Browsing by Author "Arteche, Jose Antonio"
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Item Beach carrying capacity management under Covid-19 era on the Basque Coast by means of automated coastal videometry(2021-07-01) Epelde, Irati; Liria, Pedro; de Santiago, Iñaki; Garnier, Roland; Uriarte, Adolfo; Picón, Artzai; Galdrán, Adrián; Arteche, Jose Antonio; Lago, Alberto; Corera, Zurik; Puga, Iñaki; Andueza, Jose Luis; Lopez, Gabriel; COMPUTER_VISIONThis paper describes the methodology followed to implement social distancing recommendations in the COVID-19 context along the beaches of the coast of Gipuzkoa (Basque Country, Northern Spain) by means of automated coastal videometry. The coastal videometry network of Gipuzkoa, based on the KostaSystem technology, covers 14 beaches, with 12 stations, along 50 km of coastline. A beach user detection algorithm based on a machine learning approach has been developed allowing for automatic assessment of beach attendance in real time at regional scale. For each beach, a simple classification of occupancy (low, medium, high, and full) was estimated as a function of the beach user density (BUD), obtained in real time from the images and the maximum beach carrying capacity (BCC), estimated based on the minimal social distance recommended by the authorities. This information was displayed in real time via a web/mobile app and was simultaneously sent to beach managers who controlled the beach access. The results showed a strong receptivity from beach users (more than 50.000 app downloads) and that real time information of beach occupation can help in short-term/daily beach management. In the longer term, the analysis of this information provides the necessary data for beach carrying capacity management and can help the authorities in controlling and in determining their maximum capacity.Item Fast method for slag characterization during ladle furnace steelmaking process based on spectral reflectance(2017) Vicente, A.; Macaya, Inaki; Picon, Artzai; Arteche, Jose AntonioHyperspectral imaging reflectance analysis has proven to be successful in online characterization applications such as material recycling [1], soil composition analysis [2], quality control [3] among others. The measurement of a narrow spectral reflectance of specific materials allows the use of feature extraction and regression machine learning techniques to classify the material into a specific group or estimate some chemical parameters under controlled conditions. A method for Fast slag composition estimation on the ladle furnace process, together with the steel composition information from in-process steel spectrometers, would allow implementing thermo-dynamical equilibrium models to optimize the use of steel additives to obtain a target steel grade at the optimal additive cost. In this work, we present a fast method for slag characterization which is based on the indirect analysis of the spectral reflectance of the slag. This method is based on a normalization procedure to remove the specular component of the spectra, a calibration method to correct lighting conditions and a spectral feature extraction algorithm combined with a SVr (Support vector regression) based regression method. A system consisting of a hyperspectral imaging system and a calibration method has been constructed. The system has been trained with more than 600 real slag samples taken from ladle furnace at different ArcelorMittal steel plants. In order to cover the whole slag oxidation process, three slag samples were taken at each heat. Each sample was analysed by XRF spectroscopy and the regression system was trained to map the values for CaO, SiO2, .S, FeO, MnO Al2O3, MgO, P2O5 obtaining composition errors below 10% on the calibrated ladle furnace oxidation process. The estimated slag composition was used to feed a thermo-dynamical equilibrium model that, together with the steel composition from the in-process spectrometer estimates the required additives for the specific steel grade. This showed lower additive costs than manual additive estimation with equivalent final steel quality.Item Ladle furnace slag characterization through hyperspectral reflectance regression model for secondary metallurgy process optimization(2017-11-13) Picon, Artzai; Vicente Rojo, Asier; Rodriguez-Vaamonde, Sergio; Armentia, Jorge; Arteche, Jose Antonio; Macaya, Inaki; Vicente, Asier; Tecnalia Research & Innovation; COMPUTER_VISION; INDUSTRY_THINGSIn steelmaking process, close control of slag evolution is as important as control of steel composition. However, there are no industrially consolidated techniques that allow in-situ analysis of the slag chemical composition, as in the case of steel with OES-spectrometers. In this work, a method to analyze spectral reflectance of ladle furnace slag samples to estimate their composition is proposed. This method does not require sample preprocessing and is based on a regression algorithm that mathematically maps the spectral reflectance of the slag with its actual composition with errors lower than 10%. Specifically designed normalization and calibration steps have been proposed to allow a global model training with data from different locations. This allows real-time monitoring of the thermodynamical state of the steel process by feeding a thermodynamic equilibrium optimization model. The system has been validated on several ArcelorMittal locations achieving process savings of 0.71 Euro per liquid steel tons.Item Magnetic field-based arc stability sensor for electric arc furnaces(2020-02) Vicente, Asier; Picon, Artzai; Arteche, Jose Antonio; Linares, Miguel; Velasco, Arturo; Sainz, Jose Angel; COMPUTER_VISIONDuring the last decades the strategy to define the optimal Electric Arc Furnaces (EAF) electrical operational parameters has been constantly evolving. Foaming slag practice is currently used to allow high power factors that ensures higher energy efficiency. However, this performance depends on strict electric arc stability control. Control strategies for these are normally defined for alternating current furnaces (AC EAF) and are based on intrusive and highly expensive systems. In this work we analyze the variation of the magnetic field vector around the direct current EAF (DC EAF) and its relationship with arc stability. We propose a cheap stability control system with no installation or integration requirements and thus, easily implementable to both AC and DC EAFs. To this end we have built a non-intrusive and low-cost 3-axis Hall-effect sensor that can be mounted neighboring the furnace’s electrical bars. The sensor allows acquiring the magnetic field magnitude and orientation that provides a newly defined arc stability factor metric. This proposed Arc Stability Index has been compared with three different alternative well established and more expensive measurement methodologies obtaining with similar results. The proposed index serves as a closed loop signal to the electrical regulation for controlling the arc voltage, ensuring the most convenient arc length that guaranties non-instabilities. The new system was developed and industrially validated at two different DC EAF’s in ArcelorMittal demonstrating an improvement of 6.7 kWh per Liquid steel ton during the evaluated period and a time reduction of 1.1 min per heat over the current standard procedure. Additional validation tests were also carried out also in ArcelorMittal AC EAF proving the capability of this technology for both AC and DC of furnaces.Item A Probabilistic Model and Capturing Device for Remote Simultaneous Estimation of Spectral Emissivity and Temperature of Hot Emissive Materials(2021) Picon, Artzai; Alvarez-Gila, Aitor; Arteche, Jose Antonio; Lopez, Gabriel A.; Vicente, Asier; Tecnalia Research & Innovation; COMPUTER_VISION; VISUALEstimating the temperature of hot emissive samples (e.g. liquid slag) in the context of harsh industrial environments such as steelmaking plants is a crucial yet challenging task, which is typically addressed by means of methods that require physical contact. Current remote methods require information on the emissivity of the sample. However, the spectral emissivity is dependent on the sample composition and temperature itself, and it is hardly measurable unless under controlled laboratory procedures. In this work, we present a portable device and associated probabilistic model that can simultaneously produce quasi real-time estimates for temperature and spectral emissivity of hot samples in the [0.2, 12.0μm ] range at distances of up to 20m . The model is robust against variable atmospheric conditions, and the device is presented together with a quick calibration procedure that allows for in field deployment in rough industrial environments, thus enabling in line measurements. We validate the temperature and emissivity estimates by our device against laboratory equipment under controlled conditions in the [550, 850∘C ] temperature range for two solid samples with well characterized spectral emissivity’s: alumina ( α−Al2O3 ) and hexagonal boron nitride ( h−BN ). The analysis of the results yields Root Mean Squared Errors of 32.3∘C and 5.7∘C respectively, and well correlated spectral emissivity’s.