Evaluación de condiciones de operación y protocolo de medición de hermeticidad en el fugómetro TEX G4-VF

En la ingeniera es común encontrar problemas de fugas no detectadas que ponen en peligro los equipos y la vida de las personas que manipulan estas herramientas. Constantemente se presentan accidentes que llevan a situaciones de pérdidas económicas, amenazas en la seguridad y atentados contra el medio ambiente. Para darle solución a estos problemas es necesario realizar pruebas de hermeticidad, garantizando el óptimo funcionamiento de los equipos usados por las empresas para que estas, a su vez, puedan generar productos de calidad, 100% seguros y herméticos para las personas que vayan a hacer uso de estos. El laboratorio de Ciencias Térmicas del ITM con el objetivo de avanzar en el tema y reducir los niveles de accidentes en la industria, recientemente adquirió un fugómetro para determinar fugas en gasodomésticos y tuberías. Este equipo fue analizado como parte de un producto de laboratorio mediante la verificación manual con líquido jabonoso y ensayos en diferentes procesos al interior del laboratorio. Este estudio dio como resultado una guía en los protocolos de medición del equipo y un manual práctico para su uso. Durante el estudio se realizaron análisis en la detección de fugas en gasodomésticos, líneas y ductos de gas, esto con el objetivo de evitar posibles emergencias, así como caídas de presión o pérdida de gases que repercuten en el incremento del costo y la seguridad de las personas. Dependiendo el nivel de pureza de los gases su costo puede aumentar, por esto es importante detectar las fugas a tiempo para evitar pérdidas económicas en las industrias. Existen varias metodologías para la detección y corrección de fugas: monitoreo de la presión en diferentes puntos de las tuberías, verificación de fugas en las conexiones mediante dispositivos y utilización de líquidos especiales. En el de gasodomésticos las opciones más usadas son la verificación manual mediante un líquido jabonoso o el uso del fugómetro. Resultados: con el análisis y aplicación del fugómetro. En el laboratorio se logró desarrollar una guía para el manejo de esta herramienta, evitando así las pérdidas de gas para disminuir costos en la industria.Ingeniero de Telecomunicacionespregrad

An Energy Management System for PV Sources in Standalone and Connected DC Networks Considering Economic, Technical, and Environmental Indices

This research proposes an efficient energy management system for standalone and grid-connected direct current (DC) distribution networks that consider photovoltaic (PV) generation sources. A complete nonlinear programming model is formulated to represent the efficient PV dispatch problem while taking three different objective functions into account. The first objective function corresponds to the minimization of the operational costs with respect to the energy purchasing costs at terminals of the substation, including the maintenance costs of the PV sources. The second objective function is the reduction of the expected daily energy losses regarding all resistive effects of the distribution lines. The third objective function concerns the minimization of the total emissions of CO (Formula presented.) into the atmosphere by the substation bus or its equivalent (diesel generator). These objective functions are minimized using a single-objective optimization approach through the application of the Salp Swarm Algorithm (SSA), which is combined with a matrix hourly power flow formulation that works by using a leader–follower operation scheme. Two test feeders composed of 27 and 33 nodes set for standalone and grid-connected operation are used in the numerical validations. The standalone grid corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá, and the grid-connected system is adapted to the operating conditions in the metropolitan area of Medellín, i.e., a rural area and a major city in Colombia. A numerical comparison with three additional combinatorial optimizers (i.e., particle swarm optimization (PSO), the multiverse optimizer (MVO), and the crow search algorithm (CSA)) demonstrates the effectiveness and robustness of the proposed leader–follower optimization approach to the optimal management of PV generation sources in DC grids while considering different objective function indices. © 2022 by the authors

Application of the arithmetic optimization algorithm to solve the optimal power flow problem in direct current networks

This article presents a methodology to solve to the Optimal Power Flow (OPF) problem in Direct Current (DC) networks using the Arithmetic Optimization Algorithm (AOA) and Successive Approximation (SA). This master-slave methodology solves the OPF problem in two stages: the master stage estimates the solution to the OPF problem considering its constraints and variables, and the slave stage assesses the fitness of the solution proposed by the master stage. To validate the methodology suggested in this article, three test systems cited multiple times in the literature were used: the 10, 21 and the 69 nodes test systems. In addition, three scenarios varying the allowable power limits for the Distributed Generators (DGs) are presented; thus, the methodology explores solutions under different conditions. To prove its efficiency and robustness, the solution was compared with four other methods reported in the literature: Ant Lion Optimization (ALO), Black Hole Optimization (BHO), the Continuous Genetic Algorithm (CGA), and Particle Swarm Optimization (PSO). The results show that the methodology proposed here to reduce power losses presents the best solution in terms of standard deviation. © 2022 The Author

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks

In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master–slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time

Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method

This paper addresses the Optimal Power Flow (OPF) problem in Direct Current (DC) networks by considering the integration of Distributed Generators (DGs). In order to model said problem, this study employs a mathematical formulation that has, as the objective function, the reduction in power losses associated with energy transport and that considers the set of constraints that compose DC networks in an environment of distributed generation. To solve this mathematical formulation, a master–slave methodology that combines the Salp Swarm Algorithm (SSA) and the Successive Approximations (SA) method was used here. The effectiveness, repeatability, and robustness of the proposed solution methodology was validated using two test systems (the 21- and 69-node systems), five other optimization methods reported in the specialized literature, and three different penetration levels of distributed generation: 20%, 40%, and 60% of the power provided by the slack node in the test systems in an environment with no DGs (base case). All simulations were executed 100 times for each solution methodology in the different test scenarios. The purpose of this was to evaluate the repeatability of the solutions provided by each technique by analyzing their minimum and average power losses and required processing times. The results show that the proposed solution methodology achieved the best trade-off between (minimum and average) power loss reduction and processing time for networks of any size

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks

In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master&ndash;slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master&ndash;slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time

An Effective Power Dispatch of Photovoltaic Generators in DC Networks via the Antlion Optimizer

This paper studies the problem regarding the optimal power dispatch of photovoltaic (PV) distributed generators (DGs) in Direct Current (DC) grid-connected and standalone networks. The mathematical model employed considers the reduction of operating costs, energy losses, and CO2 emissions as objective functions, and it integrates all technical and operating constraints implied by DC grids in a scenario of variable PV generation and power demand. As a solution methodology, a master&ndash;slave strategy was proposed, whose master stage employs Antlion Optimizer (ALO) for identifying the values of power to be dispatched by each PV-DG installed in the grid, whereas the slave stage uses a matrix hourly power flow method based on successive approximations to evaluate the objective functions and constraints associated with each solution proposed within the iterative process of the ALO. Two test scenarios were considered: a grid-connected network that considers the operating characteristics of the city of Medell&iacute;n, Antioquia, and a standalone network that uses data from the municipality of Capurgan&aacute;, Choc&oacute;, both of them located in Colombia. As comparison methods, five continuous optimization methods were used which were proposed in the specialized literature to solve optimal power flow problems in DC grids: the crow search algorithm, the particle swarm optimization algorithm, the multiverse optimization algorithm, the salp swarm algorithm, and the vortex search algorithm. The effectiveness of the proposed method was evaluated in terms of the solution, its repeatability, and its processing times, and it obtained the best results with respect to the comparison methods for both grid types. The simulation results obtained for both test systems evidenced that the proposed methodology obtained the best results with regard to the solution, with short processing times for all of the objective functions analyzed

Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method

In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses (Ploss) associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject 20%, 40%, and 60% of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of Ploss in both radial and mesh systems. It provided the best reduction in minimum Ploss in short processing times and showed excellent repeatability in each test system and scenario under analysis