Centro de Gestión de la Calidad y del Cambio
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- PublicationUncertainty quantification and global sensitivity analysis of continuous distillation considering the interaction of parameter uncertainty with feed variability(Elsevier, 2021-05-18) Gozálvez Zafrilla, José Marcial; García Díaz, Juan Carlos; Santafé Moros, María Asunción; Dpto. de Ingeniería Química y Nuclear; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Instituto Universitario de Seguridad Industrial, Radiofísica y Medioambiental; Centro de Gestión de la Calidad y del Cambio[EN] In this work, uncertainty and sensitivity analyses were applied to study the joint effects of model parameter uncertainty and feed variability on the response of a computational code for methanol-water continuous distillation. First, model parameter uncertainty (liquid-vapour equilibrium (VLE), enthalpy and tray efficiency) was characterised using existing experimental data. Afterwards, three tower configurations working at two operational modes (fixed product composition and fixed operation conditions) were studied at three feed variability levels. Morris analysis revealed the high importance of the VLE and efficiency-related factors. Sobol sensitivity analysis determined with more precision the sensitivity of the response to the parameters and detected non-linear effects and interactions. The Monte Carlo propagation method allowed obtaining the uncertainty margins as a function of feed variability. The results showed high impact of the model parameter uncertainty and encourage the use of the methods shown to obtain robust designs and quantify simulation accuracy.
- PublicationMonitorización del proceso de galvanizado por inmersión en baño caliente de zinc(Compobell, 2021-07-15) García Díaz, Juan Carlos; Trull Domínguez, Óscar; Peiró Signes, Ángel; Dpto. de Organización de Empresas; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio[ES] La industria del automóvil requiere de la utilización en grandes cantidades de acero galvanizado. El proceso de obtención industrial más utilizado en España es el de galvanizado continuo por inmersión en baño caliente de zinc. El proceso del baño es altamente dependiente de las cantidades de aluminio presentes en el baño, lo cual requiere un control exhaustivo de calidad. En este artículo proponemos un método efectivo de control de calidad basado en gráficos de control basado en residuos que ha sido aplicado en la industria. Se muestra el método empleado y los resultados obtenidos.
- PublicationPackaging Process Optimization in MultiheadWeighers with Double-Layered Upright and Diagonal Systems(MDPI AG, 2021-05-04) Garcia-Jimenez, Rafael; García Díaz, Juan Carlos; Pulido-Rojano, Alexander De Jesus; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Universidad Simón Bolivar; Universitat Politècnica de València[EN] In multihead weighers, packaging processes seek to find the best combination of passage hoppers whose product content provides a total package weight as close as possible to its (nominal) label weight. The weighing hoppers arranged in these machines dispense the product quantity that each package contains through computer algorithms designed and executed for this purpose. For its part, in the packaging process for double-layered multihead weighers, all hoppers are arranged in two levels. The first layer comprises a group of weighing hoppers, and the second comprises a set of booster hoppers placed uprightly or diagonally to each weighing hopper based on design of the machine. In both processes, the initial machine configuration is the same; however, the hopper selection algorithm works differently. This paper proposes a new packaging process optimization algorithm for double-layer upright and diagonal machines, wherein the hopper subset combined has previously been defined, and the packaging weight is expressed as actual values. As part of its validation, product filling strategies were implemented for weighing hoppers to assess the algorithm in different scenarios. Results from the process performance metrics prove that the new algorithm improves processes by reducing variability. In addition, results reveal that some machine configurations were also able to improve their operation.
- PublicationActitudes de los alumnos de secundaria hacia la estadística(Compobell, 2021-07-15) Peiró Signes, Ángel; Trull Domínguez, Óscar; Segarra Oña, María del Val; García Díaz, Juan Carlos; Dpto. de Organización de Empresas; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio[ES] Los estudiantes universitarios reportan un alto grado de ansiedad y una menor confianza en sí mismos cuando se enfrentan a asignaturas estadísticas. Uno de los factores fundamentales de estas actitudes se generó durante su etapa en educación secundaria. Las actitudes inadecuadas y la ansiedad se traducen en un bajo rendimiento académico. En este estudio evaluamos las actitudes y la ansiedad hacia la estadística de un grupo de estudiantes de secundaria para identificar los niveles de estos alumnos, con el objetivo de identificar aquellas actitudes más negativas que puedan causar problemas posteriores en las enseñanzas universitarias. Para el estudio empleamos el cuestionario de actitudes hacia la estadística (MSATS) en una muestra de 95 estudiantes de secundaria de un instituto. Los resultados indican que los niveles más bajos se dan en las actitudes de utilidad y agrado que invitan a pensar en la necesidad de orientar las actividades hacia aplicaciones prácticas y cercanas a las necesidades actuales de los jóvenes.
- PublicationPackaging process optimization for multihead weighing machines with vertical and diagonal double-layered systems: A bi-level approach(Elsevier, 2024-04-15) García-Jiménez, Rafael; García Díaz, Juan Carlos; Pulido-Rojano, Alexander D.; Camacho-Vallejo, José-Fernando; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; REFRACTARIOS ESPECIALES, S.A.[EN] Bi-level programming offers a modeling approach for solving problems that involve two decision makers, each with their own objective. This research addresses a bi-level programming application to multihead, vertical, and diagonal double-layered machines. In these machines, hoppers are distributed across two levels: the upper level consists of weighing hoppers that weigh the product coming from the feeding hoppers. The weighed product is then deposited into the hoppers on the lower level, known as booster hoppers, located beneath them in a vertical or diagonal position, depending on the machine type. Simultaneously, they receive a new product to weigh. In the proposed model, weighing hoppers are associated with the leader¿s decision variables and optimize the target weight, ensuring it is as close as possible to the weight stated on the label, while also being higher. Meanwhile, booster hoppers are assigned to the follower¿s decision variables, optimizing hopper priority. Hopper priority is measured by the time elapsed from the moment the product arrives at the weighing hoppers until it is loaded onto a pack (measured in the number of processed packs). This model has been tested with different filling and combination strategies, considering factors such as the number of heads in the machine, product type (fusilli and ravioli), among others. The results demonstrate that the model successfully reduces the standard deviation of the produced packages for specific combinations of factors. Furthermore, it was observed that response times on each machine increase as the number of heads or the number of hoppers to be combined grows. This is a result of the increased number of combinations required to find the optimal solution. However, this heightened complexity leads to greater effectiveness and more accurate results in terms of the precision of the content delivered to the customer. The proposed solution methodology identifies the optimal machine configuration for minimizing weight deviation in each package. Additionally, it prioritizes the utilization of machines in the lower level of decisions. Consequently, we achieve optimal operating conditions that minimize package overweight, material overcost, and appropriately select the weighing machines. Managerial insights are provided to underscore that customer satisfaction and improved business performance are outcomes of this study.
- PublicationFault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data(Elsevier, 2012-04) Debón Aucejo, Ana María; García Díaz, Juan Carlos; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Universitat Politècnica de València[EN] Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures. The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to examine their use in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures.
- PublicationStability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain(MDPI AG, 2020-04) Trull Domínguez, Óscar; García Díaz, Juan Carlos; Troncoso, Alicia; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Agencia Estatal de Investigación[EN] Electricity management and production depend heavily on demand forecasts made. Any mismatch between the energy demanded with respect to that produced supposes enormous losses for the consumer. Transmission System Operators use time series-based tools to forecast accurately the future demand and set the production program. One of the most effective and highly used methods are Holt-Winters. Recently, the incorporation of the multiple seasonal Holt-Winters methods has improved the accuracy of the predictions. These forecasts, depend greatly on the parameters with which the model is constructed. The forecasters need to deal with these parameters values when operating the model. In this article, the parameters space of the multiple seasonal Holt-Winters models applied to electricity demand in Spain is analysed and discussed. The parameters stability analysis leads to forecasters better understanding the behaviour of the predictions and managing their exploitation efficiently. The analysis addresses different time windows, depending on the period of the year as well as different training set sizes. The results show the influence of the calendar effect on these parameters and if it is necessary or not to update them in order to obtain a good accuracy over time.
- PublicationAttitudes towards statistics in secondary education: Findings from fsQCA(MDPI AG, 2020-05) Peiró Signes, Ángel; Trull Domínguez, Óscar; Segarra Oña, María del Val; García Díaz, Juan Carlos; Dpto. de Organización de Empresas; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio[EN] Students report a high degree of anxiety and reduced self-confidence when facing statistical subjects, especially in secondary education. This anxiety turns into poor academic performance. Most studies have used linear models for studying the interrelation between different attitudes and proving their impact on performance or related variables. This study uses a different approach to explain and better understand the causal patterns of factors stimulating lower levels of anxiety in students when facing statistics in secondary education. We employed the Multi-factorial Scale of Attitudes Toward Statistics (MSATS) and fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 95 secondary school students in Spain. We identified the recipes or causal combination of factors, leading to low and high levels of anxiety. The results indicate that self-confidence and motivation are important factors in these recipes, but there is no single necessary condition that ensures lower levels of anxiety.
- PublicationElectricity Forecasting Improvement in a Destination Using Tourism Indicators(MDPI AG, 2019-07-03) Trull Domínguez, Óscar; Peiró Signes, Ángel; García Díaz, Juan Carlos; Dpto. de Organización de Empresas; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio[EN] The forecast of electricity consumption plays a fundamental role in the environmental impact of a tourist destination. Poor forecasting, under certain circumstances, can lead to huge economic losses and air pollution, as prediction errors usually have a large impact on the utilisation of fossil fuel-generation plants. Due to the seasonality of tourism, consumption in areas where the industry represents a big part of the economic activity follows a different pattern than in areas with a more regular economic distribution. The high economic impact and seasonality of the tourist activity suggests the use of variables specific to it to improve the electricity demand forecast. This article presents a Holt¿Winters model with a tourism indicator to improve the effectiveness on the electricity demand forecast in the Balearic Islands (Spain). Results indicate that the presented model improves the accuracy of the prediction by 0.3%. We recommend the use of this type of model and indicator in tourist destinations where tourism accounts for a substantial amount of the Gross Domestic Product (GDP), we can control a significant amount of the flow of tourists and the electrical balance is controlled mainly by fossil fuel power plants.
- PublicationMunicipal water demand forecasting: Tools for intervention time series(Taylor & Francis, 2011) Herrera Fernández, Antonio Manuel; García Díaz, Juan Carlos; Izquierdo Sebastián, Joaquín; Pérez García, Rafael; Escuela Técnica Superior de Ingeniería de Telecomunicación; Dpto. de Matemática Aplicada; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Instituto Universitario de Matemática Multidisciplinar; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Ministerio de Ciencia e Innovación; Generalitat ValencianaThis article introduces some approaches to common issues arising in real cases of water demand prediction. Occurrences of negative data gathered by the network metering system and demand changes due to closure of valves or changes in consumer behavior are considered. Artificial neural networks (ANNs) have a principal role modeling both circumstances. First, we propose the use of ANNs as a tool to reconstruct any anomalous time series information. Next, we use what we call interrupted neural networks (I-NN) as an alternative to more classical intervention ARIMA models. Besides, the use of hybrid models that combine not only the modeling ability of ARIMA to cope with the time series linear part, but also to explain nonlinearities found in their residuals, is proposed. These models have shown promising results when tested on a real database and represent a boost to the use and the applicability of ANNs.