JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH

Evaluation of the Potential of Prosopis juliflora Biomass for 2G Bioethanol Production: Chemical Characterization and Enzymatic Hydrolysis

2025-11-11T00:21:09+00:00

This study aims to characterize different parts of the plant Prosopis juliflora (Algaroba) and to evaluate the potential of its biomass for second-generation (2G) bioethanol production. Different parts of the plant (Prosopis juliflora in natura) were evaluated for cellulose, hemicellulose, lignin, moisture, and ash content, in addition to the yield of reducing sugars in enzymatic hydrolysis with cellulase from Trichoderma reesei. The results indicated that the stem bark, rich in cellulose (38.71%) and hemicellulose (21.68%), showed the highest saccharification yield (2.74 mg/mL in 24 h). In contrast, fractions with a high lignin content, such as the pod with seed (45.91%), require pretreatments to make enzymatic conversion feasible. The strategic selection of the biomass fraction and the use of appropriate methods are crucial for optimizing the recovery of fermentable sugars, enabling the use of Algaroba in biofuel production.

Evaluation of Prosopis juliflora for Bioethanol Production: Thermogravimetric Analysis and Structural Composition

2025-11-11T00:21:05+00:00

Prosopis juliflora (P. juliflora), known as Algaroba, presents great potential for bioethanol production due to its cellulose- and hemicellulose-rich composition. This study analyzed its biomass through thermogravimetric techniques and structural characterization to assess its energy viability. Samples of woody stem, stem bark, and pods were subjected to thermal and chemical composition analyses, which identified different thermal degradation profiles and structural carbohydrate contents. Results indicated that stem bark presented the highest levels of glucose and cellulose, while lignin showed significant concentration, suggesting the need for pretreatments to optimize bioenergy conversion. Thus, P. juliflora demonstrates potential as a feedstock for biofuels, provided that challenges related to lignin and economic feasibility are overcome.

Polyphenols and Antioxidant in Southwest Mexico Agave Leaves

2025-11-11T00:21:07+00:00

Currently, the revalorization of the agave leaves produced by the mezcal industry has increased since various studies confirmed the presence of secondary metabolites in this agro-waste. These phytochemicals have shown potential applications in the pharmaceutical and food industries. Therefore, this research aimed to determine and compare the phenolic content and antioxidant activity of different agave leaf species. Leaves from the Agave species angustifolia, americana, cupreata, karwinskii, and potatorum were macerated in methanol. The extracts were concentrated and characterized by their total phenolic content (TPC: Folin-Ciocalteau) and total flavonoid content (TFC: AlCl3), antioxidant activity (AA), and phenolic profile. The results evaluated by ANOVA and post hoc Tukey analysis showed that the species and geographical origin significantly influenced the total phenolic content (p<0.05). The extracts of A. americana and A. cupreata showed high phenolic and flavonoid content within a range of 7.12±1.08-3.19±0.13 and 3.01±0.10- 0.72±0.16 mg GAE/g dry leaf, respectively. Similarly, the AA was influenced by these factors (p<0.05), within ranges from 13.02±0.60-4.86±0.95; 7.84±1.30-3.52±0.97, 15.72±3.27-4.74±0.17 µmol ET/g dry leaf for the ABTS, DPPH, and FRAP assays, respectively. The extracts with the central AA were A. americana (Chiapas); A. angustifolia (Oaxaca), and A. cupreata (Guerrero). In addition, the phenolic profile of these species showed a high content of catechin and (-)-epicatechin, two flavonoids with potent antioxidant and biological activities. The leaves of mezcal agaves are a potential source of diverse secondary metabolites of industrial interest, and they can be exploited for the development of new industrial applications.

Study of Fuel Quality in the State of Maranhão Through Principal Component Analysis

2025-11-11T00:21:03+00:00

This study evaluated the quality of S10 and S500 diesel sold in the state of Maranhão, using data from the National Petroleum Agency (ANP) and the Principal Component Analysis (PCA) technique, to identify patterns in quality indicators. PCA simplified the data, highlighting the most relevant characteristics that influence diesel quality. The results revealed relationships between indicators and groups of samples with similar characteristics, which can be helpful for fuel monitoring. The research demonstrated that PCA is an effective tool to assist in the assessment and control of fuel quality, highlighting the importance of continuous monitoring and advanced statistical analysis in determining fuel quality.

The Importance of Conducting Tests to Ensure the Proper Functioning of Chemical Injection and Lift Gas Valves

2025-11-11T00:20:47+00:00

This work aims to highlight the importance of Chemical Injection Test Units and Check Valve Test Benches for offshore operations, which involve high costs. The objective is to ensure the efficient and safe operation of lift gas valves and chemical injection valves through leak-tightness, performance, and durability tests. This testing circuit follows a specific sequence to guarantee its effectiveness and safety, resulting in the delivery of a technical report presenting the obtained results.

Hydrological Rainfall-Runoff Modeling Using the WRF-Hydro Model

2025-11-11T00:20:45+00:00

This study aims to analyze the hydrological behavior of the Negro River basin, part of the greater Amazon Basin, through the simulation of rainfall and streamflow using the mesoscale Weather Research and Forecasting (WRF-Hydro) model. The premise is that streamflow simulation is a crucial tool for predicting environmental impacts, enabling timely decision-making and contributing to the sustainable management of river basins. Simulations were conducted with a spatial resolution of 1 km and a 100 m channel network, covering the period from January 1st to January 31, 2024. It was possible to evaluate rainfall and streamflow distribution, exploring the behavior of variables in the region. Furthermore, the accuracy of streamflow simulation at each river gauging station is directly influenced by the temporal distribution of rainfall simulated within the drainage network. The simulations proved satisfactory, highlighting the model's potential to support studies and environmental analyses.

Gamification in Electrical Energy Monitoring

2025-11-11T00:20:43+00:00

The project aimed to develop, in 8 months, a gamified digital platform for monitoring electrical parameters. A flexible user interface was designed to accommodate various usage requirements. The innovative factor lay in the application of gamified workflows, which facilitated the user experience and promoted greater engagement. In the current technological scenario, apps and digital platforms play a crucial role in many corporate sectors. Several service and industrial sectors have explored this trend to improve customer experience and optimize business management. In this project, the results culminated in the optimization of graphical visualizations on the platform, enabling detailed analyses at any time. The development also included gamification resources, such as a point system converted into currency (DeepCoin) and interactive screens with user-friendly navigation.

Roadmap for the Hydrogen Supply Network in Brazil

2025-11-11T00:20:42+00:00

Brazil holds significant potential to become a leader in the global energy transition due to its predominantly renewable energy matrix. Hydrogen, particularly when produced from renewable sources, emerges as a crucial energy vector to decarbonize hard-to-electrify sectors, such as heavy transport, petrochemicals and refining, fertilizer production, and the steel industry. However, establishing a hydrogen supply network in Brazil faces substantial challenges. This project aims to create a roadmap for the hydrogen refueling network in Brazil, aligning economic and governmental interests. It is a strategic initiative to ensure that the country not only follows but leads the transition to a hydrogen economy, promoting sustainable economic growth and energy security.

Reproduction and Adaptation of Literature-Derived Modeling: Numerical Simulation of Methane/Hydrogen Non-Premixed Combustion

2025-11-11T00:20:34+00:00

It is crucial to reduce CO2 (Carbon dioxide) emissions to mitigate climate impacts. A viable strategy for this reduction is to wholly or partially replace the use of natural gas with hydrogen, a carbon-free molecule, in the combustion processes of the industrial and residential sectors. In this context, the objective of this study is to investigate the combustion of methane/hydrogen binary mixtures using Computational Fluid Dynamics (CFD). Combustion will be simulated in the vertical non-premixed burner of the Senai CIMATEC Laboratory. To optimize computation time, a 2D axisymmetric model was developed. Fundamental aspects of combustion, including flow velocity, temperature, species formation, fuel consumption, and pollutant emissions, will be analyzed to evaluate the impact of partial methane substitution with hydrogen.

Development of Coating to Mitigate Chromium Contamination from Bipolar Plates in a Metal-Supported Solide Oxide Fuel (MS-SOFC)

2025-11-11T00:20:18+00:00

Metal-Supported Solid Oxide Fuel Cells (MS-SOFCs) are electrochemical devices applicable to sustainable power generation. The main reason for efficiency loss in these cells is chromium contamination, leading to degradation and reduced lifetime. In order to improve cell durability and minimize the need for frequent replacement of its components, an extensive literature review was carried out to select the most suitable materials for the fabrication of bipolar plates and their coatings. Selection criteria included cost, ease of manufacturing, thermal expansion coefficient (TEC), and electrical conductivity, ensuring compatibility with the cell stack materials. Ferritic stainless steels were selected for the plates, and oxides and spinels of specific metals for coatings. These choices will directly impact the commercial and environmental viability of this technology.

Computational Model to Optimize the Prediction of Fouling in the Deposition Process During Oil Pre-Processing in Heat Exchanger Networks Based on Machine Learning

2025-11-11T00:20:17+00:00

The accumulation of deposits in heat exchangers during oil pre-processing, known as fouling, is a reality in the oil and gas industry. This deposition, caused by the presence of suspended solids, organic, and mineral compounds, compromises the thermal and hydraulic efficiency of heat exchangers, resulting in less efficient operations and increased maintenance and energy costs. The implementation of predictive computational models aims to ensure the functioning of heat exchangers and is essential for maintaining refinery operations. The objective of this work was to analyze models for managing deposition in heat exchangers during oil pre-processing, in order to maximize operational efficiency and minimize costs associated with maintenance and energy, using Artificial Intelligence with machine learning models capable of processing sequential data, which is particularly useful in deposition processes that evolve, as in the network of heat exchangers used in oil pre-processing. The computational models were developed using historical measurement data from a network of 25 heat exchangers at a refinery in southeastern Brazil, spanning from September 1, 2014, to July 25, 2021, and comprising a total of 57,225 records stored in a CSV (Comma-Separated Values) file. For prediction, the independent variables were the operating parameters of the exchangers, and as dependent variables, the fouling factor (Rfs), which quantifies the resistance to thermal exchange due to deposition. The prediction models were evaluated based on error metrics, and the DNN (Deep Neural Network) model presented MSE (Mean Squared Error) of 0.01835, RMSE (Root Mean Squared Error) of 0.13549, MAE (Mean Absolute Error) of 0.10743, and R² (Coefficient of Determination) of 0.3049. The LSTM (Long Short-Term Memory) model presented MSE of 0.01863, RMSE of 0.13649, MAE of 0.10895, and R² of 0.29458. The Hybrid Model presented an MSE of 0.01856, an RMSE of 0.13624, an MAE of 0.10663, and an R² of 0.29720. We concluded that predicting the deposition coefficient is critical for operational planning. Computational fouling prediction models can be utilized to minimize costs and risks in the heat exchanger network during oil pre-processing, offering an efficient approach to process optimization.

Thermal Analysis Algorithm for Oil and Gas Wells

2025-11-11T00:20:15+00:00

Thermal failures are common in oil and gas well structures, since they operate under extreme temperatures. Therefore, it is essential to investigate the thermal factors that can lead to failures in order to prevent accidents. This article presents an innovative thermal analysis method, based on steady-state heat exchanges, simulated using a Microsoft Excel algorithm to calculate the heat exchange processes among well components. The results proved to be adequate, illustrating numerically and graphically two operation scenarios proposed for the well. Therefore, it serves as an alternative to meet the growing demand for studies on thermal failures in the oil and gas industry.

Evaluation of the Degradation Process of Polyamide 12 Manufactured via Multi Jet Fusion: An FTIR Analysis

2025-11-11T00:20:14+00:00

This study investigated the effects of accelerated weathering on Polyamide 12 (PA12) manufactured by Multi Jet Fusion (MJF) technology, focusing on the chemical and structural changes of the material. The samples were exposed to UV-A radiation in an accelerated weathering chamber for 360, 720, and 1080 hours. To evaluate the degradation effect, characterization was performed by Fourier Transform Infrared Spectroscopy (FTIR), which revealed minor changes in the functional bands between 1500 cm⁻¹ and 1300 cm⁻¹, indicative of photo-oxidation. Visual analysis, in turn, revealed no cracks or surface deformations. This study contributes to the understanding of the structural modifications of PA12 and its durability in extreme environments, which is essential for critical applications in the oil and gas sector, where resistance to aging is fundamental. Future research will continue to evaluate the impacts of prolonged exposures on the material's properties.

Pipeline Leak Detection Using Infrared Cameras: A Convolutional Neural Network Approach

2025-11-11T00:20:12+00:00

Pipeline leak detection is crucial for maintaining pipeline safety, particularly in complex environments. This study proposes a novel approach that integrates infrared cameras with a convolutional neural network model, specifically VGG16, utilizing infrared cameras that do not inherently measure temperature. Our results indicate that this approach is highly effective, with the model achieving 100% accuracy on both the training and validation datasets, and a near-zero validation loss in a laboratory environment. The confusion matrix confirmed that there were no misclassifications, and the Receiver Operating Characteristic (ROC) curve demonstrated an Area Under the Curve (AUC) of 1.0. These findings underscore the model's potential for real-world pipeline monitoring applications.

3D Printing of PCL/HA Composite Scaffolds for Bone Tissue Regeneration: A Brief Review

2025-11-11T00:19:56+00:00

Tissue engineering aims to develop devices that assist in cell growth and tissue formation, making the use of biocompatible, biodegradable, and non-toxic scaffolds fundamental to promoting, for example, tissue regeneration. This review aimed to investigate the production of composites of poly(ε-caprolactone) (PCL) and hydroxyapatite (HA) applied to bone regeneration using 3D printing with the fused deposition modeling (FDM) technique. It is known that composites obtained from PCL, which is biocompatible and resorbable, combined with HA, which is also biocompatible and bioactive, exhibit osteoconductive properties and cell adhesion, making them effective for manufacturing scaffolds aimed at bone regeneration. The research shows that PCL/HA composite scaffolds obtained through 3D printing have promising results, which could serve as an alternative for use in bone regeneration.

Produced Water Treatment: A Bibliometric Review of Strategies and Technologies

2025-11-11T00:19:55+00:00

Produced Water (PW) contains chemical compounds and heavy metals that make it toxic and harmful to ecosystems and human health. The inadequate disposal of this byproduct, which is the largest in the oil industry, can pollute water bodies, impact biodiversity, and harm communities that depend on water for consumption and agriculture. This work analyzed the most widely used technologies and strategies for PW treatment. The bibliometric research was based on the Web of Science platform. It mapped the main approaches in the last five years, identifying three essential groups of treatment: physical, chemical, and biological, with increasing interest in the latter. The study emphasizes the importance of enhancing PW management to mitigate environmental impacts and promote more sustainable solutions.

A Review of the Most Well-Known Aggregation Algorithms for Federated Learning Applied to Large Language Models (LLMS)

2025-11-11T00:19:52+00:00

Research on federated learning has grown due to its ability to perform local training on distributed devices, especially in the context of artificial intelligence. However, there are still a few studies focused on the aggregation algorithms used in this type of learning, and even fewer addressing their application in large language models (LLMs). This article reviews the literature on federated learning with an emphasis on aggregation techniques applied to LLM training. A scarcity of specific studies was observed, along with the predominance of three algorithms: FedAvg, FedProx, and SCAFFOLD. Each was analyzed in terms of its strengths and weaknesses, including accuracy under data heterogeneity, convergence speed, and aspects of security and privacy. It is concluded that the future of aggregation algorithms in LLM training involves developing solutions that balance these aspects in an integrated manner.

The Importance of Data and Metadata Management for Scientific Research

2025-11-11T00:19:51+00:00

The approach presented in this review seeks to demonstrate how the impact of data quality and integration can be directly influenced by metadata standardization, ensuring greater reliability and accessibility in the modeling and analysis of complex networks. This study is ongoing, and data collection was conducted in the SciELO, Google Scholar, and ScienceDirect databases, focusing on publications from 2020 to 2025. Based on the literature review, it is expected to demonstrate the relevance of data management for conducting scientific research, highlighting how a well-defined structure can ensure reliability and accessibility of information over time. At the conclusion of this ongoing research, the results may serve as a basis for developing a data and metadata management framework to support the structuring and retrieval of data necessary for scientific research in the field of complex networks.

Bibliometric Analysis on the Joint Application of Core-Shell Catalysts and Atomic Layer Deposition in Solide Oxide Fuel Cells

2025-11-11T00:19:49+00:00

This study presents a bibliometric analysis of the scientific output between 2010 and 2025 related to solid oxide fuel cells (SOFC), atomic layer deposition (ALD), and core-shell catalysts. Based on Scopus database records processed using the Bibliometrix R tool, various combinations of these themes were evaluated to map publications, authors, institutions, collaboration networks, geographic distribution, gaps, and emerging trends. The results indicate that the association between ALD and core-shell structures already constitutes a consolidated field, with the highest volume of publications. At the same time, approaches directly involving SOFCs remain in an emerging stage. Although no studies were found that integrate ALD, core-shell catalysts, and SOFC simultaneously, the qualitative indicators observed in the individual analyses suggest a significant gap and a promising, specialized research niche. The predominance of Asian and North American countries, such as China, South Korea, and the United States, was also observed, underscoring the strategic role of these nations in leading the development of advanced energy technologies. Overall, combining these approaches may drive significant advances in the development of more efficient and sustainable energy systems.