 07.05.Mh; 07.05.Tp; 83.85.Ns

The digital twin offers a potentially powerful way of using simulation to support business and change the way industrial operations are done. The idea of the digital twin is not new but recent changes in information technology make implementation of digital twins a natural next step in the application of simulation technologies. Simulation practitioners will find that their models are increasingly embedded in complex systems that combine simulations with operational data to solve a business problem. However, the successful adoption of this approach is challenging. This paper asks the question: "How can digital twins be made sustainable, maintainable and useful?". We focus primarily on the development of twins in the oil and gas industry. Most academic work in this area has been done in the manufacturing industries. We review this literature and propose a simple model of digital twins. This allows us to identify challenges with current implementations and propose a research agenda that will allow future twins to be sustainable, maintainable and usable.

Author 's accepted manuscript, published in Control Engineering Practice 16 (2008) AbstractFault diagnosis methods based on process history data have been studied widely in recent years, and several successful industrial applications have been reported. Improved data validation has resulted in more stable processes and better quality of the products. In this paper, an on-line fault detection and isolation system consisting of a combination of principal component analysis (PCA) and two neural networks (NNs), radial basis function network (RBFN) and self-organizing map (SOM), is presented. The system detects and isolates faulty operation of the analyzers in an ethylene cracking furnace. The test results with real-time process data are presented and discussed.

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Commercial process simulators are increasing interest in the chemical engineer education. In this article, the use of commercial dynamic simulation software, D-SPICE® and K-Spice®, for three different chemical engineering courses is described and discussed. The courses cover the following topics: basic chemical engineering, operability and safety analysis and process control. User experiences from both teachers and students are presented. The benefits of dynamic simulation as an additional teaching tool are discussed and summarized. The experiences confirm that commercial dynamic simulators provide realistic training and can be successfully integrated into undergraduate and graduate teaching, laboratory courses and research.