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Coimbra, Portugal

Brasio A.S.R.,University of Coimbra | Romanenko A.,Ciengis SA | Santos L.O.,University of Coimbra | Fernandes N.C.P.,University of Coimbra
Bioresource Technology | Year: 2011

The transesterification reaction models available in the literature are valid only for one particular mixing condition. In this work, a modeling strategy is presented in order to predict the effect of mixing conditions in the transesterification process. The proposed methodology was applied to independent sets of experimental data available in the literature that show the dependency of the transesterification reaction on the frequency of rotation of the stirrer. The accuracy of the developed models corroborates the validity of the proposed modeling approach. © 2011 Elsevier Ltd. Source


Brasio A.S.R.,University of Coimbra | Brasio A.S.R.,Ciengis SA | Romanenko A.,University of Coimbra | Fernandes N.C.P.,University of Coimbra
Applied Mathematics and Information Sciences | Year: 2015

System identification plays an important role in the development of process simulators and controllers. The ability to determine correctly the model parameters directly affects the model quality and, therefore, the model based controller performance. This work details the development of a system identification approach and its computational implementation based on sequential quadratic programming (SQP) in which first and second order linear systems, represented in state-space, are identified from simulated and from real industrial process data. Both single-input single-output and multivariable processes are considered. The resulting optimization problem may become not trivial to solve as one of the examples illustrates. It is shown how a rescaling of the decision variables or the use of a priori process knowledge may be used in order to overcome the difficulties and to improve the quality of the results. © 2015 NSP. Source


Brasio A.S.R.,University of Coimbra | Brasio A.S.R.,Ciengis SA | Romanenko A.,Ciengis SA | Fernandes N.C.P.,University of Coimbra
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Stiction is a major problematic phenomenon affecting industrial control valves. An approach for detection and quantification of valve stiction using an one-stage optimization technique is proposed. A Hammerstein Model that comprises a complete stiction model and a process model is identified from industrial process data. Some difficulties in the identification approach are pointed out and strategies to overcome them are suggested, namely the smoothing of discontinuity points. A simulation study demonstrates the application of the proposed technique. © 2014 Springer International Publishing. Source


Brasio A.S.R.,University of Coimbra | Brasio A.S.R.,Ciengis SA | Romanenko A.,Ciengis SA | Fernandes N.C.P.,University of Coimbra
AIP Conference Proceedings | Year: 2012

The work concerns the system identification of industrial processes via the Sequential Quadratic Programming algorithm. The proposed approach, testing scenarios, and the system identification results are discussed. The tool is tested with two datasets, the first one collected in loco from an industrial process and the second one generated with a plant simulator of a continuous stirred tank reactor, a system widely used in industry. In both cases, the resulting models capture well the process dynamics. © 2012 American Institute of Physics. Source


Brasio A.S.R.,University of Coimbra | Brasio A.S.R.,Ciengis SA | Romanenko A.,Ciengis SA | Fernandes N.C.P.,University of Coimbra
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2015

Stiction is a persistent control valve problem in the process industry responsible for oscillations and, consequently, losses of productivity. Its early detection and separation from other oscillation causes is an important issue in the industrial context. One of simple and effective approaches to detect stiction has been proposed by Yamashita that employed a pattern recognition principle. While its performance is good in flow control loops, it fails to properly diagnose other types of processes. The present work details a new approach that enables the application of the Yamashita pattern recognition principle to level and other integrating process control loops. A simulation study demonstrates its capabilities in clean and noisy environments and analyzes the impact of the noise on the diagnostic performance. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Source

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