Browsing by Department "Departamento de Industrias"

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

AbstractSoot 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 ($$\approx 60\,\hbox {nm}$$≈60nm), whereas smaller particles ($$\approx 25\,\hbox {nm}$$≈25nm) 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.

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.

This article presents a model of performance analysis for a truck fleet system in an openpit mine, considering special characteristics of haul fleets. In these systems, the expected availability of each piece of equipment and its operating capacity are the fundamental variables to construct a global fleet performance function. Our analytical algorithm considers heterogeneous fleets with known individual characteristics of transport capacity and failure and repair behavior. The results converge to a new indicator denominated “Equivalent Availability” (EA), which arises from the need to evaluate the capacity of the truck fleet to operate at a lower payload than required using different combinations of equipment to achieve an availability goal. EA is a key indicator to determine the productive capacity of a process, and for selecting equipment and their combinations to achieve production objectives. To exemplify the potentialities of the EA, a case study is implemented in a Chilean copper truck fleet mining process.

Two axisymmetric laminar coflow non-smoking and smoking ethylene diffusion flames are studied numerically in order to assess the influence of different radiative property models on the soot formation and oxidation processes. Simulations are carried out by considering the Steady Laminar Flamelet (SLF) concept and a modified two-equation acetylene-based model to describe the soot nucleation, surface growth and oxidation processes. Several radiative property models are considered: the simple Optically Thin Approximation (OTA), the Weighted-Sum of Grey-Gases (WSGG), the Grey-Wide-Band model (GWB), the Statistical Narrow Band Correlated-k model (SNBCK) and the Full Spectrum Correlated-k model (FSCK). Comparisons between calculations carried out with the SNBCK model and experimental data show a reasonable agreement. Model results show that the choice of the radiative property models influence largely the soot prediction, especially in the upper part of the flame where oxidation occurs. Simulations show that the reabsorption of spectral gas and soot is an important feature and thus the commonly used OTA or grey models introduce large discrepancies. The GWB model leads to improved solutions, but it should be avoided if accurate predictions are desired. The FSCK provides equivalent results as compared to the SNBCK model with a substantial gain in CPU time.

This paper deals with the formalization of knowledge of an organization, the structuring of a suitable logic sequence and the processing to achieve the applicability of this knowledge in the practical field for the organization. All this is done through a methodological proposal that allows increasing the organizational knowledge, which is based on the information found in the organization’s computer systems as well as on the knowledge and experience of experts, generating significant synergies. The motivation to develop this paper comes from the need to align organizational goals with the knowledge of the people and data in information systems related to the field of asset management and maintenance. This methodological proposal uses a recursive process of knowledge generation, where the iteration of processes and the permanent consultation regarding compliance with the objectives, generate a cyclic process whose results are materialized in a conceptual model that contains qualitative and quantitative information, in order to increase the understanding of the system. Key Words: Conceptual Model, Transformation of Data into Information, Understanding of Complex Systems.

AbstractThe study is aimed at investigating the radiative properties of soot aggregates at determined morphological features using both experimental and numerical methods. Soot aggregates collected from air monitoring stations in different locations were examined. The locations were divided into three groups. The first group (Case 1) included the coastal and industrial zone; the second group (Case 2) consisted of small and large cities; and the third group (Case 3) included areas in the neighbourhood of thermal power plants. The absorbance measurements of the soot aggregates were conducted in the visible and near-infrared spectra, and in the wavelength range of 2 μm-20 μm. The samples were characterised by scanning electron microscopy (SEM), and their radiative properties were assessed using the discrete dipole approximation (DDA) for numerically generated fractal aggregates with two popular refractive indices of m = 1.60 + 0.60i and m = 1.90 + 0.75i. Calculations were conducted for primary particles in point-contact, with 20% overlapping and with a coating (50% and 80%) in the wavelength range of 0.4–1.064 μm. The largest measured absorbance values in both the winter and summer seasons were found in the cities in Case 1, and the x-ray diffraction (XRD) phases of the samples were also presented. The radiative properties of the aggregates, i.e., Df = 1.78 and kf = 2.0 representing Case 3, were close to those of aggregates with Df = 2.1 and kf = 2.35 representing Case 1 in the investigated wavelength range. The calculated radiative properties and the experimental absorbance measurements for point-contact and overlapping situations showed the same trend in the examined wavelengths. The absorbance properties of the samples of coastal and industrial zones were distinctively higher than others in the wavelength range of 2 μm-20 μm which could be attributed to the PAH effects.

Wastewater treatment plants (WWTPs) face two fundamental challenges: on the one hand, they must ensure an efficient application of preventive maintenance plans for their survival under competitive environments; and on the other hand, they must simultaneously comply with the requirements of reliability, maintainability, and safety of their operations, ensuring environmental care and the quality of their effluents for human consumption. In this sense, this article seeks to propose a cost-efficient alternative for the execution of preventive maintenance (PM) plans through the formulation and optimization of the opportunistic grouping strategy with time-window tolerances and non-negligible execution times. The proposed framework is applied to a PM plan for critical high-risk activities, addressing primary treatment and anaerobic sludge treatment process in a wastewater treatment plant. Results show a 26% system inefficiency reduction versus the initial maintenance plan, demonstrating the capacity of the framework to increase the availability of the assets and reduce maintenance interruptions of the WWTP under analysis.

Jointing Condition-Based Maintenance (CBM) with the Proportional Hazards Model (PHM), asset-intensive industries often monitor vital covariates to predict failure rate, the reliability function, and maintenance decisions. This analysis requires defining the transition probabilities of asset conditions evolving among states over time. When only one covariate is assessed, the model’s parameters are commonly obtained from expert opinions to provide state bands directly. However, the challenge lies within multiple covariate problems, where arbitrary judgment can be difficult and debatable, since the composite measurement does not represent any physical magnitude. In addition, selecting covariates lacks procedures to prioritize the most relevant ones. Therefore, the present work aimed to determine multiple covariate bands for the transition probability matrix via supervised classification and unsupervised clustering. We used Machine Learning (ML) to strengthen the PHM model and to complement expert knowledge. This paper allows obtaining the number of covariate bands and the optimal limits of each one when dealing with predictive maintenance decisions. This novel proposal of an ML condition assessment is a robust alternative to the expert criterion to provide accurate results, increasing the expectation of the remaining useful life for critical assets. Finally, this research has built an enriched bridge between the decision areas of predictive maintenance and Data Science.

A novel diagnostic is proposed to characterize the maturity of soot particles in a laminar axisymmetric coflow ethylene diffusion flame in terms of the spectral dependence of soot absorption function. The method relies on the combination of line-of-sight attenuation (LOSA) and emission measurements at four wavelengths (500, 532, 660 and 810 nm). The analysis of the measured signals enables the determination of soot temperature, soot volume fraction, soot maturity and the contribution of soot scattering to extinction. The analysis of extinction and emission measurements considers the spatial variation of soot optical properties. The introduction of a maturity index allows the evaluation of soot maturity based on the spectral variation of the soot absorption function. The maturity index is correlated with the organic or the mature soot content and finally in terms of the absolute value of absorption function at 810 nm. The methodology is validated using a set of synthetic spectral LOSA and emission signals representing experimental measurements based on numerical results obtained using the CoFlame code. A sensitivity analysis of the Abel inversion is also performed to properly address the effect of deconvolution procedure. Finally, the proposed method is applied to analyze the experimental data of spectrally-resolved LOSA and emission acquired in a laminar axisymmetric coflow ethylene diffusion flame established on a G¨ulder burner. The two-dimensional distributions of soot temperature, soot volume fraction, soot maturity, and the ratio of total scattering to absorption are determined. Mature soot particles are found on the top of the flame in the centerline region and also in the outer edge of the flame wing displaying strong gradients.

Knowledge of the optical properties of soot black carbon (BC) is required for the predictionof the radiative effects of freshly-emitted and aged BC particles. Here we review BC massabsorption cross section (MAC) and absorption function E(m) measurements, focusing onfreshly-emitted BC. First, we review recently reported MACs at 550 nm wavelength asobtained from direct measurements of particulate absorption and mass concentration; wefind an average of 8.0 ± 0.7 m2 /g from ten measurements, not significantly higher (p > 0.26)than the widely used MAC of 7.5 ± 1.2 m2/g recommended by Bond and Bergstrom [Bond, T.C., and R. W. Bergstrom. 2006. Light absorption by carbonaceous particles: An investigativereview. Aerosol Sci. Technol. 40(1):27–67]. Second, we review recently reported E(m), whoseretrieval is more complex due to the need to combine measurements with numerical mod-els to estimate the contribution of scattering to extinction. Third, we review recent numer-ical studies that have aimed to predict the BC MAC using various complex refractive indices(m ¼ n þ ik). Most of these studies have used m ¼ 1.95 þ 0.79i recommended by Bond andBergstrom (2006), yet failed to predict a MAC as high as 7.5 or 8.0 m2 /g at 550 nm wave-length. Fourth, we summarize a selected range of alternative values of m that has beenreported by recent studies and place them in the context of measurements using a contourplot of E(m) on the n–k plane. We show that the widely used m ¼ 1.95 þ 0.79i correspondsto an E(m) that is too low to be consistent with the measured MAC values. We concludethat the E(m) of BC in the visible and near infrared should be greater than 0.32, and thatthe commonly used BC models or the refractive index, or both, are still in need ofimprovement.