Modelado de sistemas complejos mediante métodos de agrupamiento e hibridación de técnicas inteligentes

El presente trabajo de investigación aborda el estudio y desarrollo de un sistema de modelado híbrido que combina métodos de agrupamiento estándar, o clustering, con algoritmos de regresión. Con esta propuesta, se pretende dividir el problema de modelado de un sistema en un conjunto de modelos locales. De esta forma se pueden definir zonas con un comportamiento similar de un modo más preciso. Durante la etapa de regresión, se aplican varias técnicas sobre cada uno de los grupos, con el fin de lograr la mejor aproximación en los modelos locales obtenidos. Por tanto, el modelo híbrido estará formado por el conjunto de todos estos modelos. Esta novedosa propuesta permite obtener resultados altamente satisfactorios en todos los procesos reales en los que se ha aplicado. El sistema desarrollado ha sido validado sobre tres supuestos reales diferentes. En el primero de ellos, el modelo híbrido se emplea para obtener o predecir el valor que debiera medir un sensor para poder realizar detección de fallos. La aplicación real utiliza la señal BIS, que se emplea para determinar el grado de hipnosis de un paciente sedado. En el segundo, el modelo propuesto se utiliza para crear un sensor virtual, obteniendo el valor de una variable a partir de otras. La aplicación real, en este caso, se desarrolla sobre un sensor para monitorizar el estado de carga de una batería. En el último caso, el modelo híbrido se usa para predecir valores de variables en un tiempo futuro, en instantes posteriores al de la ejecución del modelo. Como aplicación real para este caso, se trata de predecir el valor de la señal ANI empleada en operaciones quirúrgicas, que es un indicador del dolor que sufren los pacientes durante una intervención

A hybrid intelligent model to predict the hydrogen concentration in the producer gas from a downdraft gasifier

[Abstract] This research work presents an artificial intelligence approach to predicting the hydrogen concentration in the producer gas from biomass gasification. An experimental gasification plant consisting of an air-blown downdraft fixed-bed gasifier fueled with exhausted olive pomace pellets and a producer gas conditioning unit was used to collect the whole dataset. During an extensive experimental campaign, the producer gas volumetric composition was measured and recorded with a portable syngas analyzer at a constant time step of 10 seconds. The resulting dataset comprises nearly 75 hours of plant operation in total. A hybrid intelligent model was developed with the aim of performing fault detection in measuring the hydrogen concentration in the producer gas and still provide reliable values in the event of malfunction. The best performing hybrid model comprises six local internal submodels that combine artificial neural networks and support vector machines for regression. The results are remarkably satisfactory, with a mean absolute prediction error of only 0.134% by volume. Accordingly, the developed model could be used as a virtual sensor to support or even avoid the need for a real sensor that is specific for measuring the hydrogen concentration in the producer gas.Junta de Andalucía; 1381442Xunta de Galicia; ED431G 2019/01Ministerio de Universidades; FPU19/0093

Hybrid intelligent system for a synchronous rectifier converter control and soft switching ensurement

[Abastract]: This research implements an intelligent control strategy in a synchronous rectifier buck converter to assure that the converter operates in soft-switching mode. The converter is analysed and the two different switching modes are presented: Hard-switching and Soft-Switching. Afterwards, an intelligent model is implemented with the aim of identifying and classifying the switching mode of the power converter. The model implementation is based on classification methods through intelligent algorithms that differentiate between the two modes of operation. Satisfactory results have been obtained with the implemented classification method, achieving high accuracy and allowing the implementation of the model into the control strategy of the converter; assuring that the converter operates in the desired operating mode: Soft-Switching mode

A new method for anomaly detection based on non-convex boundaries with random two-dimensional projections

[Abstract] The implementation of anomaly detection systems represents a key problem that has been focusing the efforts of scientific community. In this context, the use one-class techniques to model a training set of non-anomalous objects can play a significant role. One common approach to face the one-class problem is based on determining the geometric boundaries of the target set. More specifically, the use of convex hull combined with random projections offers good results but presents low performance when it is applied to non-convex sets. Then, this work proposes a new method that face this issue by implementing non-convex boundaries over each projection. The proposal was assessed and compared with the most common one-class techniques, over different sets, obtaining successful results

Virtual Sensor for Fault Detection, Isolation and Data Recovery for Bicomponent Mixing Machine Monitoring

[Abstract] The present research shows the implementation of a virtual sensor for fault detection with the feature of recovering data. The proposal was implemented over a bicomponent mixing machine used for the wind generator blades manufacture based on carbon fiber. The virtual sensor is necessary due to permanent problems with wrong sensor measurements. The solution proposed uses an intelligent model able to predict the sensor measurements, which are compared with the measured value. If this value belongs to a specified range, it is valid. Otherwise, the prediction replaces the read value. The process fault detection feature has been added to the proposal, based on consecutive erroneous readings, obtaining satisfactory results

Fuel cell hybrid model for predicting hydrogen inflow through energy demand

[Abstract]: Hydrogen-based energy storage and generation is an increasingly used technology, especially in renewable systems because they are non-polluting devices. Fuel cells are complex nonlinear systems, so a good model is required to establish efficient control strategies. This paper presents a hybrid model to predict the variation of H2 flow of a hydrogen fuel cell. This model combining clusters’ techniques to get multiple Artificial Neural Networks models whose results are merged by Polynomial Regression algorithms to obtain a more accurate estimate. The model proposed in this article use the power generated by the fuel cell, the hydrogen inlet flow, and the desired power variation, to predict the necessary variation of the hydrogen flow that allows the stack to reach the desired working point. The proposed algorithm has been tested on a real proton exchange membrane fuel cell, and the results show a great precision of the model, so that it can be very useful to improve the efficiency of the fuel cell system.Ministerio de Economía, Industria y Competitividad; H2SMART-mGRID (DPI2017-85540-R

Experiencia de docencia basada en proyectos usando la música como elemento principal para la asignatura de Fundamentos de Electrónica

[Resumen] La tendencia actual y futura en las carreras técnicas y en especial en las ingenierías, es que el número de alumnos se ve cada vez más reducido, bien por falta de vocación o motivación para afrontar una carrera si bien compleja, con una alta demanda laboral. Parte de esa falta de motivación proviene muchas veces de la necesidad de convertir muchos de los contenidos en temáticas más atractivas para los estudiantes. Por ello en esta experiencia de innovación docente, se ha planteado el uso de la música como principal elemento motivador para convertir el mismo contenido de asignaturas, en especial de los primeros cursos, en contenidos más atractivos a los estudiantes y conseguir de este modo, no solo un mayor efecto de motivación sino también que esto se refleje en los resultados finales obtenidos por los estudiantes. En este caso en concreto, se propone el desarrollo e implementación de un circuito electrónico capaz de “buscar” una canción “escondida” entre otras muchas, de modo que se incluye además un aspecto competitivo entre los alumnos. Tras la experiencia, los resultados obtenidos han sido muy positivos, tanto en el aspecto motivacional con un aumento de la participación de los estudiantes en un 22%, así como en los resultados académicos obtenidos.[Abstract] The current and future trend in technical careers and especially in engineering, is that the number of students is increasingly reduced, either due to lack of vocation or motivation to face a career although complex, with a high labor demand. Part of that lack of motivation often comes from the lack of converting many of the content into more attractive topics for students. For this reason, in this experience of teaching innovation, it has proposed the use of music as a main motivating element to convert the same content of subjects, especially the first courses, into more specific content to students and obtain in this way, not only a mayor motivational effect but also that this is reflected in the final students results. In this particular case, it is proposed the development and implementation of an electronic circuit capable of "searching" for a song "hidden" among many others, so that a competitive aspect among students is also included. After the experience, the results obtained have been very positive, both in the motivational aspect with an increase in student participation in more than 22%, as well as in the academic results obtained

Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy

[Abstract] This work deals with the prediction of variables for a hydrogen energy storage system integrated into a microgrid. Due to the fact that this kind of system has a nonlinear behaviour, the use of traditional techniques is not accurate enough to generate good models of the system under study. Then, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the power, the hydrogen level and the hydrogen system degradation. In this research, a hybrid intelligent model was created and validated over a dataset from a lab-size migrogrid. The achieved results show a better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption/generation with a mean absolute error of 0.63% with the test dataset respect to the maximum power of the system.Ministerio de Economía, Industria y Competitividad; DPI2017-85540-

Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump

[Abstract] Nowadays the Heat Pump is one of the best systems to warm a building with a good performance. Usually, with the aim to increase the efficiency, a geothermal heat exchanger is added to the installation. This component shows a disturbing effect on the ground where it is placed. On this research a bio-inspired system was developed to test the ground temperature behavior where there is a heat exchanger. The novel approach has been implemented and tested under a real dataset. One year temperature measurements were recorded. The final approach is based on clustering and regression techniques. Then, the model was validated and tested with a dataset from a real installation with a good performance

A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques

[Abstract ]:This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.Junta de Castilla y León; LE078G18. UXXI2018/000149. U-220.Ministerio de Economía, Industria y Competitividad; DPI2016-79960-C3-2-