July Andrea Gómez-Camperos

Summary: Gas-liquid flow is one of the most observed in nature and in many industrial applications. The presence of dense gas is a special case (i.e., a flow with a liquid/gas viscosity ratio of 10 or even less). Dense-gas/liquid flow is found in the Brazilian offshore production scenario, which is characterized by subsea lines operating under reservoir pressures typically ranging from 100 bar to 500 bar, temperatures around 60°C, and high CO2 content, while upstream of the choke valve pressures commonly range between 30 bar and 160 bar. Despite its relevance, the technical literature shows significant gaps. Although some studies employ dynamic pressure signals to analyze two-phase flows, most rely on numerical simulations or high-frequency experimental systems. Consequently, the analysis and classification of dense-gas/liquid flow using low-frequency differential pressure signals that are precisely the most widely available measurements in offshore operations have not been addressed. Likewise, there are no approaches that integrate multiple signal-to-image encoding techniques with advanced hybrid deep learning architectures for flow-pattern characterization from pressure-gradient signals. To address these gaps, we introduce a methodology that transforms low-frequency pressure-gradient time series into multivariate images using four complementary encoding techniques. Gramian angular fields (GAFs) [Gramian angular summation field (GASF)/Gramian angular difference field (GADF)] capture temporal correlations and angular relationships; recurrence plots (RPs) reveal recurrence patterns and nonlinear dynamic structures; and Markov transition fields (MTF) model state-transition probabilities while preserving the statistical structure of the signal. These representations are integrated into a unified 2 × 2 mosaic, generating a final image of size 2m × 2n. In addition, a hybrid deep learning model convolutional neural network (CNN)-transformer with coordinated attention is proposed, designed to leverage both local features and global dependencies. The convolutional modules extract fine spatial patterns, the Swin Transformer blocks learn long-range relationships through hierarchical self-attention, and the coordinated attention module preserves directional information by recalibrating feature responses along the horizontal and vertical axes. This combination enables the model to capture rapid local variations in the pressure gradient as well as global hydrodynamic trends, both essential in complex multiphase systems. By relying exclusively on low-frequency pressure-gradient signals, which are typically available in offshore monitoring systems, the proposed method offers strong industrial applicability. The results show that the hybrid model achieves 99% accuracy in classifying dense-gas/liquid flow patterns, significantly outperforming traditional approaches based on physical correlations or statistical analysis. These advancements position the methodology as a significant contribution to multiphase-flow characterization in the oil and gas industry.

The knowledge of the stress-strain curve is of great interest due to the different parameters, conditions, and applications to which materials are exposed in different industrial sectors. This statistical study aims to analyze the strain-hardening behavior of two AISI-grade materials (1045 and 304). The ASTM E-8 standard procedure guidelines were considered for different hardening percentages due to plastic deformation (20% and 40%), following an experimental design of 45 tests, including repetitions. The results revealed an improvement in the material's mechanical properties corresponding to the hardening percentage, which was also correlated with the heat treatment process. Finally, response surface methodology plots were generated, depicting predictive equations governing the behavior of the response variables (stress, yield, load, and strain) with a confidence level of 98% in the results.

The environmental impact of traditional construction materials necessitates the development of sustainable alternatives. This study evaluates eco-cobbles as novel building materials designed to reduce environmental footprint while maintaining performance standards. The objectives were to develop an eco-friendly cobble alternative and assess its effectiveness through laboratory tests. Eco-cobbles were synthesized using recycled and bio-based materials and tested for compressive strength, flexural strength, and water absorption at 14 and 28 days. The compressive strength ranged from 11.5 MPa to 26.8 MPa, with a maximum value observed at 28 days in a mixture containing 95% concrete and 5% polyethylene terephthalate (PET). Flexural strength varied from 9.1 MPa to 28.7 MPa, with the highest value achieved in a mixture of 95% concrete and 0% fibers. Water absorption rates ranged from 2.1% to 6.6%, demonstrating an effective balance between performance and durability. Environmental assessments indicated a 30% reduction in resource consumption and a 40% decrease in carbon footprint compared to traditional cobble production methods. The findings demonstrate that eco-cobbles not only meet performance standards but also offer significant environmental benefits with a 99% compliance from the results obtained by response surface methodology plots, confirming that eco-cobbles offer a viable, sustainable alternative to conventional materials, with the potential for broader application in eco-friendly construction practices.

The study focuses on developing a prefabricated panel for non-structural purposes by optimizing mortar mix designs incorporating recycled microplastic (RMP) and construction demolition waste (CDW) at various ratios (0, 10, 20, 30, and 100%). Experimental procedures encompassed material characterization, mortar specimen manufacturing, compression resistance testing, and thermal/acoustic panel tests following Colombian technical standards. Results indicate that incorporating 20% CDW enhances material strength, with cylinder number 3 (20% of CDW) achieving a resistance of 31.45 MPa. Panels incorporating recyclable waste materials show improved acoustic and thermal insulation properties, with up to 39 dB reduction in sound transmission and a 21 °C decrease in thermal transmission observed (5.6% and 35% for panel and door, respectively). This research advances sustainable construction practices demonstrating the potential of prefabricated panels using recyclable materials, offering eco-friendly solutions with enhanced performance characteristics for construction applications.

Accurately and instantly estimating the hydrodynamic characteristics in two-phase liquid–gas flow is crucial for industries like oil, gas, and other multiphase flow sectors to reduce costs and emissions, boost efficiency, and enhance operational safety. This type of flow involves constant slippage between gas and liquid phases caused by a deformable interface, resulting in changes in gas volumetric fraction and the creation of structures known as flow patterns. Empirical and numerical methods used for prediction often result in significant inaccuracies during scale-up processes. Different methodologies based on artificial intelligence (AI) are currently being applied to predict hydrodynamic characteristics in two-phase liquid–gas flow, which was corroborated with the bibliometric analysis where AI techniques were found to have been applied in flow pattern recognition, volumetric fraction determination for each fluid, and pressure gradient estimation. The results revealed that a total of 178 keywords in 70 articles, 29 of which reached the threshold (machine learning, flow pattern, two-phase flow, artificial intelligence, and neural networks as the high predominance), were published mainly in Flow Measurement and Instrumentation. This journal has the highest number of published articles related to the studied topic, with nine articles. The most relevant author is Efteknari-Zadeh, E, from the Institute of Optics and Quantum Electronics.

In this work, an application of artificial intelligence in the oils & gas industry is developed to identify flow patterns in horizontal and vertical pipes of two-phase flow of oil and water, normalizing the word information and converting it to numerical values through the development of an artificial neural network, whose input layer is composed of the surface velocities of each fluid, the velocity of the mixture, the volumetric fraction of the substances, diameter and the inclination of pipelines and the oil viscosity. The Artificial Neural Networks (ANN) has two hidden layers composed of 45 neurons. The database with which the model was trained, validated, and tested has 6993 rows of information corresponding to the inputs of the intelligent system and particular-ized for annular flow in horizontal pipes and DO/W in vertical pipelines. Notice that the information was obtained after re-engineering the information presented by 12 and 18 authors for horizontal and vertical piping, respectively. Finally, the mean square error obtained by the model was around 1.38%, with a maximum coefficient of determination of 0.79.

Solar energy is the most abundant renewable resource on the surface of the earth; where through photovoltaic systems electricity is generated using solar cells that work from the incidence of light. The study's objective is to develop technical and economic analysis for installing a photovoltaic solar system in the municipality of El Zulia, Norte de Santander department, Colombia. For this purpose, different studies on this region's solar radiation are taken as a reference, recorded for nine years, and processed using statistical techniques that suggest photovoltaic energy exploitation. Likewise, the electricity consumption averages were obtained and related to the energy rates currently paid by users in this northern region of Santander. Based on this information, the VENSIM model was analyzed to understand the electric microgeneration process in the Colombian residential sector, and the calculations related to the selection of the inverter, the photovoltaic modules, the photovoltaic generator, and other technical specifications were carried out. Finally, the financial evaluation of the photovoltaic solar system was carried out for 25 years, considering the returns on investment from the tax benefits offered by Law 1715 of 2014 and the savings that microgeneration represents for users. Also, the project proved to have financial viability and a payback period of 4 years.

Objetivo: El objetivo principal de este trabajo fue determinar las propiedades físico-mecánicas del Bloque de Arena Comprimida (BTC) impermeabilizado de 15×10×30 cm con dos orificios de 7 cm de diámetro, con reciclaje de asfalto triturado. Metodología: El diseño de mezclas de la materia prima se llevó acabo con adición de 3 diferentes porcentajes del reciclado de asfalto triturado (3, 6 y 9%), teniendo en cuenta los parámetros de la norma NTC-5324, para la caracterización de las propiedades físico-mecánicas. De esta manera, los bloques fueron sometidos a ensayos de absorción, capacidad de carga, y de durabilidad y erosión. Resultados y conclusiones: Los resultados demostraron que los bloques de arena comprimida son alternativas sostenibles, con propiedades mejoradas mediante la adición de materiales reciclados (asfalto triturado), disminuyendo de esta manera la vulnerabilidad de los bloques ante los cambios de humedad en regiones donde se requiere que los bloques de mampostería para la construcción sean adecuados para este tipo de ambientes.

En el siguiente documento se presenta un sistema para el mejoramiento del proceso de obtención de energía mediante un biodigestor, se propone que el sistema realice la adquisición de datos de forma automática, permitiendo la visualización de las variables de temperatura, presión y potencial de hidrógeno. La automatización del proceso permite la toma de decisiones en el estudio de la fase de explotación y extracción de gases por medio de la adquisición de datos, sacando el máximo provecho de los residuos biosólidos producto de las actividades agropecuarias. Esta investigación se centra en la elaboración de un sistema desupervisión el cual permite analizar mediante un instrumento virtual las variables de temperatura, presión y pH involucradas en la adquisición de datos del sistema del Biodigestor, con el fin de nivelar la carga orgánica generada por los componentes de origen agropecuario, la cual generalmente se vierte de forma directa a los causes de agua y en campos a cielo abierto. El instrumento virtual permite la supervisión y el modelamiento de las variables medidas, el estudio de estas puede llevar al mejoramiento de los procesos de biodigestión de biomasa para la generación de gas metano.