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

Editorial: Special issue ISA 2023

Funding for open access charge: Universidade da Coruña/CISUG.[Abstract] ISA 2023 is a significant forum for presenting the development and applications of innovative techniques in closely related areas. The exchange of ideas between scientists and technicians from both academic and business sectors is essential to facilitate the development of systems that meet the demands of today’s society. Technology transfer in this field remains a challenge, so such contributions are mainly considered in this symposium. The ISA Special Session features discussions and publications on developing innovative techniques for complex problems. This Special Issue includes 11 papers selected from extended contributions presented at the Special Session on Intelligent Systems Applications (ISA) under the framework of the 20th International Symposium on Distributed Computing and Artificial Intelligence 2023 (DCAI 2023), held in Guimaraes, Portugal, 12–14 July 2023, and organized by LASI and Centro Algoritmi of the University of Minho (Portugal)

Dimensional reduction applied to an intelligent model for boost converter switching operation

The dimensional reduction algorithms are applied to a hybrid intelligent model that distinguishes the switching operating mode of a boost converter. Thus, the boost converter has been analyzed and both operating mode are explained, distinguishing between Hard-switching and Soft-switching modes. Then, the dataset is created out of the data obtained from simulation of the real circuit and the hybrid intelligent classification model is implemented. Finally, the dimensional reduction of the input variables is carried out and the results are compared. As result, the proposed model with the applied dimensional reduced dataset is able to distinguish between the HS and SS operating modes with high accuracy.ERDF -European Regional Development Fund(ED431G 2019/01

Data dimensionality reduction for an optimal switching mode classification applied to a step-down power converter

Funding for open access charge: Universidade da Coruña/CISUG.[Abstract] A dimensional reduction algorithm is applied to an intelligent classification model with the purpose of improving the efficiency and accuracy. The proposed classification model, used to distinguish the operating mode: Hard- and Soft-Switching, is presented and an analysis of the synchronized rectified step-down converter is done. With the aim of improving the accuracy and reducing the computational cost of the model, three different methods for dimensional reduction are applied to the input dataset of the model: self-organizing maps, principal component analysis and correlation matrix. The obtained results show how the number of variable is highly reduced and the performance of the classification model is boosted: the results manifest an improve in the accuracy and efficiency of the classification

Detection of DoS Attacks in an IoT Environment with MQTT Protocol Based on Intelligent Binary Classifiers

[Abstract] The present work deals with the problem of detecting Denial of Service attacks in an IoT environment. To achieve this goal, a dataset registered in an MQTT protocol network is used, applying dimension reduction techniques combined with classification algorithms. The final classifiers presents successful results.Xunta de Galicia; ED431G 2019/0

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

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

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