Artificial reefs

Treball desenvolupat en el marc del programa "European Project Semester".This report is a final report of an EPS project about artificial reefs. The project is being carried out by four engineering students who are studying at Universitat Politécnica de Catalunya in Vilanova I La Geltrù. The aim of the project, which began on February 8th, is to design an artificial reef to restore the flora and fauna in a specific area near a marine observatory established by the company SARTI. The report outlines the methodology used to carry out the project during these months of work, which involved research on the existing fauna in the targeted area (fish and crustacean species), creation and analysis of 3D designs, simulation of the different models and other processes in order to create the most suitable design. The report also contains market and competitive research as well as the analysis of different materials. Overall, the report provides a detailed overview of the two models of artificial reefs that have been created to restore the marine environment.Incomin

Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variation

Energy generated from renewable sources is exposed to extremely dynamic variations in climatic conditions as well as uncertainties (current/voltage variability, noise, measurement errors.). These conditions are relevant issues to be considered when monitoring renewable energy conversion (REC) systems. In fact, these uncertain systems are subjected to many failures leading to performance degradation and long downtime maintenance periods. Therefore, fault detection and diagnosis (FDD) are essential to ensure its high dependability. This paper proposes an FDD under climatic conditions variability of uncertain REC systems using deep recurrent neural networks (DRNNs) techniques. Firstly, a novel modeling strategy for REC systems is built. Secondly, different DRNN-based interval-valued data methods are intended to differentiate between the various REC systems operating states. Finally, the hyperparameters of the proposed techniques are tuned using the Bayesian optimization algorithm. The efficiency and robustness of the novel strategy are demonstrated through REC application, using grid-connected photovoltaic (GCPV) systems. The obtained results show the efficiency of the developed strategy by reaching an accuracy rate of 92.40%

Green hydrogen leaking accidentally from a motor vehicle in confined space: A study on the effectiveness of a ventilation system

International audienc

Assessment of an accidental hydrogen leak from a vehicle tank in a confined space

International audienceThis study aims to characterize some safety aspects by examining the geometries of the infrastructure, currently used by societies, against the accumulation of hazardous hydrogen clouds during an accidental leak in areas with limited ventilation. Using ANSYS FLUENT as a modeling tool, the influence of garage roof shape; pyramidal and domed roof compared with the basic model (flat roof), for different leak times, on dispersion and stratification of hydrogen layers, is analyzed. As a result, the domed roof promotes to have a lower hydrogen concentration and presents two remarkable peaks of the Richardson number (Ri) with the highest value more than 2 × 105, which is three times higher than the flat roof. Besides, the influence of the leak time on the dynamic of the flow, concentration, and stratification process are observed: the mole fraction of hydrogen is more than 0.25 after 1 h of leak, whereas it is lower than 0.05 after 100 s. The volume flow and therefore the flammable volume increase. This study highlights the importance of geometrical and sizing parameters on the characteristics of hydrogen leaks and subsequently gives insights to establish performance standards for the availability and reliability of safety critical systems

Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model

Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation. Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production. Recent advances in Artificial Intelligence have been very promising. Particularly, Deep Learning technologies have achieved great results in short-term time-series forecasting. Thus, it is very suitable to use these techniques for solar energy production forecasting. In this work, a combination of a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Transformer was used for solar energy production forecasting. Besides, a clustering technique was applied for the correlation analysis of the input data. Relevant features in the historical data were selected using a self-organizing map. The hybrid CNN-LSTM-Transformer model was used for forecasting. The Fingrid open dataset was used for training and evaluating the proposed model. The experimental results demonstrated the efficiency of the proposed model in solar energy production forecasting. Compared to existing models and other combinations, such as LSTM-CNN, the proposed CNN-LSTM-Transformer model achieved the highest accuracy. The achieved results show that the proposed model can be used as a trusted forecasting technique that facilitates the integration of solar energy into grids

Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model

Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation. Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production. Recent advances in Artificial Intelligence have been very promising. Particularly, Deep Learning technologies have achieved great results in short-term time-series forecasting. Thus, it is very suitable to use these techniques for solar energy production forecasting. In this work, a combination of a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Transformer was used for solar energy production forecasting. Besides, a clustering technique was applied for the correlation analysis of the input data. Relevant features in the historical data were selected using a self-organizing map. The hybrid CNN-LSTM-Transformer model was used for forecasting. The Fingrid open dataset was used for training and evaluating the proposed model. The experimental results demonstrated the efficiency of the proposed model in solar energy production forecasting. Compared to existing models and other combinations, such as LSTM-CNN, the proposed CNN-LSTM-Transformer model achieved the highest accuracy. The achieved results show that the proposed model can be used as a trusted forecasting technique that facilitates the integration of solar energy into grids