Model Predictive Control Based on Deep Learning for Solar Parabolic-Trough Plants

En la actualidad, cada vez es mayor el interés por utilizar energías renovables, entre las que se encuentra la energía solar. Las plantas de colectores cilindro-parabólicos son un tipo de planta termosolar donde se hace incidir la radiación del Sol en unos tubos mediante el uso de unos espejos con forma de parábola. En el interior de estos tubos circula un fluido, generalmente aceite o agua, que se calienta para generar vapor y hacer girar una turbina, produciendo energía eléctrica. Uno de los métodos más utilizados para manejar estas plantas es el control predictivo basado en modelo (model predictive control, MPC), cuyo funcionamiento consiste en obtener las señales de control óptimas que se enviarán a la planta basándose en el uso de un modelo de la misma. Este método permite predecir el estado que adoptará el sistema según la estrategia de control escogida a lo largo de un horizonte de tiempo. El MPC tiene como desventaja un gran coste computacional asociado a la resolución de un problema de optimización en cada instante de muestreo. Esto dificulta su implementación en plantas comerciales y de gran tamaño, por lo que, actualmente, uno de los principales retos es la disminución de estos tiempos de cálculo, ya sea tecnológicamente o mediante el uso de técnicas subóptimas que simplifiquen el problema. En este proyecto, se propone el uso de redes neuronales que aprendan offline de la salida proporcionada por un controlador predictivo para luego poder aproximarla. Se han entrenado diferentes redes neuronales utilizando un conjunto de datos de 30 días de simulación y modificando el número de entradas. Los resultados muestran que las redes neuronales son capaces de proporcionar prácticamente la misma potencia que el MPC con variaciones más suaves de la salida y muy bajas violaciones de las restricciones, incluso disminuyendo el número de entradas. El trabajo desarrollado se ha publicado en Renewable Energy, una revista del primer cuartil en Green & sustainable science & technology y Energy and fuels.Nowadays, there is an increasing interest in using renewable energy sources, including solar energy. Parabolic trough plants are a type of solar thermal power plant in which solar radiation is reflected onto tubes with parabolic mirrors. Inside these tubes circulates a fluid, usually oil or water, which is heated to generate steam and turn a turbine to produce electricity. One of the most widely used methods to control these plants is model predictive control (MPC), which obtains the optimal control signals to send to the plant based on the use of a model. This method makes it possible to predict its future state according to the chosen control strategy over a time horizon. The MPC has the disadvantage of a significant computational cost associated with resolving an optimization problem at each sampling time. This makes it challenging to implement in commercial and large plants, so currently, one of the main challenges is to reduce these computational times, either technologically or by using suboptimal techniques that simplify the problem. This project proposes the use of neural networks that learn offline from the output provided by a predictive controller to then approximate it. Different neural networks have been trained using a 30-day simulation dataset and modifying the number of irradiance and temperature inputs. The results show that the neural networks can provide practically the same power as the MPC with smoother variations of the output and very low violations of the constraints, even when decreasing the number of inputs. The work has been published in Renewable Energy, a first quartile journal in Green & sustainable science & technology and Energy and fuels.Universidad de Sevilla. Máster en Ingeniería Industria

Gestión de energía de una microrred mediante control predictivo basado en modelo

Una microrred es un conjunto de fuentes, cargas y unidades de almacenamiento de energía que puede encontrarse conectado o aislado de la red de transporte. Para asegurar su correcto funcionamiento, estos sistemas cuentan con elementos de comunicación y control. En este Trabajo de Fin de Grado se propone un algoritmo de Control Predictivo Basado en Modelo para gestionar el intercambio energético en la microrred del Centro de Experimentación de El Arenosillo (CEDEA), perteneciente al Instituto Nacional de Técnica Aeroespacial (INTA). El Control Predictivo Basado en Modelo o Model Predictive Control (MPC) es un método de control avanzado que se basa en la resolución de un problema de optimización. A partir de un modelo dinámico del sistema a controlar, se realizan predicciones del estado en instantes sucesivos y se calculan las señales de control óptimas para obtener la salida deseada a lo largo de un horizonte.A microgrid is a combination of sources, loads and energy storage units which can be connected to or isoleted from the utility grid. In order to ensure the correct operation, theese systems are provided with communication and control elements. In this Final Degree Project a Model Predictive Control algorithm is proposed to manage the energy exchange in the microgrid of the experimental center CEDEA, belonging to the National Institute of Aerospace Technology (INTA). Model Predictive Control (MPC) is an advanced control method based on the resolution of an optimization problem. From a dynamic model of the system, state predictions in successive time steps are made and optimal control signals are calculated in order to obtain the desired output throughout a horizon.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías Industriale

Deep Learning-Based Fault Detection and Isolation in Solar Plants for Highly Dynamic Days

ICCAD'22: 2022- 6th International Conference on Control, Automation and Diagnosis, Lisbon, Portugal, July 13-15, 2022Solar plants are exposed to numerous agents that degrade and damage their components. Due to their large size and constant operation, it is not easy to access them constantly to analyze possible failures on-site. It is, therefore, necessary to use techniques that automatically detect faults. In addition, it is crucial to detect the fault and know its location to deal with it as quickly and effectively as possible. This work applies a fault detection and isolation method to parabolic trough collector plants. A characteristic of solar plants is that they are highly dependent on the sun and the existence of clouds throughout the day, so it is not easy to achieve methods that work well when disturbances are too variable and difficult to predict. This work proposes dynamic artificial neural networks (ANNs) that take into account past information and are not so sensitive to the variations of the plant at each moment. With this, three types of failures are distinguished: failures in the optical efficiency of the mirrors, flow rate, and thermal losses in the pipes. Different ANNs have been proposed and compared with a simple feedforward ANN, obtaining an accuracy of 73.35%.European Research Council 10.13039/50110000078

A deep learning-based strategy for fault detection and isolation in parabolic-trough collectors

Solar plants are exposed to the appearance of faults in some of their components, as they are vulnerable to the action of external agents (wind, rain, dust, birds …) and internal defects. However, it is necessary to ensure a satisfactory operation when these factors affect the plant. Fault detection and diagnosis methods are essential to detecting and locating the faults, maintaining efficiency and safety in the plant. This work proposes a methodology for detecting and isolating faults in parabolic-trough plants. It is based on a three-layer methodology composed of a neural network to obtain a preliminary detection and classification between three types of fault, a second stage analyzing the flow rate dynamics, and a third stage defocusing the first collector to analyze thermal losses. The methodology has been applied by simulation to a model of the ACUREX plant, which was located at the Plataforma Solar de Almería. The confusion matrices have been obtained, with accuracies over 80% when using the three layers in a hierarchical structure. By forcing all the three layers, the accuracies exceed 90%.Unión Europea - Horizonte 2020 No 789 05

Health-related factors of psychological distress during the COVID-19 pandemic among non-health workers in Spain

Background: Non-health workers engaged in essential activities during the pandemic are less researched on the effects of COVID-19 than health workers. Objective: to study the differences between those who work away from home and those who do so from home, when the effects of fear of contagion cross with those of confinement, about the psychological distress during the COVID-19 in Spain. Design: Observational descriptive cross-sectional study. Data sources: The study was carried out receiving 1089 questionnaires from non-health workers that were working away from home and doing so from their homes. The questionnaire included sociodemographic and occupational data, physical symptoms, self-perceived health, use of preventive measures and possible contacts, and the Goldberg GHQ-12. Results: 71.6% of non-health female workers and 52.4% of non-health male workers had psychological distress, with differences among those working away from home and those working from home. The level of psychological distress among non-health workers is predicted by 66.5% through the variables: being a woman, 43 years old or younger, having a home with no outdoor spaces, poor perception of health, number of symptoms, and having been in contact with contaminated people or material. Among workers who work away from home, being self- employed is another predictive variable of distress. Conclusion: More than the half of the sample showed inadequate management of the psychological distress. There are modifiable factors which provide necessary elements to support a positive attitude of the workers, such as: knowledge of hygiene, transmission of the virus, protective measures, and social distancing measures

Piezochromic properties of a D-A-D platform: A joint experimental and theoretical perspective

Organic π-conjugated molecules with mechanochromic luminescence properties have attracted great interest in the last two decades due to their numerous applications in the optoelectronic field, such as sensors, probes and security inks. Materials that modify their colour under pressure are known as piezochromic materials. Usually, this variation is provoked by changes in the molecular structure, for example, crystal-to-amorphous phase transitions, modifications in dihedral angles or bond distances, and intermolecular interactions. The molecule proposed in this study is a TADF (Thermally activated delayed fluorescence) U-shaped molecule composed by two donors and one acceptor (D-A-D) units with a π-conjugated skeleton [1]. It was synthetized as a powder which under different crystallization methods gives rise to different conformers varying the dihedral angle of the bond that links the D and A units. The donors are two phenothiazine units and the acceptor is a dibenzo[a,j]phenazine unit located in the central core. Two different conformers have been analysed: the quasi equatorial - quasi equatorial (denoted as 1R) and the quasi axial - quasi axial (denoted as 1Y). In this project, we study the configurational changes triggering the piezochromic effects combining density functional theory (DFT) calculations with Raman spectroscopy experiments of the 1R and 1Y conformers during heating or in compression via a sapphire anvil cell [2]. Both show pressure and temperature dependence properties. Besides, these changes are reversible meaning that when the stimuli stop they revert to its original conformation. When these molecules are exposed to different ambient (like pressure or temperature variations) they evolved to a third conformer with an intermediate dihedral angle that results in different Raman, emission and absorption behaviour.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Sense of coherence, engagement, and work environment as precursors of psychological distress among non-health workers during the COVID-19 pandemic in Spain

Background The interrelationship between the sense of coherence, work environment, work engagement, and psychological distress have particular interest in non-health workers who carried out essential activities during the COVID-19 pandemic. Objective To assess the effects of the COVID-19 on the physical and mental health of non-health workers. Design Observational descriptive cross-sectional study. Data sources 1089 questionnaires have been analysed. Engagement (UWES-9), sense of coherence (SOC-13), mental health (Goldberg GHQ-12), demographic data, perception of health and stress and work environment were assessed. Results At low levels of engagement, the percentage of distress is higher (77.9%). Low levels of sense of coherence correspond to the highest percentages of distress (86.3%). The 94.1% believe it necessary for professionals and volunteers involved in COVID-19 to receive psychological support. Low comprehensibility is mediated by the perception of stress; if the perception is low, comprehensibility is modulated by the level of significance; if it is low, it generates 95.9% of distress. Conclusion The interrelationship between the sense of coherence, work environment, work engagement, and psychological distress have particular interest in non-health workers who carried out essential activities during the COVID-19 pandemic. Almost all respondents believed that professionals and volunteers involved in COVID-19 should receive psychological support. This may be an indicator of the effect of the COVID-19 pandemic on workers’ mental health

Expression of SATB1 and SATB2 in the brain of bony fishes: what fish reveal about evolution

Satb1 and Satb2 belong to a family of homeodomain proteins with highly conserved functional and regulatory mechanisms and posttranslational modifications in evolution. However, although their distribution in the mouse brain has been analyzed, few data exist in other non-mammalian vertebrates. In the present study, we have analyzed in detail the sequence of SATB1 and SATB2 proteins and the immunolocalization of both, in combination with additional neuronal markers of highly conserved populations, in the brain of adult specimens of different bony fish models at key evolutionary points of vertebrate diversification, in particular including representative species of sarcopterygian and actinopterygian fishes. We observed a striking absence of both proteins in the pallial region of actinopterygians, only detected in lungfish, the only sarcopterygian fish. In the subpallium, including the amygdaloid complex, or comparable structures, we identified that the detected expressions of SATB1 and SATB2 have similar topologies in the studied models. In the caudal telencephalon, all models showed significant expression of SATB1 and SATB2 in the preoptic area, including the acroterminal domain of this region, where the cells were also dopaminergic. In the alar hypothalamus, all models showed SATB2 but not SATB1 in the subparaventricular area, whereas in the basal hypothalamus the cladistian species and the lungfish presented a SATB1 immunoreactive population in the tuberal hypothalamus, also labeled with SATB2 in the latter and colocalizing with the gen Orthopedia. In the diencephalon, all models, except the teleost fish, showed SATB1 in the prethalamus, thalamus and pretectum, whereas only lungfish showed also SATB2 in prethalamus and thalamus. At the midbrain level of actinopterygian fish, the optic tectum, the torus semicircularis and the tegmentum harbored populations of SATB1 cells, whereas lungfish housed SATB2 only in the torus and tegmentum. Similarly, the SATB1 expression in the rhombencephalic central gray and reticular formation was a common feature. The presence of SATB1 in the solitary tract nucleus is a peculiar feature only observed in non-teleost actinopterygian fishes. At these levels, none of the detected populations were catecholaminergic or serotonergic. In conclusion, the protein sequence analysis revealed a high degree of conservation of both proteins, especially in the functional domains, whereas the neuroanatomical pattern of SATB1 and SATB2 revealed significant differences between sarcopterygians and actinopterygians, and these divergences may be related to the different functional involvement of both in the acquisition of various neural phenotypes.Depto. de Biología CelularFac. de Ciencias BiológicasTRUEMinisterio de Ciencia e Innovación (grant no. PID2020- 112681GB100), and the Santander/Complutense University of Madrid, Grant/Award Number: PR108/20-17Universidad Complutense de Madrid (UCM) /Banco de Santanderpu

La edad como factor determinante en la competencia digital docente

Objetivo: En los últimos tiempos, la competencia digital docente está alcanzando gran relevancia, dado que es un factor de calidad a la hora de implementar procesos de enseñanza y aprendizaje innovadores, gracias a la utilización de las tecnologías de la información y comunicación en el ámbito educativo. El objetivo de la presente investigación es identificar el nivel de competencia digital docente según la edad, en las cinco áreas que la componen. Metodología: El método de investigación usado es de tipo cuantitativo, de carácter descriptivo y correlacional, mediante diseño no experimental y de paradigma transeccional. El instrumento utilizado es un cuestionario ad hoc, compuesto por 6 dimensiones, el cual ha seguido los procesos requeridos para su validación y fiabilidad. Resultados/Discusión: Los principales resultados muestran que los niveles competenciales de los docentes, en las distintas edades, son deficitarios, dado que sus niveles son medio-bajo, siendo los docentes con edades comprendidas entre los 31 y 40 años los que alcanzan mayores valores en la mayoría de las áreas analizadas. Originalidad/Valor/Conclusión Se concluye que la edad es un factor determinante en el desarrollo de la competencia digital docente, en donde los formadores, con edades comprendidas entre los 20 y 41 años, presentan mejores niveles competenciales en todas las áreas que aquellos mayores de 41 años.

Crowdfunding para organizaciones del Tercer y Cuarto Sector

Máster en Dirección Empresarial desde la Innovación y la Internacionalización. Curso 2013/2014[ES] Las organizaciones sin ánimo de lucro así como las empresas de innovación social son muy necesarias en cualquier sociedad, puesto que juegan un papel esencial en la identificación y resolución de carencias socio-económicas, y se constituyen, junto con las administraciones públicas, en agentes vertebradores para la cohesión social y económica en un aspecto más práctico, y para el avance ético en un sentido más moral. La crisis financiera actual ha supuesto un varapalo para estas organizaciones que han visto recortadas drásticamente sus fuentes de financiación, mayoritariamente de origen público. En este contexto las tecnologías open source están ayudando a al desarrollo y supervivencia de organizaciones de pequeño tamaño y recursos limitados a través de diversas herramientas, como por ejemplo el Crowdfunding. Ahora bien, no hay que olvidar que más allá de la explosión de este fenómeno, el Crowdfunding necesita de cierta regulación y control para que pueda consolidarse como un instrumento importante de captación de fondos.[EU] Irabazi-asmorik gabeko erakundeak eta berrikuntza sozialeko enpresak baita ere oso beharrezkoak dira edozein gizartetan, giza beharrak identifikaziorako eta gabezia sozio-ekonomikoak asetzen dituztelako. Administrazio publikoekin batera, kohesio sozial eta ekonomikorako eragile bihurtzen dira zentzu praktiko batean, eta aurrerapen morala garatzen dute arlo etikoan. Gaurko krisi finantzarioak bere finantzaketa-iturriak drastikoki murriztu ditu, jatorri publikoak gehienak. Kontestu honetan open source teknologiak baliabide gutxiko eta tamaina txikiko erakundeak laguntzen ari dira bere garapen eta biziraupenerako tresna ezberdinak eskainiz, Crowdfunding adibidez. Ez da ahaztu behar, hori bai, Crowdfunding-a fenomeno iragankorra baino gehiago izan dadila, erregulazio eta kontrola behar duela diru-bilketako mekanismo garrantzitsu bat ezartzeko.[EN] Non-profit Organizations and also Social Innovation Enterprises are essential in any society because they play an important role in the identification and resolution of social and economic lacks, and they establish themselves, together with Public Institutions, as agents who provide social and economic cohesion in a practical sense, and contribute to the moral advance from an ethical point of view. The financial crisis hit drastically those organizations who has suffered cuts in their funding, especially publics funds. In the current context the open source technologies are helping to the development and survive of small sized organizations with little resources through several tools as Crowdfunding. Anyway, is important to remark that for the consolidation of crowdfunding as a fundraising tool, beyond a temporal phenomenon, is necessary some kind of regulation and control over it