Revista Científica Ingeniería y Desarrollo.

Prelims

2024-12-14T02:16:47+00:00

Prelims of Journal Ingeniería y Desarrollo, including Table of Contents.

E-Solar: A tool for solar resource assessment based on a Big data architecture in a PySpark environment

2024-03-29T03:50:35+00:00

Over time, diverse researchers have created mathematical, statistical, and predictive models to evaluate solar resources. However, their implementation in technical tools restricts their usability for non-technical users. Additionally, data processing to estimate solar radiation often necessitates powerful hardware. This study introduces a Big data based tool that employs flat files and satellite images to estimate solar radiation in Colombia. A model was developed using machine learning techniques and various programming languages. It operates within MapR, a distribution of the Hadoop ecosystem with an extensive array of Big data capabilities and utilizes the PySpark API for parallel data processing within a computer cluster. The E-Solar tool, deployed on a web server, underwent assessment by professionals within the energy sector. Usability was analyzed, compliance with recent programming standards was confirmed, and profiles of interested users were identified. The solar radiation data generated by the tool are pivotal for solar projects. Furthermore, the tool lends support to researchers and organizations in decision-making for the implementation of photovoltaic systems, as it offers pertinent information regarding the behavior of solar resources in Colombia.

Effect of permeability on the factor of safety of earthen dams under rainfall

2024-04-12T01:15:50+00:00

In this research, permeability and volumetric water content are related to evaluate their influence on the safety factor of 30-meter-high homogeneous earthen dams. For this, five analysis cases are considered, including clayey soils in the embankment, one for each case: a drainage prism and a waterproof base. Establishing three relationships between permeability and volumetric water content, based on the parameters of the soil studies and with the use of the Terzaghi and Schlichter equations, the fundamental conditions of the investigation are established. Five days of continuous rain are considered for three intensities, related to the saturated permeability of each soil. For this, a hybrid model of finite elements and limit equilibrium is used, through the GeoStudio (2018) program. The result allows establishing a relationship between the failure time, the permeability estimation method and the safety factor for the rain intensities, observing that, with the application of the Schlichter method, the safety factor decreases abruptly in the first 48 hours, while for the Terzaghi method the results show a greater similarity with those obtained from the parameters of the soil studies.

Evaluation of the quality of the electrical energy supplied by a grid-connected solar photovoltaic plant using machine learning and data mining algorithms

2024-06-09T00:29:04+00:00

The presence of photovoltaic solar plants to produce electricity implies the reduction of the use of fossil fuels, and the reduction of polluting emissions. The availability of solar energy depends on weather conditions, so the parameters of the electrical energy to be delivered could be affected. The objective of this research is to present a methodology based on data science for the evaluation of the energy quality of photovoltaic solar plants connected to the grid, considering current standards. It is applied to a 260 kWp plant of the National Institute of Standards and Technology of the United States. The parameters used are total harmonic distortion, voltage fluctuations and unbalance, electrical frequency, and power factor. Almost 100% of the records comply with the limits established by the standards for the parameters, except for power factor, with 63.56%. From the power factor classification model, it was obtained that apparent and active power, and frequency, are the most important variables. From the subgroup discovery algorithm, it was obtained that solar irradiance appears in 40% of the subgroups, and frequency in 50%.

Digital Transformation: Evolving Applications of Artificial Intelligence in the Coffee Industry

2024-06-22T01:01:40+00:00

The evolution of Artificial Intelligence (AI) in coffee is crucial for transforming this agro-industry. Colombia annually produces 12.6 million sacks and develops research on AI applied to the sector; from the detection of defects in grains to the optimization of roasting to improve coffee quality. However, there is a lack of publications that address research lines and indicators comprehensively. In this context, this research work was based on a multivariate statistical analysis of hierarchical clustering used in the bibliometric analysis methodology. This allowed inferring the current research trend in AI applied to the coffee industry. Additionally, using bibliometric techniques for information retrieval, 208 documents from the Scopus database were refined and analyzed with descriptive statistics. The results showed that Colombian researchers significantly impact the production of knowledge in AI applied to coffee, compared to Brazil, the largest coffee producer. Furthermore, research lines in market analysis through Machine Learning (ML), technologies to detect diseases and improve productivity, algorithmic methods to solve challenges in this agro-industry, and the use of remote sensing and AI for environmental and agricultural management in production were identified.

Advanced chemical and structural characterization of RAM memory waste: An approach towards recyclability and environmental safety

2024-07-07T20:36:26+00:00

This study addresses the chemical and structural characterization of powders derived from the grinding of RAM memory waste, focusing on the identification of their chemical composition (metallic and non-metallic elements). Using advanced techniques such as X-ray energy dispersive spectroscopy (EDS) and X-ray diffraction, a variety of metallic (Cu, Si, Sn) and non-metallic (fiberglass) elements and compounds essential in the manufacture of electronic circuit boards were detected. Furthermore, the concentrations of environmentally hazardous elements (Pb, As, Cd) and polychlorinated biphenyls were evaluated, finding that, for the most part, they are within the levels permitted by international regulations, suggesting that these wastes are relatively safe from an ecological perspective. However, levels of lead exceeding the established limits were observed, indicating the need for careful management. This work contributes to the knowledge about the composition of electronic waste and underscores the importance of developing effective and safe recycling processes to mitigate potential environmental risks.

Ergonomic risk factors in furniture factory workers in the state of Chihuahua, Mexico

2024-07-26T13:05:29+00:00

The application of ergonomic evaluation methods is useful when you want to identify activities that can potentially be a risk factor in the development of musculoskeletal disorders for workers. In the present study, an ergonomic evaluation was carried out in the furniture industry of the south-central region of the Mexican state of Chihuahua, with the purpose of identifying the highest risk postures and applying the rapid upper limb assessment (RULA) method to determine the level of risk in the development of Musculoskeletal Disorders; and, generate a basis on which pertinent changes in the work method and design are proposed to improve the quality of work life. The results showed that, of the sample of 31 workers evaluated, 29% identified the presence of pain in the back, 24% in the neck and 13% in the wrists, mainly. Furthermore, the results of the RULA method showed that in 89% of furniture factories, workers in the assembly area are at a high risk level of developing musculoskeletal disorders, which is why it is considered urgent to promote changes in the methods of the tasks carried out by the workers.

Frost and relevant meteorological variables forecast in agriculture in the "Sabana de Bogotá" using Machine Learning

2024-07-18T01:07:55+00:00

Taking into account historical information on climatological and frost variables, it is possible to improve decisions made in agricultural activities, seeking to determine patterns that guarantee greater yield and quality of crops and implementing forecast models based on machine learning (ML). This work presents the development of a ML model that allows determining the behavior of the meteorological variables, temperature, rainfall and relative humidity, as well as frost in the Sabana de Bogotá. The starting point was the creation of a historical database of these variables from 2010 to April 2023, considering information from ten different meteorological stations in the region. It has been necessary to implement data imputation techniques in information gaps. To determine the model with the response closest to reality, a model based on multiple linear regression and another on artificial neural networks were developed. According to the results obtained and the level of absolute error, the second model approximates its forecasts closer to the real data. The work developed can be an essential tool to generate an early warning system that helps farmers in the Sabana de Bogotá.

Edible coating based on aloe vera mucilage to preserve whole slices of pineapple fruit

2023-09-29T05:26:01+00:00

This work aimed to study the changes in the quality attributes of fresh-cut pineapple fruit due to the application of an coating based of Aloe vera. Were selected and classified 10 kg of fresh fruit, from which cuts were obtained in whole slices, that were previously sanitized and immersed in a 1.5% (w/w) of solution of calcium chloride, citric acid at 1.5% (w/w) and ascorbic acid at 1.5% (w/w) and then the edible coating was applied, which was made from Aloe vera gel to 50% (w/w), glycerol to 1.75% (w/w), polysorbate 20 to 0.01% (w/w), canola vegetable oil to 0.7% (w/w) and distilled water to 47.54%. The pineapple fruit in whole slices, with coating based of Aloe vera, packed in vacuum and stored at 5±1°C and 85±3% RH, presented suitable sensory attributes, pH values and firmness, and microbiological values up to day 15 storage.