Magnetic field-based arc stability sensor for electric arc furnaces

During 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.Partial financial support of this work by the Basque Govern-ment (Hazitek AURRERAB ZE-2017/00009 and FASIN ZE-2016/0016 Projects) is gratefully acknowledged

A Probabilistic Model and Capturing Device for Remote Simultaneous Estimation of Spectral Emissivity and Temperature of Hot Emissive Materials

Estimating 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.This work was supported in part by the Basque Government (Hazitek AURRERA B: Advanced and Useful REdesign of CSP process for new steel gRAdes) under Grant ZE-2017/00009

Historia general de España

Contiene: Reyes cristianos desde Alfonso VI hasta Alfonso XI en Castilla, Aragon, Navarra y Portugal / por Manuel Colmeiro, 1 v.-- Castilla y Leon durante los reinados de Pedro I, Enrique II, Juan I y Enrique III, Tomo I / por Juan Catalina Garcia. -- 1892. -- Reinado de Carlos III / por Manuel Danvila y Collado, Tomo I. -- 1893. -- Reinado de Carlos IV / por el general Jose Gomez de Arteche, Tomo I. -- 1893PALAU 115612IndicesNota

Heterozygous and Homozygous Variants in SORL1 Gene in Alzheimer's Disease Patients: Clinical, Neuroimaging and Neuropathological Findings

In the last few years, the SORL1 gene has been strongly implicated in the development of Alzheimer’s disease (AD). We performed whole-exome sequencing on 37 patients with early-onset dementia or family history suggestive of autosomal dominant dementia. Data analysis was based on a custom panel that included 46 genes related to AD and dementia. SORL1 variants were present in a high proportion of patients with candidate variants (15%, 3/20). We expand the clinical manifestations associated with the SORL1 gene by reporting detailed clinical and neuroimaging findings of six unrelated patients with AD and SORL1 mutations. We also present for the first time a patient with the homozygous truncating variant c.364C>T (p.R122*) in SORL1, who also had severe cerebral amyloid angiopathy. Furthermore, we report neuropathological findings and immunochemistry assays from one patient with the splicing variant c.4519+5G>A in the SORL1 gene, in which AD was confirmed by neuropathological examination. Our results highlight the heterogeneity of clinical presentation and familial dementia background of SORL1-associated AD and suggest that SORL1 might be contributing to AD development as a risk factor gene rather than as a major autosomal dominant gene.This work was supported by the Instituto de Salud Carlos III (PI17/01067) and AGAUR from the Autonomous Catalan Government (2017SGR1134). Dr. Víctor Antonio Blanco-Palmero is supported by the Instituto de Salud Carlos III (ISCIII, Spanish Biomedical Research Institute) through a “Río Hortega” contract (CM18/0095). Dr. Sara Llamas-Velasco is supported by the Instituto de Salud Carlos III (ISCIII; Spanish Biomedical Research Institute) through a “Juan Rodés” contract (JR 18/00046).S

Fast method for slag characterization during ladle furnace steelmaking process based on spectral reflectance

Hyperspectral 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.Partial financial support of this work by the Basque Government (Etorgai NUPROSS ER-2010/00001 and DAVOS ER-2014/0004 Projects) is gratefully acknowledged

New sensor for Electric Arc Furnaces arc stability control

Publisher Copyright: © ICS 2018 - 7th International Congress on Science and Technology of Steelmaking: The Challenge of Industry 4.0. All rights reserved.During the last decades the strategy to define the optimal Electric Arc Furnaces (EAF) electrical operational parameters has changed several times. Probably one of the major advances has been the development of the foaming slag practice, which allows operating with very high-power factors on the last stages of melting process ensuring higher energy consumption efficiency and reducing the electrode consumption. As 90% of steel using electric route is made in Alternating Current (AC) EAFs, and due to the fact that the arc stability is higher in AC because of shorter arc lengths, most of the efforts done to increase the arc efficiency have been conducted on AC technologies such as Rogowski coils [1] and acoustic signal processing techniques [2]. However, for Direct Current (DC) Electric Arc Furnaces, there are few certificated commercial systems that have proved their validity as voltage regulators to optimize arc stability so far and all of them required high CAPEX and complex sensor installation and integration. In this work we analyse the magnetic field variation vector around the DC EAF and their relationship with the arc stability. This have allowed the development of a cheap stability control system with no installation or integration requirements easily implementable to any EAF. The solution described is based on a non-intrusive and low cost Hall-effect sensor that can be mounted neighbouring the furnace's electrical bars. The sensor captures the magnetic field magnitude and analyses the acquired signal providing an arc stability factor metric. The calculated stability factor serves as a closed loop signal to the electrical regulation PLC for controlling the arc voltage, ensuring the most convenient arc length that guaranties non-instabilities. The new system was developed and industrially installed at two different DC EAF's in ArcelorMittal in Spain demonstrating a clear improvement in the overall energy efficiency of the melting process. Several tests were also carried out in other ArcelorMittal AC EAF in Spain proving the capability of this technology for both types of furnaces.Partial financial support of this work by the Basque Government (Hazitek AURRERAB ZE-2017/00009 and FASIN ZE-2016/0016 Projects) is gratefully acknowledged.Peer reviewe

Ladle furnace slag characterization through hyperspectral reflectance regression model for secondary metallurgy process optimization

In 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.Partial financial support of this work by the Basque Government (Etorgai NUPROSS ER-2010/00001 and DAVOS ER-2014/0004 Projects) is gratefully acknowledged

Beach carrying capacity management under Covid-19 era on the Basque Coast by means of automated coastal videometry

This 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.This work has been supported by the Gipuzkoa Provincial Council, the Zarautz Council, and the Donostia-San Sebastian Council. The au-thors would like to acknowledge the technical assistance and data pro-vided by the Bizkaia Provincial Council. Roland Garnier acknowledges funding from the Provincial Council of Gipuzkoa through the Fellows Gipuzkoa Programme (Ref: 2020-FELL-000007-01

Expanding the clinical and genetic spectrum of SQSTM1-related disorders in family with personality disorder and frontotemporal dementia

Objective:SQSTM1-variants associated with frontotemporal lobar degeneration have been described recently. In this study, we investigated a heterozygous in-frame duplication c.436_462dup p. (Pro146_Cys154dup) in the SQSTM1 gene in a family with a new phenotype characterized by a personality disorder and behavioral variant frontotemporal dementia (bvFTD). We review the literature on frontotemporal dementia (FTD) associated with SQSTM1. Methods: The index case and relatives were described, and a genetic study through Whole Exome Sequencing was performed. The literature was reviewed using Medline and Web of Science. Case reports, case series, and cohort studies were included if they provided information on SQSTM1 mutations associated with FTD. Results: Our patient is a 70-year-old man with a personality disorder since youth, familial history of dementia, and personality disorders with a 10-year history of cognitive decline and behavioral disturbances. A diagnosis of probable bvFTD was established, and the in-frame duplication c.436_462dup in the SQSTM1 gene was identified. Segregation analysis in the family confirmed that both affected sons with personality disorder were heterozygous carriers, but not his healthy 65-year-old brother. A total of 14 publications about 57 patients with SQSTM1-related FTD were reviewed, in which the bvFTD subtype was the main phenotype described (66.6%), with a predominance in men (63%) and positive family history in 61.4% of the cases. Conclusions: We describe a heterozygous in-frame duplication c.436_462dup p.(Pro146_Cys154dup) in the SQSTM1 gene, which affects the zinc-finger domain of p62, in a family with a personality disorder and bvFTD, expanding the genetics and clinical phenotype related to SQSTM1. © 2021 World Federation of Neurology on behalf of the Research Group on Motor Neuron Diseases