Browsing by Department "Departamento de Industrias"

The Gaussian copula is a powerful tool that has been widely used to model spatial and/or temporal correlated data with arbitrary marginal distributions. However, this kind of model can potentially be too restrictive since it expresses a reflection symmetric dependence. In this paper, we propose a new spatial copula model that makes it possible to obtain random fields with arbitrary marginal distributions with a type of dependence that can be reflection symmetric or not. Particularly, we propose a new random field with uniform marginal distributions that can be viewed as a spatial generalization of the classical Clayton copula model. It is obtained through a power transformation of a specific instance of a beta random field which in turn is obtained using a transformation of two independent Gamma random fields. For the proposed random field, we study the second-order properties and we provide analytic expressions for the bivariate distribution and its correlation. Finally, in the reflection symmetric case, we study the associated geometrical properties. As an application of the proposed model we focus on spatial modeling of data with bounded support. Specifically, we focus on spatial regression models with marginal distribution of the beta type. In a simulation study, we investigate the use of the weighted pairwise composite likelihood method for the estimation of this model. Finally, the effectiveness of our methodology is illustrated by analyzing point-referenced vegetation index data using the Gaussian copula as benchmark. Our developments have been implemented in an open-source package for the R statistical environment.

The most commonly used probabilistic model in reliability studies is the Perfect Renewal Process (PRP), which is characterized by the condition or type of maintenance represented: once the maintenance activities are executed, the equipment is restored to its original condition, leaving it “as good as new.” It is widely used since it represents an optimistic state when an item is replaced, assuming a perfect operational condition of the item after the maintenance. Some models have been developed for determining optimum preventive maintenance (PM) based on different criteria, and almost all aimed at PRP reliability modeling. The contribution of this paper is to analyze a model for determining the optimal preventive maintenance policy for a long time run under PRP and developing a general and chart-based tool for the problem, making it easier to solve the day-to-day practice and operation of equipment. As a result, a generalized chart was developed to support maintenance decisions through the elaboration of an original isometric table and complemented with a step-by-step methodology to determine the optimum time in which the preventive maintenance activities must be implemented. In most cases, these types of maintenance activities will consider a replacement activity.

Renewable energies have a fundamental role in sustainability, with wind power being one of the most important due to its low production costs. Modern wind turbines are becoming bigger and more complex, and their operation and maintenance must be as optimized as possible. In this context, supervisory control and data acquisition systems provide valuable information, but there is no precise methodology for their analysis. To overcome this need, a generalized methodology is proposed to determine the recognition of critical subsystems through alarm analysis and management. The proposed methodology defines each subsystem in a precise way, shows the indicators for the alarms, and presents a theoretical framework for its application using the quantity and activation times of alarms, along with the real downtime. It also considers the transition of states when the wind turbine is operationally inactive. To highlight the proposal’s novelty, the methodology is exemplified with a case study from the Southern Cone, applying the method through a data management and analysis tool. Four critical subsystems were found, with the alarms of wind vanes, anemometers, and emergency speeds being of relevance. The indicators and the graphical tools recommended helped guide the applied analysis.

The proportional hazards model (PHM) is a vital statistical procedure for condition-based maintenance that integrates age and covariates monitoring to estimate asset health and predict failure risks. However, when dealing with multi-covariate scenarios, the PHM faces interpretability challenges when it lacks coherent criteria for defining each covariate’s influence degree on the hazard rate. Hence, we proposed a comprehensive machine learning (ML) formulation with Interior Point Optimizer and gradient boosting to maximize and converge the logarithmic likelihood for estimating covariate weights, and a K-means and Gaussian mixture model (GMM) for condition state bands. Using real industrial data, this paper evaluates both clustering techniques to determine their suitability regarding reliability, remaining useful life, and asset intervention decision rules. By developing models differing in the selected covariates, the results show that although K-means and GMM produce comparable policies, GMM stands out for its robustness in cluster definition and intuitive interpretation in generating the state bands. Ultimately, as the evaluated models suggest similar policies, the novel PHM-ML demonstrates the robustness of its covariate weight estimation process, thereby strengthening the guidance for predictive maintenance decisions.

This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations.

Industrial combustion processes are an important source of particulate matter, causing significant pollution problems that affect human health, and are a major contributor to global warming. The most common method for analyzing the soot emission propensity in flames is the Smoke Point Height (SPH) analysis, which relates the fuel flow rate to a critical flame height at which soot particles begin to leave the reactive zone through the tip of the flame. The SPH and is marked by morphological changes on the flame tip. SPH analysis is normally done through flame observations with the naked eye, leading to high bias. Other techniques are more accurate, but are not practical to implement in industrial settings, such as the Line Of Sight Attenuation (LOSA), which obtains soot volume fractions within the flame from the attenuation of a laser beam. We propose the use of Video Magnification techniques to detect the flame morphological changes and thus determine the SPH minimizing observation bias. We have applied for the first time Eulerian Video Magnification (EVM) and Phase-based Video Magnification (PVM) on an ethylene laminar diffusion flame. The results were compared with LOSA measurements, and indicate that EVM is the most accurate method for SPH determination.

This article proposes an original probabilistic modelling methodology named Virtual Standby (VSB), which enables a practical simulation, analysis, and evaluation of the impact on availability and reliability achieved by potential buffering policies on the performance of complex production systems. Virtual Standby (VSB) corresponds to a design and operational characteristic where some machines under a failure scenario are capable to provide for a limited time, continuity to the subsystems downstream before suffering delay which is currently not considered when assessing availability. This feature plays a relevant role on the propagation of the effect of a failure; indeed, it could prevent the propagation by guaranteeing the isolation time needed to recover from its failure, controlling and reducing the production losses downstream. A case study of the preliminary treatment process of a wastewater treatment facility (WWTF) is developed bearing in mind the systemic behaviour in the event of a failure and the specific features of each equipment. VSB is a big advantage for the representation of this complex processes because, among other things, it considers the impact of buffering policies on the perceived availability of the system. This model allows determining different production levels, with a better and easier fitting of the reliability, availability, and production forecast of the process. Finally, the comparison between the VSB simulation results with traditional procedures that do not consider the operational continuity under a failure scenario confirms the strength and precision of the proposal for complex systems.

Background: Wildfires have caused significant damage in Chile, with critical infrastructure being vulnerable to extreme wildfires. Aim This work describes a methodology for estimating wildfire risk that was applied to an electrical substation in the wildland–urban interface (WUI) of Valparaíso, Chile. Methods Wildfire risk is defined as the product between the probability of a wildfire reaching infrastructure at the WUI and its consequences or impacts. The former is determined with event trees combined with modelled burn probability. Wildfire consequence is considered as the ignition probability of a proxy fuel within the substation, as a function of the incident heat flux using a probit expression derived from experimental data. The heat flux is estimated using modelled fire intensity and geometry and a corresponding view factor from an assumed solid flame. Key results The probability of normal and extreme fires reaching the WUI is of the order of 10−4 and 10−6 events/year, respectively. Total wildfire risk is of the order of 10−5 to 10−4 events/year Conclusions This methodology offers a comprehensive interpretation of wildfire risk that considers both wildfire likelihood and consequences. Implications The methodology is an interesting tool for quantitatively assessing wildfire risk of critical infrastructure and risk mitigation measures.

In the framework of asset management, the effectiveness of an integrated process that manages reliability and maintenance should be adequately and timely evaluated based on a thorough analysis of a series of contributing factors, which should respond entirely to the result of maintenance activities on the performance of assets that make up the production process. According to the above, then arises the motivation for the design and application of tools to determine the effectiveness of such activities in asset management, understanding the latter as a holistic process that involves a diversity of functions and areas within an organization

Soot emissions from flaming combustion are relevant as a significant source of atmospheric pollution and as a source of nanomaterials. Candles are interesting targets for soot characterization studies since they burn complex fuels with a large number of carbon atoms, and yield stable and repeatable flames. We characterized the soot particle size distributions in a candle flame using the planar two-color time-resolved laser induced incandescence (2D-2C TiRe-LII) technique, which has been successfully applied to different combustion applications, but never before on a candle flame. Soot particles are heated with a planar laser sheet to temperatures above the normal flame temperatures. The incandescent soot particles emit thermal radiation, which decays over time when the particles cool down to the flame temperature. By analyzing the temporal decay of the incandescence signal, soot particle size distributions within the flame are obtained. Our results are consistent with previous works, and show that the outer edges of the flame are characterized by larger particles ( 60nm ), whereas smaller particles ( 25 nm ) are found in the central regions. We also show that our effective temperature estimates have a maximum error of 100 K at early times, which decreases as the particles cool.

Wastewater treatment is a critical and necessary task every human settlement is obligated to address. If not, the consequences might be catastrophic, not just for humans but for the ecosystems as well, pushing research into finding new ways to improve wastewater treatment processes to make them safer and more efficient. Hence, there is a need to address matters, such as reliability and maintainability of Wastewater Treatment Plants (WWTP), when analyzing the availability and operational conditions. These should be addressed by analyzing the plant operational effectiveness impact (P-OEI), and in this article specifically, a WWTP study case to identify design flaws or improvement opportunities. A vital aspect of a complex system is to determine the contribution to resilience, reliability, and availability of every element embedded in the system. This is performed by adapting and applying the P-OEI methodology and real data of a WWTP located in Chile. This methodology breaks down the system into several levels of disaggregation similar to RBD methodology, analyzing the upstream for availability and the downstream for the P-OEI analysis from the system itself to the individual elements within subsystems. The potential impact on the overall system’s lack of efficiency is also quantified by an Expected Operational Impact (EOI) index, which is also calculated by the methodology. The P-OEI and EOI analyses performed in this study are powerful tools to assess the design and performance of complex systems and WWTP in particular.

Purpose: This study explores how eWOM, user-generated content trust (UGCT), economic perspective, and social perspective influence travel intention in TikTok users. Unlike previous research, we conceptually differentiate eWOM from UGCT and analyze their effect on travel decisions, providing empirical evidence from an emerging digital context. Design/methodology/approach We applied structural equation modeling (SEM) and artificial neural network (ANN) analysis to a sample of 470 Chilean TikTok users interested in travel. We validated the relationships using SPSS AMOS and used ANN to identify the relative importance of the predictors. Findings: The results show that eWOM directly influences destination trust, economic perspective, and UGCT. In turn, user-generated content trust significantly impacts travel intention, being the most important predictor according to ANN. Economic and social perspectives strengthened trust in content, while the direct relationship between destination trust and travel intention was not significant, highlighting the mediating role of UGCT in tourism decisions. Originality/Value The study contributes by differentiating eWOM from UGCT and demonstrating their specific influence on travel intention. Furthermore, it proposes an innovative approach by integrating SEM and ANN, offering theoretical and practical contributions to the design of digital tourism marketing strategies based on trust and pre-purchase interaction.

Fire spread inside a spacecraft is a constant concern in space travel. Understanding how the fire grows and spreads, and how it can potentially be extinguished is critical for planning future missions. The conditions inside a spacecraft can greatly vary from those encountered on earth, including microgravity, low velocity flows, reduced ambient pressure and high oxygen, and thus affecting the combustion processes. In microgravity, the contributions of thermal radiation from gaseous species and soot can play a critical role in the spread of a flame and the problem has not been fully understood yet. The overall objective of this work is to address this by studying the soot temperature of microgravity flames spreading over a thin solid in microgravity. The experiments presented here were performed as part of the NASA project Saffire IV, conducted in orbit on board the Cygnus resupply vehicle before it re-entered the Earth’s atmosphere. The fuel considered is a thin fabric made of cotton and fiberglass (Sibal) exposed to a forced flow of 20 cm/s in a concurrent flow configuration. Reconstruction of the flame temperature fields is extracted from two color broadband emission pyrometry (B2CP) as the flame propagates over the solid fuel. A methodology, relevant assumptions and its applicability to other microgravity experiments are discussed here. The data obtained shows that the technique provides an acceptable average temperature around K, which remains relatively constant during the spread with an error value smaller than 117 K. The data presented in this work provides a methodology that could be applied to other microgravity experiments to be performed by NASA. It is expected that the results will provide insight for what is to be expected in different conditions relevant for fire safety in future space facilities.

The Old Limachino Tomato is a valuable fruit with exceptional nutritional values and organoleptic sensory properties. However, it suffers from a short shelf-life, compromising post-harvest behavior. As an attempt to improve the fruit’s qualities, Limachino (L) scion was grafted onto rootstock from the rustic landrace Poncho Negro (R). Fruits produced in this graft combination were compared with fruits produced by self-grafted plants (L/L) and from a long-shelf-life cultivar Seminis (LSL). The trials were carried out for 146 days during summer of two consecutive years. Poncho Negro rootstock increased the total number of fruits produced by Limachino scion (L/R). It did not affect the fresh weight of individual fruits but reduced their water content. It has no impact on the Limachino fruit form (quality), a typical characteristic well appreciated by consumers. Fruits produced by LSL exhibited a higher firmness but a lower titratable acidity and antioxidant capacity than L/R and L/L fruits. Panels of 104 untrained final consumers and a trained panel of 13 experts attributed the highest value to L/R fruits and the lowest one to LSL. It was concluded that Poncho Negro rootstock contributes to increasing preferences and the level of acceptability towards Limachino fruits. Further research is needed to develop local technologies in order to expand the production of local tomatoes that are highly valued by consumers.

The effects of salt on the quality of fruits were investigated in order to compare the impact of salt on key fruit properties of the cultivated domesticated tomato species (Solanum lycopersicum) and its wild halophyte relative Solanum chilense. To this end, cherry tomato plants (S. lycopersicum var. cerasiforme) and from accession LA4107 (S. chilense) were maintained for 112 days in the absence or presence of NaCl (40 and 80 mM) in nutrient solution. Among others, salinity decreased fruit weight and increased total soluble solid (TSS) in S. lycopersicum but not in S. chilense. The fruit antioxidant capacity estimated by ferric reducing antioxidant power (FRAP) analysis was higher in S. chilense than in S. lycopersicum and increased in the former while it decreased in the latter in response to NaCl. Salinity increased the lycopene (LYC) content but decreased ß-carotene (b-CAR) concentration in the fruits of S. lycopersicum, while these compounds were not detected in the wild halophyte S. chilense. The oxidative status of salt-treated fruits was more tightly regulated in S. chilense than in S. lycopersicum. The two considered species, however, possess complementary properties and interspecific crosses may therefore be considered as a promising option for the improvement of salt-stress resistance in tomatoes.

In this work, we present the fundamentals of the estimation of the energy efficiency in an industrial coal boiler based in novel optical combustion diagnostics variables and several machine learning regression methods. The total radiation Radt and flame temperature Tf were considered. The inclusion of those variables allows to increase the overall performance in the estimation of the energy efficiency. The comparison in the performance of the tested methods for regression, suggest that Extreme Learning Machines in combination with Partial Least Squares for regression, lead to the best performance with a Pearson correlation coefficient R ≈ 0.7 in the test data set.

Vegetation plays a crucial role in the ignition, propagation, and severity of fire, and understanding the relationship between plants and fire through flammability attributes has become a useful tool that is increasingly used in studies on fire dynamics worldwide. However, in the southern cone of South America, rather few studies have systematically and specifically addressed the flammability of vegetation, and yet fewer have compared native and non-native species. Given the increasing interest in knowing the flammability characteristics of vegetation, this review aims to assess the potential differences in flammability between native and non-native plant species that inhabit the southern cone and to identify the main methodologies and experiments used to analyze vegetation flammability. Twenty-eight species were identified, 18 native to the region and 10 non-native. Additionally, 64 experimental tests were revised to evaluate plant flammability. It was found that Cryptocarya alba, Acacia dealbata, Eucalyptus globulus, and Pinus ponderosa are the species with a high flammability index. By contrast, the species Araucaria araucana, Austrocedrus chilensis, Embothrium coccineum, and Persea lingue showed low flammability. The methodologies used to evaluate vegetation flammability were highly variable, with the use of epiradiators being the most frequent. Our review indicates that the geographic origin of vegetation (native vs. non-native in South America) is not a decisive factor in determining species-level differences in flammability. Other relevant factors that contribute with the degree of plant flammability include fuel moisture, the morphology of the species, and its internal chemical compounds. We highlight the necessity of continuing the study of plant flammability and advance in the standardization of protocols and measurements, using uniform criteria and increasing comparative studies between species, particularly in the southern cone of South America where catastrophic wildfires are increasing.

Fuel moisture content (FMC) proved to be one of the most relevant parameters for controlling fire behavior and risk, particularly at the wildland-urban interface (WUI). Data relating FMC to spectral indexes for different species are an important requirement identified by the wildfire safety community. In Valparaíso, the WUI is mainly composed of Eucalyptus Globulus and Pinus Radiata—commonly found in Mediterranean WUI areas—which represent the 97.51% of the forests plantation inventory. In this work we study the spectral signature of these species under different levels of FMC. In particular, we analyze the behavior of the spectral reflectance per each species at five dehydration stages, obtaining eighteen spectral indexes related to water content and, for Eucalyptus Globulus, the area of each leave—associated with the water content—is also computed. As the main outcome of this research, we provide a validated linear regression model associated with each spectral index and the fuel moisture content and moisture loss, per each species studied.

In this study, the tunable algorithm of cluster–cluster aggregation developed by Filippov et al. (2000) for generating fractal aggregates formed by monodisperse spherical primary particles is extended to polydisperse primary particles. This new algorithm, termed FracVAL, is developed by using an innovative aggregation strategy. The algorithm is able to preserve the prescribed fractal dimension (Df) and prefactor (kf) for each aggregate, regardless of its size, with negligible error for lognormally distributed primary particles with the geometric standard deviation σp,geo being as large as 3. In contrast, for polydisperse primary particles the direct use of Filippov et al. (2000) method, as is done by Skorupski et al. (2014), does not ensure the preservation of Df and kf for individual aggregates and it is necessary to generate a large number of aggregates to achieve the prescribed Df and kf on an ensemble basis. The performance of FracVAL is evaluated for aggregates consisting of 500 and 1000 monomers and for fractal dimension variation over the entire range of Df between 1 and 3 and kf between 0.1 and 2.7. Aggregates consisting of 500 monomers are generated on average in less than 2.4 min on a common laptop, illustrating the efficiency of the proposed algorithm.

This study focuses on discovering sustainable practices and possible innovative practices according to factors of Contingency Theory. In order to achieve this, a qualitative approach has been conducted. The study analyses 24 independent hotels. Semi-structured interviews were used in this study and conducted in person with the top managers at each hotel. The results show that, in accordance with Contingency Theory, the organizational behaviour is determined by the environment in which the hotel operates, the size of the establishment, where large- and medium-sized hotels are more committed to sustainable development, the environmental technology adopted and implemented by the hotel, and the main type of clientele, with hotels aimed at business travellers who show greater attention to the environment than those aimed at leisure travellers, mainly associated with socio-cultural values. The factors that were not completely decisive in the results analysed were the age of the hotel and the sex of the hotel owner. Considering the achieved results, this study may also contribute to identifying the most sustainable hotels and can help hotel businesses understand and reap the benefits of following a sustainable path.