Monitoring of an industrial dearomatisation process

Process monitoring methods have been studied widely in recent years, and several industrial applications have been published. Early detection and identification of abnormal and undesired process states and equipment failures are essential requirements for safe and reliable processes. This helps to reduce the amount of production losses during abnormal events. In this paper, statistical multivariate methods and neural networks applied in monitoring of an industrial dearomatisation process are compared. No appriori process knowledge for the methods were assumed. The data for the comparison were generated with a dynamic simulator model of the process. Special emphasis was put on a case of internal leak in a heat exchangerPeer reviewe

An online application of dynamic PLS to a dearomatization process

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 online-monitoring application was successful in real-time process monitoring and it fulfilled the industrial requirements. PACS: 07.05.Mh; 07.05.Tp; 83.85.NsPeer reviewe

FTC based on data driven FDI for a dearomatisation process

In this paper, a fault tolerant control (FTC) system based on data driven fault detection (FDI) is presented. The behaviour of the system with proactive and reactive FTC strategies is studied in the presence of faults in an online product quality analyser with a simulated dearomatisation process operated under model predictive control (MPC). The performance of the system is validated onsite at the Neste Oil Oyj Naantali refinery. It is shown, that the inherent accommodation properties and model information in the studied MPC provide means to realise the proposed types of FTC strategies as confirmed both by simulation and the real process results. It is also shown that similar results are achieved within a simulated and the real process environments.Peer reviewe

Fault detection and isolation of an online analyzer for an ethylene cracking process

Fault 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.Peer reviewe