Obtención de curvas de enfriamiento: diagrama eutécnico simple

Memoria ID-344. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2013-2014

Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training

[EN] This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different length in order to ease the use of a fallback mechanism together with the precomputation of softmax normalization constants for these inputs. The proposed approach is empirically validated, showing their capability to emulate lower order N-grams with a single Neural Network. A machine translation task shows that the proposed model constitutes a good solution to the normalization cost of the output softmax layer of Neural Networks, for some practical cases, without a significant impact in performance while improving the system speed.This work was partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R (to MJCB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Zamora Martínez, FJ.; España Boquera, S.; Castro-Bleda, MJ.; Palacios Corella (2018). Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training. PLoS ONE. 13(7). https://doi.org/10.1371/journal.pone.0200884S13

Aplicación de la espectroscopía de infrarrojo a la identificación y la cuantificación de diferentes compuestos

Memoria ID-0147. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2015-2016

Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms

[EN] Accurate and efficient battery modeling is essential to maximize the performance of isolated energy systems and to extend battery lifetime. This paper proposes a battery model that represents the charging and discharging process of a lead-acid battery bank. This model is validated over real measures taken from a battery bank installed in a research center placed at "El Choco", Colombia. In order to fit the model, three optimization algorithms (particle swarm optimization, cuckoo search, and particle swarm optimization + perturbation) are implemented and compared, the last one being a new proposal. This research shows that the identified model is able to estimate real battery features, such as state of charge (SOC) and charging/discharging voltage. The comparison between simulations and real measures shows that the model is able to absorb reading problems, signal delays, and scaling errors. The approach we present can be implemented in other types of batteries, especially those used in stand-alone systems.This research was supported by "Implementacion de un programa de desarrollo e investigacion de energias renovables en el departamento del Choco"-BPIN:20130000100285; COLCIENCIAS (Administrative Department of Science, Technology and Innovation of Colombia) scholarship program PDBCEx, COLDOC 586, and the support provided by the Corporacion Universitaria Comfacauca, Popayan-Colombia.Ariza-Chacón, HE.; Banguero-Palacios, E.; Correcher Salvador, A.; Pérez-Navarro, Á.; Morant Anglada, FJ. (2018). Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms. Energies. 11(9):1-14. https://doi.org/10.3390/en11092361S11411

Cinética homogénea y heterogénea en flujo

Memoria ID-0211. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015

Creación de vídeos de apoyo para la enseñanza de práctica de técnicas básicas de análisis químico físicos

Memoria ID-0212. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

[EN] Energy storage has become a fundamental component in renewable energy systems, especially those including batteries. However, in charging and discharging processes, some of the parameters are not controlled by the battery's user. That uncontrolled working leads to aging of the batteries and a reduction of their life cycle. Therefore, it causes an early replacement. Development of control methods seeks battery protection and a longer life expectancy, thus the constant-current-constant-voltage method is mostly used. However, several studies show that charging time can be reduced by using fuzzy logic control or model predictive control. Another benefit is temperature control. This paper reviews the existing control methods used to control charging and discharging processes, focusing on their impacts on battery life. Classical and modern methods are studied together in order to find the best approach to real systems.The authors would like to acknowledge the research project “Implementación de un programa de desarrollo e investigación de energías renovables en el departamento del Chocó, BPIN 2013000100285” and the Universidad Tecnológica del Chocó.Banguero-Palacios, E.; Correcher Salvador, A.; Pérez-Navarro, Á.; Morant Anglada, FJ.; Aristizabal Cardona, AJ. (2018). A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems. Energies. 11(4):1-15. https://doi.org/10.3390/en11041021S11511

Diseño, aplicación y evaluación de actividades docentes innovadoras para la enseñanza- aprendizaje y la adquisición de competencias

Memoria ID11-146. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2011-2012

On the Influence of VOCs on New Particle Growth in a Continental-Mediterranean Region

[Abstract] A field campaign has been performed in the Madrid region to study the VOC influence in the growth of new particles in ambient air. A number of instruments have been deployed to characterize the main pollutant gases and particle properties and composition. The measurements were performed simultaneously at three sites (rural, urban background and urban traffic influenced) in the period 1–17 July 2019. The sites: Tres Cantos (rural), CIEMAT (urban background) and Leganés (urban traffic) were located within the Madrid airshed. Particle size distributions, mass concentrations at fractions PM10, PM2.5 and PM1, black carbon, VOCs species and gaseous pollutants (NOx and O3) were obtained in the sites. Some supplementary measurements were obtained in at least one of the sites: meteorological parameters, non-refractory submicron aerosol species and vertical profiles of aerosol optical properties. It has been observed that the new particle formation (NPF) events, nucleation and subsequent growth, happened at a regional scale, although differently among the sites. In the rural site, fewer events than expected were observed because of the high temperatures that affected the BVOC emissions. In the urban background site, the highest number of events was reached. In this station, it is common to receive air masses from the nearby forest and from the urban area, producing a mix of conditions with high BVOC and AVOC concentrations. In the urban traffic site, several NPF cases appeared, being a site dominated by AVOCs. Among the BVOCs measured in the three stations, the most common were α-Pinene and Limonene. Among the AVOCs measured, aromatics and linear hydrocarbon compounds for C10 and above were found. The linear group was found to be predominant during the NPF event days in the urban background site. This work provides new insights about the aerosol-forming precursors and growth of new particles in the Madrid region.This research has been partially funded by the CRISOL Project (CGL2017-85344-R MINECO/AEI/FEDER, UE), OASIS project (PID2021-127885OB-I00 fund by MCIN/ AEI/10.13039/501100011033 and by 'ERDF A way of making Europe') and by the TIGAS-CM project (Madrid Regional Government Y2018/EMT-5177)Comunidad de Madrid; Y2018/EMT-517