DeustoTeka Examinando por Autor "Alberdi Celaya, Elisabete"

Examinando por Autor "Alberdi Celaya, Elisabete"

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Analysis of the air quality of the Basque Autonomous Community using spatial interpolation
(MDPI, 2020-05-20) Alberdi Celaya, Elisabete; Álvarez González, Irantzu; Hernández Guerra, Heber; Oyarbide Zubillaga, Aitor; Goti Elordi, Aitor
This work presents the results obtained from a spatial modeling and analysis process on pollutants measured in the air through forty-three monitoring stations located in the three provinces of the Basque Autonomous Community (Spain). The pollutants measured correspond to the set of nitrogen oxides (nitric oxide, NO; nitrogen dioxide, NO2; and nitrogen oxides, NOx) and atmospheric particulate matter with a diameter less than or equal to 10 micrometers (PM10). The objective of this work was to generate a map of the pollutants that exhaustively covers the entire area of the Basque Autonomous Community using geostatistical techniques, in such a way that it serves as a basis for short and midterm environmental studies.
Application of the k-prototype clustering approach for the definition of Geostatistical estimation domains
(MDPI, 2023-02-01) Hernández Guerra, Heber; Alberdi Celaya, Elisabete; Goti Elordi, Aitor ; Oyarbide Zubillaga, Aitor
The definition of geostatistical domains is a stage in the estimation of mineral resources, in which a sample resulting from a mining exploration process is divided into zones that show homogeneity or minimal variation in the main element of interest or mineral grade, having geological and spatial meaning. Its importance lies in the fact that the quality of the estimation techniques, and therefore, the correct quantification of the mineral resource, will improve in geostatistically stationary areas. The present study seeks to define geostatistical domains of estimation for a mineral grade, using a non-traditional approach based on the k-prototype clustering algorithm. This algorithm is based on the k-means paradigm of unsupervised machine learning, but it is exempt from the one-time restriction on numeric data. The latter is especially convenient, as it allows the incorporation of categorical variables such as geological attributes in the grouping. The case study corresponds to a hydrothermal gold deposit of high sulfidation, located in the southern zone of Peru, where estimation domains are defined from a historical record of data recovered from 131 diamond drill holes and 37 trenches. The characteristics directly involved were the gold grade (Au), silver grade (Ag), type of hydrothermal alteration, and type of mineralization. The results obtained showed that clustering with k-prototypes is an efficient approach and can be used as an alternative or complement to the traditional methodology.
Comparison of trivariate copula-based conditional quantile regression versus machine learning methods for estimating copper recovery
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-02) Hernández Guerra, 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
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
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.
A guide for the food industry to meet the future skills requirements emerging with industry 4.0
(MDPI Multidisciplinary Digital Publishing Institute, 2020-04-14) Akyazi, Tugçe; Goti Elordi, Aitor; Oyarbide Zubillaga, Aitor; Alberdi Celaya, Elisabete; Báyon, Félix
The food industry has recently faced rapid and constant changes due to the current industrial revolution, Industry 4.0, which has also profoundly altered the dynamics of the industry overall. Due to the emerging digitalisation, manufacturing models are changing through the use of smart technologies, such as robotics, Artificial Intelligence (AI), Internet of Things (IoT), machine learning, etc. They are experiencing a new phase of automation that enables innovative and more efficient processes, products and services. The introduction of these novel business models demands new professional skills requirements in the workforce of the food industry. In this work, we introduce an industry-driven proactive strategy to achieve a successful digital transformation in the food sector. For that purpose, we focus on defining the current and near-future key skills and competencies demanded by each of the professional profiles related to the food industry. To achieve this, we generated an automated database of current and future professions and competencies and skills. This database can be used as a fundamental roadmap guiding the sector through future changes caused by Industry 4.0. The interest shown by the local sectorial cluster and related entities reinforce the idea. This research will be a key tool for both academics and policy-makers to provide well-developed and better-oriented continuous training programs in order to reduce the skill mismatch between the workforce and the jobs.
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
Influence of the sampling density in the coestimation error of a regionalized locally stationary variable
(MDPI AG, 2020-01-21) Hernández Guerra, Heber; Alberdi Celaya, Elisabete; Goti Elordi, Aitor
In the present study, the influence of the sampling density on the coestimation error of a regionalized, locally stationary and geo-mining nature variable is analyzed. The case study is two-dimensional (2D) and synthetic-type, and it has been generated using a non-conditional Sequential Gaussian Simulation (SGS), with subsequent transformation to Gaussian distribution, seeking to emulate the structural behavior of the aforementioned variable. A primary and an auxiliary variable with different spatial and statistical properties are constructed using the same methodology. The collocated ordinary cokriging method has been applied, in which the auxiliary variable is spatially correlated with the primary one and it is known exhaustively. Fifteen sampling densities are extracted from the target population of the primary variable, which are compared with the simulated values after performing coestimation. The obtained results follow a potential function that indicates the mean global error (MGE) based on the sampling density percentage (SDP) (MGE = 1.2366 · SDP−0.224).
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
Metallurgical copper recovery prediction using conditional quantile regression based on a copula model
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-07) Hernández Guerra, 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.
Modeling the municipal waste collection using genetic algorithms
(MDPI AG, 2020-04-27) Alberdi Celaya, Elisabete; Urrutia, Leire; Goti Elordi, Aitor; Oyarbide Zubillaga, Aitor
Calculating adequate vehicle routes for collecting municipal waste is still an unsolved issue, even though many solutions for this process can be found in the literature. A gap still exists between academics and practitioners in the field. One of the apparent reasons why this rift exists is that academic tools often are not easy to handle and maintain by actual users. In this work, the problem of municipal waste collection is modeled using a simple but efficient and especially easy to maintain solution. Real data have been used, and it has been solved using a Genetic Algorithm (GA). Computations have been done in two different ways: using a complete random initial population, and including a seed in this initial population. In order to guarantee that the solution is efficient, the performance of the genetic algorithm has been compared with another well-performing algorithm, the Variable Neighborhood Search (VNS). Three problems of different sizes have been solved and, in all cases, a significant improvement has been obtained. A total reduction of 40% of itineraries is attained with the subsequent reduction of emissions and costs.
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
Skills needs of the civil engineering sector in the european union countries: current situation and future trends
(MDPI AG, 2020-10-16) Akyazi, Tugçe; Álvarez González, Irantzu; Alberdi Celaya, Elisabete; Oyarbide Zubillaga, Aitor; Goti Elordi, Aitor; Báyon, Félix
The construction sector has always occupied a strategic place in the European economy. The European construction industry suffered during the 2007–2008 global financial crisis, and today the sector is undergoing a recovery process. Among all the construction subsectors, civil engineering has the highest growth rate. Currently, the sector has to face profound industrial changes emerging with digital transformations (Industry 4.0), sustainability, climate change and energy efficiency. To promote the growth of the civil engineering sector and accelerate the recovery, we need to create a highly qualified and competent workforce that can handle the challenges coming up with the technological progress and global competitiveness. The main condition to achieve this capable workforce is to define the expected evolution of skills requirements. For that purpose, our work focuses on identifying current and near-future key skills required by the civil engineering occupations. To achieve this, we developed an automated sectoral database for the current and near-future skills requirements of the selected professional profiles. It is our belief that this sectoral database is a fundamental framework that will guide the sector through the future changes. We also believe that our research can be used as a key tool for construction companies, policy-makers, academics and training centers to develop well-designed and efficient training programs for upskilling and reskilling the workforce.
Skills requirements for the European machine tool sector emerging from its digitalization
(MDPI AG, 2020-12-13) Akyazi, Tugçe; Goti Elordi, Aitor; Oyarbide Zubillaga, Aitor; Alberdi Celaya, Elisabete; Carballedo Morillo, Roberto; Ibeas, Rafael; García Bringas, Pablo
The machine tool industry, which is the starting point of all the metal producing activities, is presently undergoing rapid and continuous changes as a result of the fourth industrial revolution Industry 4.0. Manufacturing models are profoundly transforming with emerging digitalization. Smart technologies like artificial intelligence (AI), big data, the Internet of Things (IoT), digital twin, allow the machine tool companies to optimize processes, increase efficiency and reduce waste through a new phase of automation. These technologies, as well, enable the machine tool producers to reach the aim of creating products with improved performance, extended life, high reliability that are eco-efficient. Therefore, Industry 4.0 could be perceived as an invaluable opportunity for the machine tool sector, only if the sector has a competent workforce capable of handling the implementation of new business models and technological developments. The main condition to create this highly qualified workforce is reskilling and upskilling of the current workforce. Once we define the expected evolution of skills requirements, we can clarify the skills mismatch between the workers and job profiles. Only then, we can reduce them by delivering well-developed trainings. For this purpose, this article identifies the current and foreseen skills requirements demanded by the machine tool industry workforce. To this end, we generated an integrated database for the sector with the present and prospective skills needs of the metal processing sector professionals. The presented sectoral database is a fundamental structure that will make the sector acquire targeted industrial reforms. It can also be an essential instrument for machine tool companies, policymakers, academics and education or training centers to build well-designed and effective training programs to enhance the skills of the labor force.
Validation of real case solving (RCS) methodology as an efficient engineering learning tool
(MDPI AG, 2020-12-19) Goti Elordi, Aitor; Akyazi, Tugçe; Calle, Alberto de la; Oyarbide Zubillaga, Aitor; Alberdi Celaya, Elisabete
In recent times, new learning methodologies known as student-based methodologies have been introduced to simplify the learning process for the students and facilitate the acquisition of skills for them. Among them, problem based learning (PBL) and project-based learning (PjBL) are widely used methods in the world of education. Real case solving (RCS) is a variant of the PBL where students solve real cases through the application of the PBL methodology. RCS seems to be a relevant approach for educators, but it has an apparently limited implementation degree at the academic level. This article presents the successful implementation of four different RCS approaches in the lecturing process in five different classes in the engineering degree of University of Deusto. The initiative has been analyzed both quantitative and qualitatively; the overall performance and success rate of the students were compared with the ones acquired from conventional teaching methods. The results were found to be promising, demonstrating a significantly better performance than the traditional teaching methodologies. The successful results encouraged the university to continue working further in this direction.

Examinando por Autor "Alberdi Celaya, Elisabete"

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