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Llanes-Santiago O.,CUJAE | Prieto-Moreno A.,CUJAE | de Lazaro J.M.B.,CUJAE | Knupp D.C.,Nova Energy | Neto A.J.S.,Nova Energy
Chemometrics and Intelligent Laboratory Systems | Year: 2017

This paper presents a procedure proposed for the multiblock-based fault diagnosis in complex plants using fewer classifiers, while keeping the best performance indexes. Such proposal can be completely automated so algorithms to this aim are also included. In order to prove its feasibility, this procedure has been applied to the Tennessee Eastman Process test problem using classifiers based on the Maximum a Posteriori Probability (MAP), k-Nearest Neighbors (kNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). © 2017 Elsevier B.V.

Salazar P.,ESPE | Ayala P.,ESPE | Jimenez S.G.,CUJAE | Correa A.F.,CUJAE
2013 25th Chinese Control and Decision Conference, CCDC 2013 | Year: 2013

This paper studies DC-to-DC buck-boost converter, as well as of the most important features asociated with the sliding mode control, such as adding robustness to the system with respect to variations of its parameters and external disturbances applied to this converter. © 2013 IEEE.

Acosta Diaz C.,Center for Mathematical Studies | Camps Echevarria L.,Center for Mathematical Studies | Prieto-Moreno A.,CUJAE | Silva Neto A.J.,State University of Rio de Janeiro | Llanes-Santiago O.,CUJAE
Chemical Engineering Research and Design | Year: 2016

Nonlinear bioreactors are considered essential technology in chemical and biochemical industries. This paper presents a proposal of a robust model based fault diagnosis in a nonlinear bioreactor, formulated as the solution of an inverse problem. The optimization problem is solved by using four different evolutionary strategies: Particle Swarm Optimization (PSO), Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Particle Swarm Optimization with Memory (PSO-M), with DE resulting the best according to the evaluated quantitative indicators. The results obtained with this approach indicate advantages in comparison to other methods of fault diagnosis (FDI) present in literature. © 2016 The Institution of Chemical Engineers

Bernal-de-Lazaro J.M.,Reference Center for Advanced Education | Llanes-Santiago O.,Cujae | Prieto-Moreno A.,Cujae | Knupp D.C.,State University of Rio de Janeiro | Silva-Neto A.J.,State University of Rio de Janeiro
Chemical Engineering Science | Year: 2016

The conventional SPE and Hotelling[U+05F3]s T2 statistics may not work properly in the detection of incipient and small-magnitude faults. In this paper, an enhanced dynamic Multivariate Statistical Process Control approach is proposed, which combined with the dimension reduction techniques KPCA and KICA improved the detection of these types of faults. In the parameters choice task two metaheuristic algorithms were used. The kernel optimization criterion used involves the computation of the False Alarm Rate (FAR) and False Detection Rate (FDR) indicators, unified by the Area Under the ROC Curve (AUC). The proposal was tested with excellent results on the Tennessee Eastman (TE) process. © 2016 Elsevier Ltd.

Prieto-Moreno A.,CUJAE | Camara L.D.T.,State University of Rio de Janeiro | Llanes-Santiago O.,CUJAE | Neto A.J.S.,State University of Rio de Janeiro
CMES - Computer Modeling in Engineering and Sciences | Year: 2015

This work deals with a statistical approach to the uncertainty propagation analysis when estimating the kinetic mass transfer parameters used to model a chromatographic column in the Simulated Moving Bed. The chromatographic column modeling was performed using the new front velocity approach. The uncertainty propagation analysis of operational factors intervening in the chromatographic process to estimated parameters was made using the response surface methodology. The application of the factorial experimental design allowed us to establish those operational factors showing a greater influence on continuous chromatography. Besides, the chromatographic regions, where factors cause a greater output variation as well as their respective patterns, were determined. The analysis was applied to the separation of glucose and fructose. © 2015 Tech Science Press.

Prieto-Moreno A.,CUJAE | Llanes-Santiago O.,CUJAE | Garcia-Moreno E.,Polytechnic University of Valencia
Journal of Process Control | Year: 2015

The Principal Component Analysis is one of most applied dimensionality reduction techniques for process monitoring and fault diagnosis in industrial process. This work proposes a procedure based on the discriminant information contained in the principal components to determine the most significant ones in fault separability. The Tennessee Eastman Process industrial benchmark is used to illustrate the effectiveness of the proposal. The use of statistical hypothesis tests as a separability measure between multiple failures is proposed for the selection of the principal components. The classifier profile concept has been introduced for comparison purposes. Results show an improvement in the classification process when compared with traditional techniques and the StepWise selection. This has resulted in a better classification for a fixed number of components, or a smaller number of required components to obtain a prefixed error rate. In addition, the computational advantage is demonstrated. © 2015 Elsevier Ltd. All rights reserved.

Association rule mining is a very popular data mining technique. Rules in this technique are often used to identify and represent dependencies between attributes in databases. Specifically, fuzzy association rules are rules that use the concepts of fuzzy sets and can be considered as a special case of fuzzy predicates. Many quality measures have been defined for fuzzy association rules, but all consider a specific structure: antecedent and consequence. In the case of fuzzy predicates in the normal form (i.e., conjunctive or disjunctive), it is necessary to define different quality measures that do not consider the structure as an antecedent or a consequence. The only available measure for this scenario is the fuzzy predicate truth value (FPTV), which has serious limitations. The evaluation of fuzzy predicates in the normal form through appropriate quality measures has not yet been clearly defined in the literature. Thus, we propose several quality measures specifically for fuzzy predicates in the conjunctive (CNF) and disjunctive (DNF) normal forms. Experimental studies illustrate the use of the proposed measures and allow some general conclusions about each measure. © 2014, Revista Ingenieria e Investigacion - Editorial Board. All Rights Reserved.

Vazquez K.,ITESM Juarez City | Cordova K.,ITESM Juarez City | Gonzalez A.I.,CUJAE
Proceedings - 2015 International Conference on Mechatronics, Electronics, and Automotive Engineering, ICMEAE 2015 | Year: 2015

The objective of the study is to determine the efficiency of Integral of the Time-Weighted Absolute Value of the Error (ITAE) and Integral of the Absolute of the Error (IAE) methods as a way of tuning controllers for a functional electrical stimulation cycle ergometer system. These systems provide aid for people with a damaged spinal cord. The methodology was to find a mathematical model to describe the behavior of the individual at different points of operation and to describe the cycle ergometer as a secondary system. Two models were obtained via experimentation. A proportional-integral-derivative (PID) action multi-controller for each operation points was tuned. The results were validated by simulation and experimentation respectively. Due to the actual physical aspects, the electromechanical system will be improved and will require further investigation in order to find a generic or auto tuning controller for an integrated system. © 2015 IEEE.

Quinones-Grueiro M.,CUJAE | Prieto-Moreno A.,CUJAE | Llanes-Santiago O.,CUJAE
Industrial and Engineering Chemistry Research | Year: 2016

Usually, industrial processes have multiple operational modes due to different production strategies, external environmental variability, or changes in product specifications. Monitoring of multimode processes constitutes a challenging problem considering multiple steady-state operational regions and dynamic transitions. This paper proposes a novel method for the offline identification of stable modes and transitions based on a local kernel density estimation algorithm. The online monitoring scheme is based on mode identification and transition tracking. The Tennessee Eastman (TE) benchmark process is used as a case study to evaluate the performance of the proposal. As a result, stable modes are successfully isolated from transitions, even when these involve complex changes in the production mode. The results also demonstrate that the proposed scheme is capable of tracking mode changes, and finally, results monitored during transitions confirm the validity and efficacy of the new approach compared with previous works. © 2015 American Chemical Society.

Avila D.R.,Ecole Polytechnique Federale de Lausanne | Garcia S.P.,CUJAE | Marrero Y.P.,CUJAE | De Vera A.S.,CUJAE | And 3 more authors.
2016 10th European Conference on Antennas and Propagation, EuCAP 2016 | Year: 2016

This paper focuses on the design of two CPW-fed slot multiband antennas. The first prototype works on four bands (2.4, 3.5, 5.2 and 5.8 GHz), and is printed on a 30×32mm2 FR-4 substrate with thickness of 1.5mm. The surface current distribution for each frequency is analyzed to introduce the different slots in the antenna. Following the same design procedure, the second antenna was obtained, this time for five bands (the four bands mentioned above and 1.7 GHz) and it was printed on 30×30mm2 of the same substrate. The measured and simulated results prove that the proposed antennas are suitable for LTE, M-WiMAX, Bluetooth, GSM and WLAN applications. © 2016 European Association of Antennas and Propagation.

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