DeustoTeka Examinando por Autor "Goti Elordi, Aitor"

Examinando por Autor "Goti Elordi, Aitor"

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Application of the k-prototype clustering approach for the definition of Geostatistical estimation domains
(MDPI, 2023-02-01) Hernández, 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.
¿Atraerá el BIG- DATA al P-HACKING al mundo de la ingeniería industrial?
(Publicaciones DYNA SL, 2019-05) Goti Elordi, Aitor; Bom, Pedro; Campos Granados, José Antonio; Galar-Pascual, Diego
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
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 and application of a multi-objective tool for thermal design of heat exchangers using neural networks
(MDPI AG, 2021-05) Andrés Honrubia, José Luís de; Gaviria de la Puerta, José; Cortés Martínez, Fernando; Aguirre Larracoechea, Urko; Goti Elordi, Aitor; Retolaza, Jone
This paper presents the design of a multi-objective tool for sizing shell and tube heat exchangers (STHX), developed under a University/Industry collaboration. This work aims to show the feasibility of implementing artificial intelligence tools during the design of Heat Exchangers in industry. The design of STHX optimisation tools using artificial intelligence algorithms is a visited topic in the literature, nevertheless, the degree of implementation of this concept is uncommon in industrial companies. Thus, the challenge of this research consists of the development of a tool for the design of STHX using artificial intelligence algorithms that can be used by industrial companies. The approach is implemented using a simulated dataset contrasted with ARA TT, the company taking part in the project. The given dataset to develop a theoretical STHX calculator was modeled using MATLAB. This dataset was used to train seven neural networks (NNs). Three of them were mono-objective, one per objective to predict, and four were multi-objective. The last multi-objective NN was used to develop an inverse neural network (INN), which is used to find the optimal configuration of the STHXs. In this specific case, three design parameters, the pressure drop on the shell side, the pressure drop on the tube side and heat transfer rate, were jointly and successfully optimised. As a conclusion, this work proves that the developed tool is valid in both terms of effectiveness and user-friendliness for companies like ARA TT to improve their business activity.
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.
The effects of industry 4.0 on steel workforce: identifying the current and future skills requirements of the steel sector and developing a sectorial database
(Springer Science and Business Media Deutschland GmbH, 2024) Akyazi, Tugçe; Goti Elordi, Aitor; Báyon, Félix
In recent years, the European steel sector has undergone constant and substantial changes due to the digitalization of steel production as well as persistent demands to place the industry on a further environmentally sustainable footing. However, the majority of the experienced workforce in the metallurgy sector do not have the necessary technological competencesCompetences. The steel sector is in need of a highly qualified labour force to keep up with the growing digitalization, and to manage the implementation of new business models. Creating a competent labour force with updated skills is only possible through addressing the current skills needs and trends as well as anticipating the future ones. This chapter is developed to respond to this need and guide the sector through performing a detailed desk analysis and generating a sectoral occupational database. We believe that the sectoral database would serve the steel industry as a crucial tool for all the future technological and organizational changes. Steel manufacturers, universities, trainingTraining and education centres are aimed to be the end-users of the database, since they are responsible from the development, redesign and delivery of training programs.
The historic importance and continued relevance of steel-making in Europe
(Springer Science and Business Media Deutschland GmbH, 2024) Weinel, Martin; Antonazzo, Luca; Stroud, Dean; Behrend, Clara; Colla, Valentina; Goti Elordi, Aitor; Schröder, Antonius Johannes
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
Identifying the skills requirements related to industrial symbiosis and energy efficiency for the European process industry
(Springer, 2023-07-20) Akyazi, Tugçe ; Goti Elordi, Aitor; Báyon, Félix ; Kohlgrüber, Michael; Schröder, Antonius Johannes
The need for sustainable production, efficient use of resources, energy efficiency and reduction in CO2 emission are currently the main drivers that are transforming the European process industry besides Industry 4.0. Since the potential of industrial symbiosis (IS) and energy efficiency (EE) about environmental, economic and social issues has been discovered, the interest in them is gradually increasing. The funding and investments for IS and EE are highly encouraged by the European Commission, while more and more policies as well as research and innovation (R&I) activities are initiated to promote European industry’s advancement towards a circular economy and CO2 neutrality. The aim is to maintain the competitiveness and economic progress of the industry. The key to build a competitive and sustainable European manufacturing industry is to create a competent, highly qualified workforce that is capable of handling the new business models coming with IS and EE requirements and digital technologies. We can generate this by identifying the skills needs and upskilling and reskilling the current workforce accordingly by delivering the suitable training programmes. Therefore, this work identifies the most critical skills needs related to IS and EE for six different energy-intensive sectors (steel, ceramic, water, cement, chemical and minerals) in Europe. The effect of the digital transformation on the skills needs is as well discussed. The identified skills are aimed to be included in vocational education and training (VET), tertiary education and other kinds of training curricula. We also identify the cross-sectoral most representative job profiles linked with EE and IS in these sectors and demonstrate the methodology for the selection process. Furthermore, we present a key tool for identifying the most significant current and future skills requirements. Also, we define the critical skill gaps of the European process industry using this tool. Once the skill gaps are defined, they can be reduced by delivering well-developed continuous trainings. We also link our work to the respectable ESCO, the European Classification of skills, competences, qualifications and occupations, to attain a common ground with other studies and frameworks, minimise the complexity and contribute to their work. Our work is developed to be an academic and industrial guideline to prepare well-developed training programmes to deliver the needed skills.
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, 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.
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

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