Examinando por Autor "Alberdi Celaya, Elisabete"

Mostrando 1 - 7 de 7
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    Comparison of trivariate copula-based conditional quantile regression versus machine learning methods for estimating copper recovery
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-02) Hernández, Heber; Díaz Viera, Martín Alberto; Alberdi Celaya, Elisabete; Goti Elordi, Aitor
    In this study, an innovative methodology using trivariate copula-based conditional quantile regression (CBQR) is proposed for estimating copper recovery. This approach is compared with six supervised machine learning regression methods, namely, Decision Tree, Extra Tree, Support Vector Regression (linear and epsilon), Multilayer Perceptron, and Random Forest. For comparison purposes, an open access database representative of a porphyry copper deposit is used. The database contains geochemical information on minerals, mineral zoning data, and metallurgical test results related to copper recovery by flotation. To simulate a high undersampling scenario, only 5% of the copper recovery information was used for training and validation, while the remaining 95% was used for prediction, applying in all these stages error metrics, such as R2, MaxRE, MAE, MSE, MedAE, and MAPE. The results demonstrate that trivariate CBQR outperforms machine learning methods in accuracy and flexibility, offering a robust alternative solution to model complex relationships between variables under limited data conditions. This approach not only avoids the need for intensive tuning of multiple hyperparameters, but also effectively addresses estimation challenges in scenarios where traditional methods are insufficient. Finally, the feasibility of applying this methodology to different data scales is evaluated, integrating the error associated with the change in scale as an inherent part of the estimation of conditioning variables in the geostatistical context
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    Definition of the future skills needs of job profiles in the renewable energy sector
    (MDPI AG, 2021-05-02) Arcelay Fernández-Meras, Irene; Goti Elordi, Aitor; Oyarbide Zubillaga, Aitor; Akyazi, Tugçe ; Alberdi Celaya, Elisabete; García Bringas, Pablo
    The growth of the renewable energy industry is happening at a swift pace pushed, by the emergence of Industry 4.0. Smart technologies like artificial intelligence (AI), Big Data, the Internet of Things (IoT), Digital Twin (DT), etc. enable companies within the sector of renewable energies to drastically improve their operations. In this sectoral context, where upgraded sustainability standards also play a vital role, it is necessary to fulfil the human capital requirements of the imminent technological advances. This article aims to determine the current skills of the renewable energy industry workforce and to predict the upcoming skill requirements linked to a digital transition by creating a unified database that contains both types of skills. This will serve as a tool for renewable energy businesses, education centers, and policymakers to plan the training itinerary necessary to close the skills gap, as part of the sectoral strategy to achieve a competent future workforce
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    Development of a transdisciplinary research-based framework for the improvement of thermal comfort of schools through the analysis of shading system
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-01) Oregi Isasi, Xabat; Goti Elordi, Aitor; Pérez Acebo, Heriberto; Álvarez González, Irantzu; Eguía Ribero, María Isabel; Alberdi Celaya, Elisabete
    This article investigates a methodology for the application of the design of sunlighting and shading systems in educational settings, focusing on their impact on thermal comfort. As educational environments increasingly recognize the importance of physical comfort in enhancing learning outcomes, this study starts with an analysis of current shading practices and their effectiveness. A user-friendly methodology for assessing sunlight and shading in schools is developed, utilizing a transdisciplinary research approach, with various stakeholders, including educators, architects, and environmental scientists. Through case studies conducted in Zornotza, Spain, the research warns about the detrimental effects of inadequate shading on student well-being and proposes design solutions for each of the cases. Our findings underscore the necessity for innovative design strategies that integrate both passive and active shading solutions, ultimately contributing to healthier, more sustainable learning environments. These innovative strategies can be better oriented at the early stages of the analysis of the problem if transdisciplinary research is applied, advocating for a holistic approach to educational facility design that prioritizes the comfort and success of students.
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    Identifying the future skills requirements of the job profiles related to sustainability in the engineering sector
    (Gökmen Arslan, 2023) Goti Elordi, Aitor; Akyazi, Tugçe; Loroño, Agathe; Alberdi Celaya, Elisabete; Oyarbide Zubillaga, Aitor; Ukar Arrien, Olatz
    The field of engineering has undergone significant evolution over the time. With the advent of newindustrial revolutions and the growing importance of sustainability, the skills necessary to excel as anengineer have changed drastically. To be a competent engineer in the future, and to achieve thepsychological wellbeing of a qualified and up-to-date professional, it is necessary to analyze potentialchanges that may occur in the field and adapt one's skills accordingly. Engineers can stay ahead of thecurve and remain relevant in an ever-changing landscape, only by anticipating and preparing forfuture developments as well as foreseeing the future skills needs. In order to address the need ofidentifying the future skill requirements for engineers, in this work, we created a skills database with astrong focus on sustainability. This database not only integrates current skills, but also foresees andestablishes the skills related to sustainability, which will be needed in the future. For this aim, webenefited from the ESCO database for selecting the engineering job profiles related to sustainabilityas well as the current skills needs of the engineers. On the other hand, we conducted a detailed deskresearch in order to analyse and identify the future skills needs for the selected engineering jobprofiles. The aim of our work is to address the lack of a skills database specifically designed for theengineering field in relation to sustainability. The database is intended to provide end -users withinformation on new skill requirements that may arise from future changes, such as industrial andsustainable shifts
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    Mechanical behavior modeling of containers and octabins made of corrugated cardboard subjected to vertical stacking loads
    (MDPI AG, 2021-05-04) Gallo Laya, Javier; Cortés Martínez, Fernando; Alberdi Celaya, Elisabete; Goti Elordi, Aitor
    The aim of this paper is to characterize the mechanical behavior of corrugated cardboard boxes using simple models that allow an approach to the load capacity and the deformation of the boxes. This is very interesting during a box design stage, in which the box does not exist yet. On the one hand, a mathematical model of strength and deformation of boxes with different geometry is obtained from experiments according to the Box Compression Test and Edge Crush Test standards. On the second hand, a finite element simulation is proposed in which only the material elastic modulus in the compression direction is needed. For that, corrugated cardboard sheets are glued to build billets for testing, and an equivalent elastic modulus is obtained. This idea arises from the fact that the collapse of the box is given by the local bucking of the corrugated cardboard panels, due to the slenderness itself, and the properties in the compression direction are predominant. As a result, the numerical models show satisfactory agreement with experiments, concluding that it is an adequate methodology to simulate in a simple and efficient way this type of boxes built with corrugated cardboard
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    Metallurgical copper recovery prediction using conditional quantile regression based on a copula model
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-07) Hernández, Heber; Díaz Viera, Martín Alberto; Alberdi Celaya, Elisabete; Oyarbide Zubillaga, Aitor; Goti Elordi, Aitor
    This article proposes a novel methodology for estimating metallurgical copper recovery, a critical feature in mining project evaluations. The complexity of modeling this nonadditive variable using geostatistical methods due to low sampling density, strong heterotopic relationships with other measurements, and nonlinearity is highlighted. As an alternative, a copula-based conditional quantile regression method is proposed, which does not rely on linearity or additivity assumptions and can fit any statistical distribution. The proposed methodology was evaluated using geochemical log data and metallurgical testing from a simulated block model of a porphyry copper deposit. A highly heterotopic sample was prepared for copper recovery, sampled at 10% with respect to other variables. A copula-based nonparametric dependence model was constructed from the sample data using a kernel smoothing method, followed by the application of a conditional quantile regression for the estimation of copper recovery with chalcocite content as secondary variable, which turned out to be the most related. The accuracy of the method was evaluated using the remaining 90% of the data not included in the model. The new methodology was compared to cokriging placed under the same conditions, using performance metrics RMSE, MAE, MAPE, and R2. The results show that the proposed methodology reproduces the spatial variability of the secondary variable without the need for a variogram model and improves all evaluation metrics compared to the geostatistical method.
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    Recasting the future of the European steel industry
    (Springer Science and Business Media Deutschland GmbH, 2024) Akyazi, Tugçe; Goti Elordi, Aitor; Alberdi Celaya, Elisabete; Behrend, Clara; Schröder, Antonius Johannes; Colla, Valentina; Stroud, Dean; Antonazzo, Luca; Weinel, Martin