Revistas UTB ( Universidad Tecnológica de Bolívar)
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Automated System for OEE Management in the Industrial Sector
Full text linkIn today’s fast-paced manufacturing environment, the need to monitor production processes is becoming increasingly urgent. As companies strive to remain competitive in the Industry 4.0 era, they seek innovative solutions to enhance efficiency. This project addresses that need by providing a solution to capture OEE (Overall Equipment Effectiveness) measurements from machines in the drum-filling industry, specifically targeting semi-automatic equipment. The primary objective is to streamline decision-making and improve data management performance. In collaboration with Alianza Team S.A., this article outlines the detailed design and development process of a web platform called AutOEE, which integrates the Snap7 communication technology. Additionally, the article presents technical experiments, including tests conducted using a PLC provided by ELEIA to simulate real production environments. These tests verified system stability, web interface responsiveness, and accurate data extraction, with reconnection features to recover from connectivity loss. The platform also supports real-time and historical OEE data visualization, with customizable views for specific days and shifts. User feedback, gathered through a web interface test with randomized data, was overwhelmingly positive (98%), praising ease of use, relevance, and load times. However, suggestions for improvement included simplifying access to historical data, adding PDF report generation, improving security, and enhancing error reporting. These insights will guide future platform updates
Gasification of Lenga (Nothofagus pumilio) chips in a fixed bed system for rural area implementation: Magallanes case study
Full text linkThis research explores the gasification of Lenga wood chips (Nothofagus pumilio) sourced from forest remnants within a fixed-bed gasification system with a 10 kWe capacity. The primary focus is on its potential application in remote rural regions. Utilizing a factorial analysis approach, we examine the influence of particle size (ranging from 3–8 mm to 8–20 mm) and the frequency of bed agitation (occurring every 2, 4, and 6 minutes) on critical performance indicators. Throughout the experimentation, the equivalence ratio (ER) remains constant within the range of 0.17–0.20. Cold efficiency demonstrates variability, spanning from 44.8% to 58.8%. Meanwhile, the High Heating Value (HHV) varies between 6.07 and 7.18 MJ/Nm³, with gasification temperatures fluctuating between 850 and 900 °C. The introduction of bed agitation, whether at high or low frequencies, has a notable impact on gas flow, leading to substantial deviations. Larger particle sizes tend to enhance gas flow and process stability but simultaneously have adverse effects on HHV, ER, and overall process efficiency. During transient analysis, it becomes evident that gas flow requires a prolonged duration to achieve stabilization. Frequent agitation cycles (at a rate of 1/140 s⁻¹) result in fewer deviations but a slower stabilization process, whereas less frequent agitation (1/380 s⁻¹) induces greater variations but accelerates the stabilization phase. This comprehensive investigation offers valuable insights into the optimization of Lenga wood chip gasification, particularly for addressing energy needs in rural areas by harnessing forest residues
Investigating the impact of diverse PIDs and ESDs in frequency regulation of a wind-diesel hybrid system
Full text linkMicrogrids are gaining momentum these days as they can generate the cleaner and affordable electrical energy through renewable energy sources. The renewable energy sources such as wind has enough potential however, its operation is restricted as wind speed highly varies over the period of the day and that is why diesel engine generation is a possible solution to overcome the wind challenges as well as to supply the uninterrupted electrical energy to the customers. This paper presents the design of various PID controllers to match the energy generation with load demand and hence to stabilize the operation of the microgrid for various operating conditions. The performance of the PID controllers is obtained through gains calculation, diverse error values and through dynamic responses of the microgrids obtained through diverse controllers. Further, this paper also shows the impact of diverse energy storage devices (ESD) with PID controllers for the microgrid, and it is observed that PIL-PID with redox flow battery outperform other controllers and ESDs and most suited for various working conditions of the microgrid
Investigations and power quality improvement of optimally located large scale RES integrated with conventional distribution system with custom power devices
Full text linkNon-conventional energy sources are gaining popularity since they have no carbon footprint. As the world's population rises and technology advances, so does the need for power. A conventional grid is combined with renewable energy sources (RES) to supply this need. This link compromises the electrical grid's capacity to run safely and securely despite its benefits. Concerns about system power quality are the most prominent difficulty since they directly influence consumer devices and grid system performance. This article examines the power quality issues and associated worldwide standards for a conventional power network. This article presents simulated examinations of the impact of Photovoltaic (PV) and Wind Energy Conversion System (WECS) on the power quality of the Distribution System using the Modified IEEE 33 Node Radial Distribution Test System. This inquiry considers power quality concerns such as voltage fluctuations, voltage magnitude changes, and system harmonics. The Particle Swarm Optimisation (PSO) approach is used to find the ideal position for the PV system. The D-STATCOM is used to improve the system's voltage profile and harmonics. For simulational analysis, the MATLAB/Simulink software environment is employed
Design optimization and analysis of switched reluctance motor using genetic algorithm optimization technique
Full text linkThis paper presents efficiency optimization of switched reluctance motor based on genetic algorithm optimization technique. Switched reluctance motor (SRM) is considered for various applications due to its simple and robust construction. It is very essential to improve efficiency of switched reluctance motor. In this paper, optimization of 8/6 switched reluctance motor is achieved by using genetic algorithm with efficiency as its objective function. The objective of the paper is to identify the best switched reluctance motor design that provides better efficiency to satisfy the unique requirements of various applications. Using finite element analysis, a design validation of motor and characterization was made. It is analyzed that analytical results and simulation results are very close which establishes correctness of designs. The optimization result shows that the newly developed SRM design achieved better efficiency. The efficiency is increased from 82.75 % to 86.19 % with minor increase in weight. Improvement in efficiency can lead to lower energy usage, longer motor life span, and better performance
Sensorless control of switched reluctance motors through artificial neural network with a fuzzy interface
Full text linkSwitched resistance motors, are becoming increasingly used in industrial and hybrid electric vehicle (HEV) applications. But improving SRM performance is still a crucial field for study, especially with regard to their inherent benefit of accurate rotor position estimate, which is closely related to operational effectiveness. Rotor position sensing has traditionally required the incorporation of specialized sensors, which has led to difficulties like increased costs, complicated alignment, limited size, and maintenance requirements. In order to overcome these constraints, this work promotes the creation of sensorless motor control schemes that make use of cutting edge Artificial intelligence methods such as fuzzy logic and Adaptive Neuro-Fuzzy Inference System (ANFIS).To provide robust control, the suggested sensorless control systems make use of inputs such bus voltage, rotor speeds, torque instructions, and SRM parameters. The effectiveness of these methods is thoroughly assessed by painstaking simulation experiments, confirming their capacity to attain accurate control and high-performance operation. To further illuminate the benefits of sensorless control implementations, this research also does a comparison analysis between the two suggested soft computing approaches and their sensor-based equivalents. In the end, this research advances the field of SRM technology, opening the door to more dependable, economical, and efficient motor control systems in a variety of industrial and automotive applications
Modeling labor income through ICT use and sociodemographic factors: A linear regression application in Tijuana’s urban system
Full text linkIn the context of increasing digitalization and labor market transformation, income generation is influenced by multiple interrelated factors, including education, age, gender, and access to digital technologies. Understanding the impact of these variables is essential for designing equitable and efficient labor systems, particularly in border cities like Tijuana, where migration and urban growth create complex socioeconomic dynamics. This study examines how sociodemographic and technological factors affect workers' income in Tijuana’s labor ecosystem. Using a linear regression model applied to survey data from 443 individuals, the analysis finds that age, years of schooling, and computer use have a positive and statistically significant effect on income, while marital status and internet access show no significant relationship. Notably, the model reveals a persistent gender wage gap, with male workers earning significantly more than their female counterparts. These findings highlight the importance of digital competencies and education as system-level drivers of income and suggest the need for targeted public policies to foster wage equity and technological inclusion in emerging urban economies
Evaluating the influence of diethyl ether on performance and emission outputs in KIRLOSKAR TV-I engines fueled with pumpkin seed oil biodiesel
Full text linkThis investigation examines the performance and emission characteristics of the KIRLOSKAR TV-I engine utilizing pumpkin seed oil (Cucurbita pepo L) methyl ester blended with 5% diethyl ether (DEE). Various blends containing 10%, 20%, 30%, 40%, and 50% pumpkin seed oil biodiesel were analysed for their chemical and physical properties, including viscosity, density, flash point, cetane number, and oxidation stability, in compliance with ASTM standards. Gas Chromatography-Mass Spectrometry (GC-MS) was employed to determine the fatty acid composition of the biodiesel. Experimental results revealed that the 20% biodiesel blend exhibited superior performance, combustion, and emission characteristics, making it a viable substitute for conventional diesel with minimal engine modifications. Emission analysis of the 20% blend showed a 0.65% reduction in carbon monoxide (CO), a 10.3% decrease in carbon dioxide (CO2), and a 21.1% reduction in nitrogen oxide (NOx) compared to diesel. Notably, blends without additives also demonstrated significant reductions in NOx (25.83%), CO (14.3%), and CO2 (13.8%) emissions, highlighting the environmental benefits of these biodiesel formulations
Development of an accessible system to enhance driving instruction for individuals with hearing impairments
Full text linkThis development took place within the framework of Resolution 20223040030355 from the Ministry of Transportation of Colombia, which states that individuals with hearing disabilities have the right to obtain driver’s licenses. Consequently, driving schools must adapt their methodologies to facilitate the participation of these individuals in classes. With the aim of improving these processes for deaf individuals during practical driving lessons, a prototype was created. This prototype incorporates an algorithm to process voice commands and route them to a speech recognition model called VOSK, along with a graphical interface implemented in Python using the PyQt5 library. Tests were conducted during practical sessions in vehicles at a driving school in the city, and a socialization session with members of an association of individuals with hearing disabilities resulted in mostly positive outcomes. The project’s approach introduces innovative methodologies compared to those used in other countries, contributing to educational and inclusive advancement through technology
Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots
Full text linkIn the dynamic realm of Autonomous Mobile Robots (AMRs), ensuring smooth navigation among obstacles is critical, especially as they become increasingly integral to industries such as manufacturing and transportation. Recent advances have introduced several learning models to aid in obstacle avoidance, but many face computational challenges. This research introduces the Adaptive Stochastic Gradient Descent with Least Angle Regression (ASGD-LARS) algorithm, specifically designed to enhance the navigation of AMRs. By carefully considering obstacle orientations, it facilitates quicker decision-making for direction changes. When compared with well-established algorithms like KNN, XG Boost, Naive Bayes, and Logistic Regression, ASGD-LARS consistently performs better in terms of accuracy, computational efficiency, and reliability. This study lays the foundation for the deployment of smarter and more efficient AMRs across diverse industries