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Tiszaújváros, Hungary

Farsang B.,University of Pannonia | Balogh I.,Tisza Chemical Group Public Ltd Company | Nemeth S.,University of Pannonia | Szekvolgyi Z.,Tisza Chemical Group Public Ltd Company | Abonyi J.,University of Pannonia
Chemical Engineering Transactions | Year: 2015

Melt flow index (MFI) is a very important property of thermoplastic polymers. Laboratory measurements follow standard methods (ASTM D1238 or ISO 1133) to determine MFI and give accurate values, but these measurements are available only in 2-4 hour sampling intervals. Using soft sensors real time estimation of MFI is available for process control and monitoring. When detailed knowledge about the process is not available, data-driven soft sensors can be applied. In this case historical process data are used to build statistical models to determine the relationship between inputs and outputs. Since these methods are based on measurements, the performance of soft sensor depends on the quality of data. Measurements are always affected by errors so pre-processing of data should be necessary. The measurement noise and process variable can be correlated with each other so one opportunity to improve measurement accuracy is using multivariate statistical methods (PCA, PLS). Statistical methods can be improved when phenomenological knowledge is taken into account (e.g. balance equations). The aim of the presented research is to propose a methodology to support the data-driven development of process monitoring systems. We developed a method which improves the effectiveness of data based MFI soft sensor. This method includes advantages of a priori knowledge based models and data-driven multivariate statistical process monitoring tools. As a case study we developed a soft-sensor to estimate MFI of the products of industrial polypropylene reactor at TVK Plc. The proposed method is able to detect undesirable operation states and it can be used for fault detection. Copyright © 2015, AIDIC Servizi S.r.l. Source


Farsang B.,University of Pannonia | Gomori Z.,Tisza Chemical Group Public Ltd Company | Horvath G.,Tisza Chemical Group Public Ltd Company | Nagy G.,Tisza Chemical Group Public Ltd Company | And 2 more authors.
Chemical Engineering Transactions | Year: 2013

We present a model based algorithm for simultaneous validation of online analysers and process simulators. Reconciled on-line and historical process data satisfying balance and model equations provides the opportunity to validate and improve process models and soft sensors. Accurate simulation results and laboratory measurements can be used for the validation of online analysers. Validated and reconciled data can be used to the iterative and interactive identification of the unknown parameters of the simulator, e.g. for the determination of kinetic parameters. This method can also be used for monitoring and diagnostics of complex processes because situations when the operating conditions have been significantly changed can be discovered. The approach is illustrated by the analysis of an industrial hydrogenation system. We present the proposed iterative and interactive procedure of model development and analyser validation, the applied data reconciliation method, and the details of the case study. The results show the applicability of the proposed scheme in industrial environment and the benefits of the extracted information in the maintenance and monitoring of advanced model based process engineering tools. The developed tool can increase operating efficiency that is the key of reducing energy consumption and environmental impact. This is especially true in hydrocarbon industry where the operation of the technology is supported by process simulator and on-line analyser based advanced process control systems. Copyright © 2013, AIDIC Servizi S.r.l. Source

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