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In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000. [ABSTRACT FROM AUTHOR]

One of the challenges of small modular reactors (SMRs) in comparison with large reactors is the greater difficulty in achieving high burnups in smaller cores. With greater neutron leakage through the periphery, a key factor is the neutron economy of the fuel cladding. However, all large supercritical water-cooled reactor (SCWR) concepts have employed neutron-absorbing stainless steels and nickel-based alloys in order to meet all the requirements in terms of corrosion and thermalhydraulics. In order to achieve higher burnups and extend the time between refueling in a SCW-SMR, the use of chromium-coated zirconium alloy as a potential fuel cladding candidate has been explored. Chromium coatings up to a few micrometers thick have shown improved oxidation resistance of zirconium-based claddings under operating conditions relevant to SCWR concepts. In this study, Zr-2.5Nb alloy (UNS R60904) from pressure tube samples was coated using a physical vapor-deposition (PVD) method. Oxidation tests were performed on coated samples at 500 °C and approximately 25 MPa in a refreshed autoclave. The effects of the oxide on heat transfer and hydraulic resistance are also discussed in this study. Last, but not least, this study evaluates the coating cost of the fuel cladding with chromium in a vacuum plasma spray process. [ABSTRACT FROM AUTHOR]

Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice's value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample's elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92–100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors. [ABSTRACT FROM AUTHOR]

The analysis of the behavior of lithium-ion batteries has gained considerable interest in recent years. There are different alternatives for the analysis of this behavior; however, depending on the type of modeling, there are application and optimization restrictions. In this work, a hybrid model has been made that is capable of predicting the characteristics of a lithium-ion battery. As a novelty, the simplification, at the same time, facilitates the sampling of parameters for their prompt selection for optimization. A new model open to the user is proposed, which has proven to be efficient in simulation time. For example, one hour simulates it in 5 min, providing information detailing how these parameters, State of Health (SOH), Open Circuit Voltage (VOC), State of charge (SOC), and Number of charge and discharge cycles, in the face of temperature variations and charge and discharge cycles. Opening the possibility of optimizing the parameters with different techniques to estimate the performance and dynamics in the face of temperature change and charge and discharge cycles. A model based on linear regressions, manufacturer characteristics, and integrating equations in the electrical model of electrochemical phenomena is proposed. [ABSTRACT FROM AUTHOR]

The problems related to the increase in the generation of discarded tires demonstrate the need for profitable, efficient, cost-effective, and sustainable processes for their waste management. In particular, the valorization of pyrolytic solids for energy storage applications is of interest. In this study, four processes were performed: (1) pyrolysis; (2) chemical activation and pyrolysis; (3) pyrolysis and physical activation; and (4) chemical activation, pyrolysis, and physical activation. The process consisting of chemical activation, pyrolysis, and physical activation yielded 52% solid material with the highest electrical conductivity (2.43 Ω–1 cm–1) and a surface area of 339 m2/g with an average pore size of 3.6 nm. In addition, it was found that pore size had a greater effect on the conductivity than surface area. Liquid and gas fraction compositions were modified by the presence of chemical activation: aromatization reactions were favored, and limonene was not observed in the liquid fraction, while an increase on the CH4 concentration caused an increment in the heating value of the gas fraction. It was demonstrated that chemical and physical activation enhance the properties of the pyrolytic solid product derived from waste tires that make it suitable for the partial substitution of materials for electric energy storage applications. [ABSTRACT FROM AUTHOR]

Tropospheric levels of O3 have historically exceeded the official annual Mexican standards within the Monterrey Metropolitan Area (MMA) in NE Mexico. High-frequency and high-precision measurements of tropospheric O3, NOy, NO2, NO, CO, SO2, PM10 and PM2.5 were made at the Obispado monitoring site near the downtown MMA from September 2012 to August 2013. The seasonal cycles of O3 and NOy are driven by changes in meteorology and to a lesser extent by variations in primary emissions. The NOy levels were positively correlated with O3 precursors and inversely correlated with O3 and wind speed. Recorded data were used to assess the O3-Volatile Organic Compounds (VOC)-NOx system's sensitivity through an observational-based approach. The photochemical indicator O3/NOy was derived from measured data during the enhanced O3 production period (12:00-18:00 Central Daylight Time (CDT), GMT-0500). The O3/NOy ratios calculated for this time period showed that the O3 production within the MMA is VOC sensitive. A box model simulation of production rates of HNO3 (PHNO3) and total peroxides (Pperox) carried out for O3 episodes in fall and spring confirmed the VOC sensitivity within the MMA environment. No significant differences were observed in O3/NOy from weekdays to weekends or for PHNO3/Pperox ratios, confirming the limiting role of VOCs in O3 production within the MMA. The ratified photochemical regime observed may allow the environmental authorities to revise and verify the current policies for air quality control within the MMA. [ABSTRACT FROM AUTHOR]

In this work, carbon dots were created from activated and non-activated pyrolytic carbon black obtained from waste tires, which were then chemically oxidized with HNO3. The effects caused to the carbon dot properties were analyzed in detail through characterization techniques such as ion chromatography; UV–visible, Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy; ζ potential; transmission electron microscopy (TEM); and spectrofluorometry. The presence of functional groups on the surface of all carbon dots was revealed by UV–visible, FTIR, XPS, and Raman spectra. The higher oxidation degrees of carbon dots from activated precursors compared to those from nonactivated precursors resulted in differences in photoluminescence (PL) properties such as bathochromic shift, lower intensity, and excitation-dependent behavior. The results demonstrate that the use of an activating agent in the recovery of pyrolytic carbon black resulted in carbon dots with different PL properties. In addition, a dialysis methodology is proposed to overcome purification obstacles, finding that 360 h were required to obtain pure carbon dots synthesized by a chemical oxidation method. [ABSTRACT FROM AUTHOR]

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m−3. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m−3. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM2.5 distributions in an urban area with low air quality monitor density. [ABSTRACT FROM AUTHOR]

Source attribution of airborne particulate matter (PM) relies on a host of different chemical species. Organic molecular markers are a set of particularly useful marker compounds for estimating source contributions to the fine PM fraction (i.e., PM2.5). Although there are many source apportionment studies based on organic markers, these studies heavily rely on the few studies that report region-specific emission profiles. Source attribution efforts, particularly those conducted in countries with emerging economies, benefit from ad hoc information to conduct the corresponding analyses. In this study, we report organic molecular marker source profiles for PM2.5 emitted from 12 major sources types from five general source categories (meat cooking operations, vehicle exhausts, industries, biomass and trash burning, and urban background) for the Monterrey Metropolitan Area (Mexico). Source emission samples were obtained from a ground-based source-dominated sampling approach. Filter-based instruments were utilized, and the loaded filters were chemically characterized for organic markers by GC-MS. Levoglucosan and cholesterol dominate charbroiled-cooking operation sources while methoxyphenols, PAHs and hopanes dominate open-waste burning, vehicle exhaust and industrial emissions, respectively. A statistical analysis showed values of the Pearson distance < 0.4 and the similarity identity distance > 0.8 in all cases, indicating dissimilar source profiles. This was supported by the coefficient of divergence average values that ranged from 0.62 to 0.72. These profiles could further be utilized in receptor models to conduct source apportionment in regions with similar characteristics and can also be used to develop air pollution abatement strategies. [ABSTRACT FROM AUTHOR]

The variability of renewable energies and their integration into the grid via power electronics demands the design of robust control algorithms. This work incorporates two techniques to ensure the stability of a boost converter through its state equations, implementing the inverse optimal control and the gain-scheduling technique for robust control settings. In such a way that, under a single adjustment, it is capable of damping different changes such as changes in the parameters, changes in the load, the input voltage, and the reference voltage. On the other hand, inverse optimal control is based on a discrete-time control Lyapunov function (CLF), and CLF candidate depends on fixed parameters that are selected to obtain the solution for inverse optimal control. Once these parameters have been found through heuristic or artificial intelligence methods, the new proposed methodology is capable of obtaining a robust optimal control scheme, without having to search for new parameters through other methods, since these are sometimes sensitive changes and many times the process of a new search is delayed. The results of the approach are simulated using Matlab, obtaining good performance of the proposed control under different operation conditions. Such simulations yielded errors of less than 1% based on the voltage reference, given the disturbances caused by changes in the input variables, system parameters, and changes in the reference. Thus, applying the new methodology, the stability of our system was preserved in all cases. [ABSTRACT FROM AUTHOR]

In this work, the particulate matter (PM) from three different monitoring stations in the Monterrey Metropolitan Area in Mexico were investigated for their compositional, morphological, and optical properties. The main aim of the research was to decipher the different sources of the particles. The methodology involved the ex situ sequential analysis of individual particles by three analytical techniques: scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), polarized light microscopy (PLM), and micro-Raman spectroscopy (MRS). The microanalysis was performed on samples of total suspended particles. Different morphologies were observed for particles rich in the same element, including prismatic, spherical, spheroidal, and irregular morphologies. The sequential microanalysis by SEM-EDS/PLM/MRS revealed that Fe-rich particles with spherical and irregular morphologies were derived from anthopogenic sources, such as emissions from the metallurgical industry and the wear of automobile parts, respectively. In contrast, Fe-rich particles with prismatic morphologies were associated with natural sources. In relation to carbon (C), the methodology was able to distinguish between the C-rich particles that came from different anthopogenic sources—such as the burning of fossil fuels, biomass, or charcoal—and the metallurgical industry. The optical properties of the Si-rich particles depended, to a greater extent, on their chemical composition than on their morphology, which made it possible to quickly and accurately differentiate aluminosilicates from quartz. The methodology demonstrated in this study was useful for performing the speciation of the particles rich in different elements. This differentiation helped to assign their possible emission sources. [ABSTRACT FROM AUTHOR]

Statistical time series forecasting is a useful tool for predicting air pollutant concentrations in urban areas, especially in emerging economies, where the capacity to implement comprehensive air quality models is limited. In this study, a general multiple regression with seasonal autoregressive moving average errors model was estimated and implemented to forecast maximum ozone concentrations with a short time resolution: overnight, morning, afternoon and evening. In contrast to a number of short-term air quality time series forecasting applications, the model was designed to explicitly include the effects of meteorological variables on the ozone level as exogenous variables. As the application location, the model was constructed with data from five monitoring stations in the Monterrey Metropolitan Area of Mexico. The results show that, together with structural stochastic components, meteorological parameters have a significant contribution for obtaining reliable forecasts. The resulting model is an interpretable, useful and easily implementable model for forecasting ozone maxima. Moreover, it proved to be consistent with the general dynamics of ozone formation and provides a suitable platform for forecasting, showing similar or better performance compared to models in other existing studies. [ABSTRACT FROM AUTHOR]

Pyrolysis is a feasible solution for environmental problems related to the inadequate disposal of waste tires, as it leads to the recovery of pyrolytic products such as carbon black, liquid fuels and gases. The characteristics of pyrolytic carbon black can be enhanced through chemical activation in order to produce the required properties for its application. In the search to make the waste tire pyrolysis process profitable, new applications of the pyrolytic solid products have been explored, such as for the fabrication of energy-storage devices and precursor in the synthesis of nanomaterials. In this study, waste tires powder was chemically activated using acid (H2SO4) and/or alkali (KOH) to recover pyrolytic carbon black with different characteristics. H2SO4 removed surface impurities more thoroughly, improving the carbon black's surface area, while KOH increased its oxygen content, which improved the carbon black's stability in water suspension. Pyrolytic carbon black was fully characterized by elemental analysis, inductively coupled plasma–optical emission spectrometry (ICP-OES), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), N2 adsorption/desorption, scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM-EDS), dynamic light scattering (DLS), and ζ potential measurement. In addition, the pyrolytic carbon black was used to explore its feasibility as a precursor for the synthesis of carbon dots; synthesized carbon dots were analyzed preliminarily by SEM and with a fluorescence microplate reader, revealing differences in their morphology and fluorescence intensity. The results presented in this study demonstrate the effect of the activating agent on pyrolytic carbon black from waste tires and provide evidence of the feasibility of using waste tires for the synthesis of nanomaterials such as carbon dots. [ABSTRACT FROM AUTHOR]

Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM2.5), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM2.5 ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM2.5 ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM2.5, sea salt PM2.5, black carbon (BC), organic carbon (OC), and sulfate (SO42−) reanalysis data were identified as factors that significantly influenced PM2.5 concentrations. The ENN estimated a PM2.5 monthly mean of 25.62 μg m−3 during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient R ~ 0.90; root mean square error = 1.81 μg m−3; mean absolute error = 1.31 μg m−3. Overall, the PM2.5 levels were higher in winter and spring. The highest PM2.5 levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM2.5 was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps. [ABSTRACT FROM AUTHOR]

Here, we present an assessment of long-term trends in the O3 weekend effect (WE) occurrences and spread within the Mexico City (MCMA), Guadalajara (GMA), and Monterrey (MMA) metropolitan areas, which are the three largest metropolitan areas (MAs) of Mexico and concentrate around 33% of the total population in the country. Daytime averages and peak differences in O3 concentrations from weekdays to weekends were used as a proxy of WE occurrence. All MAs exhibited the occurrence of WE in all years at least in one monitoring site. Substantial differences in O3 daytime averages and peaks from weekdays to weekends have decreased over time in all MAs, and since 1998 and 2013 for the MCMA and GMA, respectively, higher O3 levels during weekends are typical during most of the year. The largest variations in the O3 WE were observed at downwind and urban core sites of the MCMA and GMA. Significant increasing trends (p < 0.05) in the O3 WE magnitude were observed for Sundays at all sites within the MCMA, with trends in annual averages ranging between 0.33 and 1.29 ppb O3 yr−1. Within the GMA, for Sundays, fewer sites exhibited increasing trends in the WE occurrence and at lower growth rates (0.32 and 0.48 ppb yr−1, p < 0.1) than within the MCMA, while within the MMA no apparent trends were observed in marked contrast with the MCMA and GMA. Our findings suggest that policies implemented have been successful in controlling weekday ground-level O3 within the MCMA and GMA, but further actions must be introduced to control the increases in the O3 WE magnitude and spread. [ABSTRACT FROM AUTHOR]