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Finalista (puesto 4º). Modalidad junio

Assessment of the geothermal potential in the region of Ávila (Spain): An integrated and interactive thermal approach

Exploring and exploiting a geothermal resource has become one of the most prolific tasks for contributing to the global sustainable development. Despite this fact, several countries, such as Spain, are still far from achieving a generalized use of these renewable systems. The reason for this underuse often derives from the lack of information and characterization of the geothermal energy source. Considering this, the present research aims to provide relevant data about the geothermal potential of the Spanish region of Ávila. The geological context of this province lays the foundations for considering the region as a promising site for different geothermal uses. In order to estimate the geothermal energy potential of the region, the existing geological information has been complemented with thermal surveys carried out in the study area. The experimental measuring has consisted of the register of the underground temperature in piezometers of variable depth and natural springs distributed throughout the province. The processing of these records has allowed knowing the thermal evolution of the subsoil at the different levels evaluated in the research. Results show that there are two main potential areas in the province that could be successfully used for heating purposes (maybe as part of district heating systems) and for future deeper evaluations in the sense of Hot Dry Rock (HDR) techniques. Final conclusions have also been included in an interactive and open-source tool that allows visualizing the thermal findings with the aim of planning future geothermal uses in the region

Geophysical exploration for shallow geothermal applications: A case study in Artà, (Balearic Islands, Spain)

[EN] Within the installation of a shallow geothermal system, the lack of information on the subsoil frequently leads to errors in the design of the geothermal wellfield. This research presents the application of geophysics, combining 2D and 3D electrical resistivity tomography surveys and the geological information of a certain area for defining the structural distribution of the underground. Processed electrical resistivity data allow elucidating possible geological units and the thermal behavior of the in-depth materials. Two different assumptions (with different locations of the wells) are designed by using the specific geothermal software GES-CAL. Results show, that Case 1 (based on the geophysical results, so avoiding complex areas) allows the reduction of the global drilling length, and hence, the general initial investment of the system (around 20% lower). Meanwhile, Case 2 (without considering the geophysics) is less economically advantageous and could also present technical difficulties during the drilling process, as well as the possible alteration to the normal system operation. The study highlights the benefits of geophysics as an effective approach to characterize the underground and to help to understand its thermal behavior, which is, in turn, crucial for a proper geothermal design.S

Investigating the potential of the slurry technology for sustainable pig farm heating

Sustainable energy development in the farming sector is an essential strategy to respond the combined challenge of achieving a reliable and affordable solution but including mitigation and adaptation to climate change. Intensive breeding farms require maintaining an adequate indoor thermal environment that results in high energy demands, usually covered by fossil fuels and electricity. This paper addresses the application of the combined slurry technology for a particular pig farm that currently uses a diesel boiler to supply the piglet heating energy needs. The study also considers different options based on closed ground source heat pump systems. After the design of the slurry alternative and the geothermal ones, notable advantages are detected compared to the existing diesel system. Results show that the implementation of the slurry technology implies an important reduction of the operational costs, which, in turn, involves short amortization periods for this system in relation to the diesel one. Greenhouse gases emissions are also highly reduced in the slurry alternative based on the low electricity use of the heat pump. The environmental side is reinforced by the reduction of polluting substances such as methane of ammonia derived from the descent of temperature of the slurry

Evaluation of different methodologies for calculating the energy demand and their influence on the design of a low enthalpy geothermal system

The increasing importance of shallow geothermal resources in the decarbonization of heating and cooling systems requires the correct management of all the project stages. One of the fundamental steps in this process is determining the space energy demand, which plays a significant role in the subsequent geothermal design. In the context of Spain, different tools are available for the estimation of the mentioned parameter. For evaluating these procedures, this research applies the principal energy demand calculation tools and uses the outcomes for the later design of the shallow geothermal system. Results show how the Spanish official tools (HULC and CE3X) provide lower energy demand values adjusted to the construction conditions of the building that allow the optimization of the geothermal well field. On the contrary, simpler, and more intuitive applications (regular spreadsheets and GES-CAL) assume higher heating energy demands, which in turn implies an oversizing of the geothermal scheme. Even though all the procedures ensure to cover the energy requirements of the building, the most precise tools manage to reduce the initial investment of the system and its operating costs, in addition to reducing the global CO2 emissions because of the lower power of the associated geothermal heat pump

Predictive models for the characterization of internal defects in additive materials from active thermography sequences supported by machine learning methods

The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.This research was funded by Ministry of Science and Innovation, Government of Spain, through the research project titled Fusion of non-destructive technologies and numerical simulation methods for the inspection and monitoring of joints in new materials and additive manufacturing processes (FaTIMA) with code RTI2018-099850-B-I00. The authors are grateful to the Fundación Universidad de Salamanca for the indirect support provided by the ITACA proof-of-concept project (PC_TCUE_18-20_047), being this helpful for some of the purposes of this article

Climatización eficiente mediante bombas de calor y tecnología solar fotovoltaica: Análisis de viabilidad en edificios comerciales en España

Según los datos publicados por el IDAE en el Plan de Acción de Ahorro y Eficiencia Energética 2011-20, el 58% de la energía utilizada en edificios comerciales españoles se destina al acondicionamiento térmico (HVAC). En este contexto, la subida de los precios de la electricidad experimentada en los últimos años, y la disminución de los precios de la tecnología fotovoltaica, han establecido las bases para que se realicen estudios detallados de la viabilidad energética y económica de soluciones basadas en bombas de calor alimentadas por generadores fotovoltaicos. En el presente estudio se analizan en distintos escenarios las curvas horarias de generación fotovoltaica y la demanda eléctrica para HVAC de un edificio de oficinas típico ubicado en Madrid. El estudio analiza como varía la demanda eléctrica para HVAC que habría que suministrar desde la red en el caso de que no existiera el generador fotovoltaico con la que habría que cubrir disponiendo del generador instalado en cubierta y conectado en modalidad de autoconsumo. Los resultados muestran que el sistema fotovoltaico proporcionaría ahorros de facturación anual entre 30% y 50% bajo distintas hipótesis de aprovechamiento de la cubierta. El artículo analiza asimismo la rentabilidad económica de la inversión. Palabras clave: HVAC, bombas de calor, fotovoltaica, ahorro económico, autoconsumo

TSFEDL: A python library for time series spatio-temporal feature extraction and prediction using deep learning

The combination of convolutional and recurrent neural networks is a promising framework. This arrangement allows the extraction of high-quality spatio-temporal features together with their temporal dependencies. This fact is key for time series prediction problems such as forecasting, classification or anomaly detection, amongst others. In this paper, the TSFEDL library is introduced. It compiles 22 state-of-the-art methods for both time series feature extraction and prediction, employing convolutional and recurrent deep neural networks for its use in several data mining tasks. The library is built upon a set of Tensorflow + Keras and PyTorch modules under the AGPLv3 license. The performance validation of the architectures included in this proposal confirms the usefulness of this Python package.This work has been partially supported by the Contract UGRAM OTRI-4260 and the Regional Government of Andalusia, under the program ‘‘Personal Investigador Doctor”, reference DOC_00235. This work was also supported by project PID2020-119478 GB-I00 granted by Ministerio de Ciencia, Innovación y Universidades, and projects P18-FR-4961 and P18-FR-4262 by Proyectos I + D+i Junta de Andalucia 2018

Enfermedades Iatrogénicas y Mal Praxis. Revisión bibliográfica.

En estudios realizados en la Biblioteca de la Facultad de Ciencias Médicas Dr. Enrique Cabrera, en el período de octubre-enero, nos percatamos de la escasez de bibliografía de las enfermedades iatrogénicas y mala praxis, lo cual motivó una investigación retrospectiva, recuperando, en las Bases de Datos LILASC, desde 1982 hasta la fecha y Medline de 1967 a la actualidad, un listado bibliográfico sobre estas enfermedades, y se obtuvo un total de112 documentos como fuente de información para mejorar el servicio a los usuarios del lugar. Una vez recuperada ésta se procesan las temáticas tratadas y se analizan los resultados en cuanto a productividad de las publicaciones en años, revistas de mayor impacto, etcétera. Con el objetivo de ofrecer a los investigadores, profesores y estudiantes una bibliografía de estas dos temáticas médicas como importante herramienta auxiliar que les permita llegar a sus documentos originales.  Palabras clave: Enfermedad iatrogénica, mala praxis

Coordinación de competencias e intercambio de experiencias docentes en el marco del Master Geotecnologías Cartográficas en Ingeniería y Arquitectura

Memoria ID-0178. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015