Increase In Processing Time Research Articles

ВЛИЯНИЕ ПАРАМЕТРОВ ТЕРМИЧЕСКОЙ ОБРАБОТКИ НА КАЧЕСТВО ПОЛИМЕРНО-ПОРОШКОВОГО ПОКРЫТИЯ ДРЕВЕСНЫХ МАТЕРИАЛОВ

This paper presents the results of a study of the qualitative parameters of the coating of the polymer-powder composition of coniferous wood, depending on the modes of heat treatment during its production. The quality of the polymer coating was assessed by abrasion, lattice incision and pencil hardness methods. To obtain coatings, an epoxy-polyester composition applied to wood by spraying followed by curing during heat treatment was used. The temperature of the heat treatment varied in the range of 80-120 ° C with an exposure time of 5-20 minutes. The intervals of thermal effects were chosen based on the analysis of domestic and international research experience in this area. The influence of the curing time and temperature of the polymer-powder coating on the following parameters of the material surface is shown: wear resistance, water absorption, adhesion, hardness. It was found that the wood coating based on an epoxy-polyester powder composition, treated at a temperature of 90 ° C for 15 minutes, has the highest quality technological and operational parameters. An increase in temperature and processing time has a negative effect on the quality of the coating due to the release of resins from the wood. This is especially noticeable at a curing temperature of 120 °C, when the process of resin extraction from coniferous wood increases. The results obtained make it possible to optimize the process of processing wood using polymer-powder coatings to achieve maximum protection and decorative characteristics with simultaneous drying or thermal exposure. Based on a comparative analysis with the literature data, it is shown that the technological modes of processing polymer powder paint significantly depend on the type of wood.

YOLO_MRC: A fast and lightweight model for real-time detection and individual counting of Tephritidae pests

Tephritidae pests severely affect the quality and safety of various melons, fruits and vegetable crops. However, many agricultural managers lack an adequate understanding of the level of pest occurrence, resulting in the misuse of pesticides, which ultimately leads to environmental pollution and economic loss. Therefore, real-time detection and counting of Tephritidae pests are important for timely pest spotting and control. This work helps quickly determine the distribution and abundance of pests in the current environment, thus providing data on pest conditions for agricultural management to optimize pesticide use. Nevertheless, the fast speed, high accuracy, and lightweight performance of real-time detection and counting are difficult to balance. To address this problem, based on the YOLOv8n model, this paper takes Bactrocera cucurbitae pests as the detection target and proposes a fast and lightweight real-time detection and individual counting model for Tephritidae pests, named YOLO_MRC. This paper introduces three key improvements: (1) Constructing a new module called Multicat into the neck network increases the focus on the detection target by incorporating an attention mechanism; (2) Reducing the original three detection heads to two and then adjusting their sizes to decrease the number of parameters in the network model; (3) Devising a novel module, C2flite, to enhance the deep feature extraction capability of the backbone network. According to the above points, this paper conducts ablation experiments to compare the performances of different models. Experiments showed that the Multicat module significantly offsets the large increase in GFLOPs and processing time caused by reducing the detection head and can further reduce the number of parameters and improve the accuracy when combined with the C2flite module. On our Bactrocera cucurbitae pest dataset, the mAP@0.5 of the YOLO_MRC model reached 99.3%. Simultaneously, as the number of parameters decreases by 63.68%, GFLOPs is reduced by 19.75%, and the processing time is shortened by 5%. To ensure the validity of the model, YOLO_MRC is compared with four excellent detection models by using manual counting results as the benchmark. YOLO_MRC achieves an average pest counting accuracy of 94%, demonstrating superior performance in terms of model size and processing time. To further explore the performance of YOLO_MRC in multiclass insect detection tasks, we choose the public dataset Pest_24_640 for comparison with four state-of-the-art models. YOLO_MRC achieves a 3.6 ms processing time and 70.4% accuracy with only a 2.4 MB model size, which demonstrates the potential of YOLO_MRC in multiclass pest detection.

IMAGE FEATURE EXTRACTION METHODS FOR STRUCTURE DETECTION FROM UNDERWATER IMAGERY

Abstract. The use of autonomous underwater vehicles (AUVs) for surveying underwater infrastructure presents a potential cost saving in comparison to remotely operated vehicles (ROVs). One of the challenges when processing images of underwater structures captured by an AUV, is that vast number of images captured during the mission usually do not show the structure. For instance, images captured during the dive to the structure or of the sea floor, or of the deep sea facing away from the structure. Too many images captured, without relevant information for a 3D reconstruction of the structure, leads to increased processing time and issues during the reconstruction process. There are two solutions to reduce the images to only images showing the structure. Firstly, only images of the structure are captured in the first place or remove images that are not useful after the capture and before further processing. This study developed and evaluated techniques that would enable the first strategy to be applied in an AUV. To apply this strategy in an AUV, would require an on-board structure detection system to ensure that they are correctly orientated for capturing useful footage during a survey mission. However, the marine environment poses several challenges to image-based object detection. Furthermore, small AUVs have limited power and computational resources available while deployed on a mission. To investigate the suitability of creating a lightweight structure detection model for the purpose of image evaluation, three computationally efficient image feature extraction methods (colour moments, local binary patterns (LBP), and Haar wavelet decomposition) were evaluated for their ability to distinguish underwater structures from background areas using unsupervised k-means models. LBP was found to be an effective method for identifying underwater structures in open water conditions. For identifying a structure against the seabed, colour moments were identified as the most effective method.

Convolutional Neural Network for Earthquake Ground Motion Prediction Model in Earthquake Early Warning System in West Java

As urbanization continues, more people and infrastructure are concentrated in areas that are at risk from earthquakes. This can increase the potential damage and loss of life when earthquakes occur. Indonesia is a region that is near the boundary of three major tectonic plates which has a very high frequency of earthquake occurrences. Over the past two decades, a new approach to earthquake disaster risk mitigation has emerged. It is based on the advent of digital seismology and advances in data transmission and automatic processing that make it possible to send warnings before the largest ground motion that called the Earthquake Early Warning System (EEW). On-site EEW is a type of EEW that consists of limited seismic stations located at a specific destination/infrastructure (for early detection systems). On-site EEW estimates ground motion parameters directly from the characteristics of seismograms recorded by the system. An artificial intelligence approach to EEW is necessary to increase the speed and accuracy of information, which increases processing time, especially in areas very close to the epicenter

Effect of Changes Turbidity on Oxygen Solubility in Fish Pond Water Electrocoagulation Process

The water used for fish farming by the Mina Lestari is very dirty, because many soil particles are dissolved in water. The water with high dirty in generally has very high turbidity. So that oxygen is difficult to dissolve in water and is not sufficient for fish life. So many fish die, due to insufficient oxygen for breathing. To reduce water turbidity is treated by electrocoagulation process combined with the aeration process. The aim of the research was to study the effect of changes in turbidity on dissolved oxygen in water. The study began by measuring the turbidity using a turbidimeter and dissolved oxygen using a DO meter. Furthermore, pour 10 liters of water into the electrocoagulation process tank. The electrocoagulation was carried out at 12 volt or 0,3 ampere for 10 minutes. The process was stopped, then the water flowed into the settling tank and left for 30 minutes to precipitate of the dirt. The water flowed into a holding tank and followed aeration process by flowing air at a rate of 500 cc per minute for 30 minutes. Then followed measuring turbidity by turbidimeter and dissolved oxygen by DO meter. Subsequent studies were carried out using the same procedure with an interval of 10 minutes processing time for electrocoagulation process. The results showed that an increase in processing time can reduce water turbidity from 68 NTU to 45 NTU or 33.82% and increase dissolved oxygen from 2.8 mg/L to 5.3 mg/L or 89.29% at 50 minutes processing.

Filtered back projection vs. iterative reconstruction for CBCT: effects on image noise and processing time.

To assess the effect of standard filtered back projection (FBP) and iterative reconstruction (IR) methods on CBCT image noise and processing time (PT), acquired with various acquisition parameters with and without metal artefact reduction (MAR). CBCT scans using the Midmark EIOS unit of a human mandible embedded in soft tissue equivalent material with and without the presence of an implant at mandibular first molar region were acquired at various acquisition settings (milliamperages [4mA-14mA], FOV [5 × 5, 6 × 8, 9 × 10 cm], and resolutions [low, standard, high] and reconstructed using standard FBP and IR, and with and without MAR. The processing time was recorded for each reconstruction. ImageJ was used to analyze specific axial images. Radial transaxial fiducial lines were created relative to the implant site. Standard deviations of the gray density values (image noise) were calculated at fixed distances on the fiducial lines on the buccal and lingual aspects at specific axial levels, and mean values for FBP and IR were compared using paired t-tests. Significance was defined as p < 0.05. The overall mean for image noise (± SD) for FBP was 198.65 ± 55.58 and 99.84 ± 16.28 for IR. IR significantly decreased image noise compared to FBP at all acquisition parameters (p < 0.05). Noise reduction among different scanning protocols ranged between 29.7% (5 × 5 cm FOV) and 58.1% (5mA). IR increased processing time by an average of 35.1 s. IR significantly reduces CBCT image noise compared to standard FBP without substantially increasing processing time.

Experimental Investigation of the Effect of Microsilica on the Uniaxial Compressive Strength and Rupture Strain of Fine Sand

In many areas, natural soil does not have necessary resistance, some additives such as cement and microsilica are added to increase their resistance. In this research, the effect of microsilica on the strength and physical characteristics of fine sand stabilized with cement in sand samples containing cement and microsilica will be discussed. For this purpose, samples of fine sand with three different ratios of cement (2, 4, and 6 percent of soil weight) and four different ratios of microsilica (0, 0.5, 1, and 1.5 percent of soil weight) were mixed with optimal moisture, after Two curing periods of 1 and 7 days were subjected to compression and uniaxial compressive strength tests. The results of the experiments indicate that with the increase in the amount of cement and microsilica, the optimum moisture content in the density increases, the specific weight of the mixture of sand, cement and microsilica increases by adding these materials up to a certain amount, but after that it begins to decrease. . With increasing processing time, uniaxial compressive strength and rupture strain both increase. With a curing time of 1 day, for a fixed cement ratio, the compressive strength of sand-cement increases with the increase of microsilica up to about 20% of the cement weight, but after that, the resistance ratio decreases.&#x0D; With a curing time of 7 days for a constant cement ratio, the compressive strength increases with the increase of microsilica. It is also observed that in both curing times for different cement ratios, with the increase in the amount of microsilica, the relative deformation of the fracture increases.

Improved separation and quantification method for microplastic analysis in sediment: A fine-grained matrix from Arctic Greenland

Microplastic analysis requires effective separation and purification methods, which greatly depend on the matrix and target particle size. Microplastics-sediment extraction usually involves intermediate steps, increasing processing time and particle loss, particularly for particles <100 μm. Here, we propose an improved separation and quantification method for fine-grained sediment that minimizes microplastic loss by reducing intermediate steps. First, the sample is treated with CH3COOH, KOH and NaClO, and only transferred for the density separation (ZnCl2). The extraction efficiency, visually evaluated on spiked samples, was higher than 90% for particles >100 μm and 83% for 63‐75 μm particles. This indicates that a sequential extraction method reduces the risk of particle loss, particularly of the small size fraction. Comparatively, the extraction of ABS particles (20‐100 μm) was low (30%) but the recovery, assessed via μFTIR, was higher (55%). Additionally, the proposed method can be adapted to other sediment types and environmental matrices.

A dynamic annealing learning for PLSOM neural networks: Applications in medicine and applied sciences

In recent years, the field of unsupervised learning in neural networks has witnessed significant advancements. This innovative learning technique holds great promise for applications in diverse domains, with particular significance in the realms of medicine and applied sciences such as medical image analysis, drug discovery, predictive analytics, and pattern recognition. The neighborhood function plays a crucial role in the Improved Parameter-Less Self-Organizing Map (PLSOM2) algorithm by governing the rate of change in the vicinity of the winning neuron. During learning iterations, the neighborhood size is dynamically adjusted to encompass the activated neighboring neurons relative to the winning neuron. The training process begins with a larger neighborhood radius, promoting rough ordering, and gradually refines the process by reducing the radius for fine-tuning. This dynamic neighborhood size significantly influences the final training outcome of the PLSOM2 algorithm. However, one of the major bottlenecks of the PLSOM2 algorithm has been the slow ordering time and the challenge of determining an optimal neighborhood size. These issues often lead to topological defects during training, such as kinks or warps in the output maps. Merely increasing processing time has proven insufficient to overcome these challenges. In this paper, we propose a novel dynamic neighborhood function designed to accelerate the convergence process of the PLSOM2 algorithm, achieving the best shape and adaptation of the neighborhood width. The study demonstrates that by improving the neighborhood function of the PLSOM2 algorithm, map distortion can be effectively suppressed. Importantly, this enhancement enables the algorithm to handle network size, neighborhood size, and the large dimensional output space adeptly. It adaptively decreases the neighborhood size over time, ensuring convergence while appropriately managing network growth and avoiding twisting and misconfiguration. To assess the effectiveness of the proposed method, we conducted an extensive set of experiments across eight real-world benchmark datasets. Notably, the outcomes of these experiments are presented showcases the results of paired t-tests, highlighting the consistency and robustness of the proposed algorithm's performance. Despite non-significant p-values in many cases, the algorithm consistently excels across various datasets, underlining its practical significance. On the other hand, presents the results of One-way Analysis of Variance (ANOVA) tests. These results further reinforce the consistency of the performance of algorithm, as indicated by p-values exceeding the common significance threshold of 0.05. The combined findings from both tables provide strong statistical evidence of the proposed algorithm's robustness and effectiveness across diverse datasets. The proposed research introduces a dynamic neighborhood function that not only improves the PLSOM2 algorithm's convergence but also enhances its adaptability and topological preservation. These enhancements, supported by the statistical tests results, underscore the algorithm's practical significance and its suitability for real-world applications, where statistical significance may not always capture the full extent of its capabilities.

Synergistic Effects of Ultrasonic Rolling Textures and PTFE‐PPS/SiO2 Coatings on Dry Friction and Wear Properties of GCr15 Steel Surfaces

Ultrasonic surface rolling processing (USRP) is used to fabricate textures with different shapes and processing times on the surface of GCr15 substrates. The polytetrafluoroethylene‐polyphenylene sulfide/silicon dioxide (PTFE‐PPS/SiO2) lubricant coating is subsequently deposited on texture surfaces by electrohydrodynamic atomization. USRP texturing not only forms the texture shapes but also realizes the strengthening effects of increased hardness and residual stress. Synergistic effects of the PTFE‐PPS/SiO2 coating and different USRP textures on dry friction and wear properties are investigated. The results show that the synergistic effect of the linear texture with two processing times and the PTFE‐PPS/SiO2 coating achieves better dry friction and wear performance than the individual effect. The texture shape and the processing times both affect the synergistic effect. The increase in processing times has a positive effect on synergistic improvement. The linear texture plays a better synergistic effect compared with that of the wavy texture with the same processing times. The USRP textures affect the tribological properties of texture‐coating samples by mainly influencing the coating‐substrate adhesion. The texture with an appropriate shape and height can improve the adhesion with the coating, and thus synergistically improve the dry friction and wear properties of the substrate.

A new upper bound based on Dantzig-Wolfe decomposition to maximize the stability radius of a simple assembly line under uncertainty

This work presents a new upper bounding approach, based on Dantzig-Wolfe decomposition and column generation, for a relatively novel problem of designing simple assembly lines to maximize their stability radius under uncertainty in task processing times. The problem considers task precedence constraints, a fixed cycle time, and a fixed number of workstations. This NP-hard optimization problem aims to assign a given set of assembly tasks to workstations in order to find the most robust feasible line configuration. The robustness of the configuration is measured by the stability radius with respect to its feasibility, i.e., the maximum increase in task processing times, for which the cycle time constraint remains satisfied. The reformulation resulting from the Dantzig-Wolfe decomposition is enhanced with valid inequalities and tight assignment intervals are used to reduce the solution space of pricing sub-problems. In addition, a bisection method is proposed as a pre-processing technique to improve the initial upper bound on the stability radius, which is an input for the pricing sub-problem. Computational experiments show that the proposed approach can significantly improve the upper bound on the stability radius for the most challenging instances.

Assessing factors impacting inter-satellite variability of grassland curing estimates for fire monitoring in Victoria, Australia using remote sensing

ABSTRACT Grassland fires in Victoria, Australia pose significant environmental issues. Monitoring these fires relies on the Grassland Curing Degree (GCD) indicator and the corresponding Grassland Curing Map (GCM) for spatial distribution. Inter-satellite variability (ISV) assesses the variations in Grassland Curing Maps (GCMs) produced from remote sensing data with varying spatial resolutions (SR). Higher SR data improves GCM accuracy but increases processing time. ISV helps identify priority areas that need higher SR imageries. This study analyzes sample sites in Victoria and finds correlations between ISV, seasonality, temperature, precipitation, and distance to residential areas. Results reveal lower ISV during summers and autumns compared to winters and springs. Temperature shows a strong negative linear relationship with ISV, indicating that higher temperatures result in lower ISV. Precipitation exhibits a weak positive correlation with ISV, suggesting heavier precipitation leads to increased ISV. Distance to grasslands negatively correlates with ISV, indicating that greater distances from residential lands result in lower ISV. Based on these findings, it is recommended to use higher SR satellite data for GCM creation during winters and springs when temperatures are low, precipitation is heavy, and areas are closer to residential lands. Implementation suggestions are provided for fire management based on these results.

Effect of Ti3O5 addition on oxide evolution for HRB400

Abstract The transient evolution of oxide was studied after directly adding Ti3O5 powder into HRB400 steel. This experiment was carried out in magnesia crucible with vacuum induction furnace and the intermediate samples were taken at 1, 5, 10, and 15 min after Ti3O5 addition. At 20th min, the furnace was powered off to get furnace-cooled cast sample. For intermediate samples, it is found that with increasing treatment time, the titanium content increased though the acid–soluble aluminum content remained stable. Besides, the Ti-bearing oxides were observed by scanning electron microscope (SEM). Moreover, statistical analysis indicated that for Ti-bearing oxide, both number density and titanium content after further normalization increased with increasing processing time. For cast sample, the characteristic of Ti-bearing oxide at different heights are similar. These results confirmed the adding valid of Ti3O5, which may be due to its decomposition. Finally, after heat treatment, these introduced Ti-bearing oxides can induce the intragranular ferrite nucleation. This indicates the effectiveness of external adding method in oxide metallurgy.

The effect of time on lexical and syntactic processing in aphasia.

Processing deficits at the lexical level, such as delayed and reduced lexical activation, have been theorized as the source of breakdowns in syntactic operations and thus contribute to sentence comprehension deficits in individuals with aphasia (IWA). In the current study, we investigate the relationship between lexical and syntactic processing in object-relative sentences using eye-tracking while listening in IWA. We explore whether manipulating the time available to process a critical lexical item (the direct-object noun) when it is initially heard in a sentence has an immediate effect on lexical access as well as a downstream effect on syntactic processing. To achieve this aim, we use novel temporal manipulations to provide additional time for lexical processing to occur. In addition to exploring these temporal effects in IWA, we also seek to understand the effect that additional time has on sentence processing in neurotypical age-matched adults (AMC). We predict that the temporal manipulations designed to provide increased processing time for critical lexical items will 1) enhance lexical processing of the target noun, 2) facilitate syntactic integration, and 3) improve sentence comprehension for both IWA and AMC. We demonstrate that strengthening lexical processing via the addition of time can affect lexical processing and facilitate syntactic retrieval of the target noun and lead to enhanced interference resolution in both unimpaired and impaired systems. In aphasia, additional time can mitigate impairments in spreading activation thereby improving lexical access and reducing interference during downstream dependency linking. However, individuals with aphasia may require longer additions of time to fully realize these benefits.

Blanching effect on nutritionally important starch fractions of selected processing potato cultivars

Blanching is the primary treatment given to potato tubers before any other processing but has a differential impact on its starch content quality parameter. The present study aimed to evaluate the effect of three different blanching methods, i.e., water (100 °C for 5, 10, 15 min), steam (100 °C for 10, 15, 20 min) and microwave (640 W for 1, 3, 5 min) with different time-temperature combinations on nutritionally essential starch fractions of five potato processing cultivars (Kufri Chipsona 1, Kufri Chipsona 3, Kufri Chipsona 4, Kufri Himsona and Kufri Frysona). The total starch content in the raw samples ranged from 17.92 g/100 g to 69.27 g ̸ 100 g. All the blanching treatments caused a significant reduction of starch content, and 5 min water blanching treatment was standardized for optimum total starch content. Rapidly Digestible Starch content decreased as processing time was increased in all the treatments with maximum degradation by water blanching for 15 min. Slowly Digestible Starch content enhanced with an increase in processing time. The levels of the starch fractions varied depending on blanching methods. Blanching significantly influenced the various starch fractions in potatoes.

Efficacy of power assisted microdebrider in endoscopic adenoid resection

Background: Nasal obstruction, snoring, mouth breathing, ear ache is some of the most common problems encountered in pediatric otorhinolaryngological practice. The most common cause for the above symptoms is adenoid hypertrophy. Methods: Patients with age group of 4-16 years with symptoms of adenoid hypertrophy such as snoring, mouth breathing, ear ache and adenoid hypertrophy confirmed by Diagnostic Nasal Endoscopy and Radiological Investigations were included. Previous H/o surgery for adenoidectomy, bleeding disorders, cases with cleft palate or previous H/o cleft palate repair neuromuscular / craniofacial anomalies were excluded. Results: 3.10 mean grade was seen among study participants, with 39% of children having Grade 4 adenoids, 35% having Grade 3 adenoids, and 26% having Grade 2 adenoids. Snoring, nasal obstruction, and breathing scores all decreased statistically significantly. Conclusions: A microdebrider-assisted adenoidectomy has shown to provide full clearance with only a minor increase in haemorrhage and process time.

Effect of processing with ozone on in vitro and in situ ruminal degradability and intestinal digestibility of feather meal protein

This study aimed to investigate the effect of processing feather meal (FM) with ozone (O3) gas on nutrient content, ruminal degradability, and intestinal digestion of crude protein (CP) using in vitro and in situ techniques. Experimental treatments were designed as control (not-processed), O3-processed FM for 12 h (h; treat 1), O3-processed FM for 24 h (treat 2), and O3-processed FM for 48 h (treat 3). Five Ghezel sheep equipped with ruminal cannulas were used to measure the nutrient degradability and to obtain ruminal fluid for in vitro gas production. Processing with O3 gas significantly decreased dry matter (DM), ether extract (EE), and Ash content of the FM compared to the control (P < 0.05). Processing FM by O3 increased the volume of in vitro gas production (P < 0.05). The soluble and potentially degradable fractions of the DM and CP showed a significant increase with increasing processing time (P < 0.05). Processing with O3 gas increased quickly degradable protein (QDP), rumen degradable protein (RDP), effective rumen degradable protein (ERDP), and digestible undegradable protein (DUP) content as a proportion of feather meal CP. Still, undegradable dietary protein (UDP) decreased significantly. Intestinal digestibility of rumen undegradable protein (RUP) and Metabolize Protein (MP) content of the FM was also significantly increased with increased processing time and the highest values were achieved after 48 h of processing (P˂0.05). According to the results of this experiment, it can be concluded that processing FM using O3 for 48 h increases its nutritional value in ruminant nutrition.

Improvement of surface insulating performance for polytetrafluoroethylene film by atmospheric pressure plasma deposition

The surface flashover phenomenon across a vacuum-dielectric interface severely limits the service life and operational reliability of high voltage electrical equipment. Surface modification by atmospheric pressure plasma treatment is a promising method to improve the surface insulating performance of polymers. In order to explore the mechanism of plasma processing on the vacuum flashover characteristics of polymer materials, atmospheric pressure plasma deposition was used to treat polytetrafluoroethylene (PTFE) film. The surface parameters under different processing conditions, such as surface chemical composition, surface resistivity, surface charge decay and trap distribution, were tested and analyzed. The space charge distribution of PTFE and the flashover voltage in vacuum were measured. The results show that Si–O–Si and Si–OH groups are introduced on the surface of PTFE, and the characteristic peaks of PTFE are gradually weakened with the increase of processing time. The surface trap density increases and more traps with lower energy level arise with longer processing time. The plasma deposition changes the space charge distribution in PTFE body, and leads to positive charge accumulation inside the sample. The flashover field strength respectively increases by 15% and 70% in direct current (DC) voltage and microsecond pulse voltage after plasma deposition. The rapid dissipation of surface charge is the main reason for pulse flashover voltage enhancement, while the increase of surface leakage current due to lower surface resistivity and space charge accumulation in PTFE body make the DC flashover voltage reach the saturation point. Therefore the surface insulating and body performance of polymer materials after plasma modification processing should be considered comprehensively based on different applications.

Modeling and simulation of non-Newtonian fluid flows and heat transfer in a non-isothermal coiled tubing to oil well operations

The coiled tubing system has become an attractive option for oil industry operations in deep-water environments. Composed of a long, continuous, and flexible steel pipe, the coiled tubing provides a quick and safe intervention in active wells reducing the process time and cost. The fluid flow in curved pipes is characterized by centrifugal forces that significantly affect the frictional head loss and fluid heat transfer. The temperature variation affects the fluid physicochemical properties, such as rheology, density, thermal properties, and the cement setting time. To avoid the risk of the cement slurry setting before reaching the desired location, set retarders are added to its formulation. Therefore, it is essential to predict the temperature profile in coiled tubing to optimize the fluid formulation and process control, avoiding increased process time and cost. This research aimed to develop a mathematical model to simulate the heat transfer in coiled tubing fluid flow based on momentum, energy, and mass conservation. The mathematical model is composed of a set of partial differential equations spatially discretized using the finite volume technique. A case study was used, providing actual oilfield data to simulate the non-isothermic flow through the coiled tubing. Temperature data from a Brazilian offshore oil well abandonment process in two different operating conditions were used to validate the mathematical model. The results obtained in this paper indicated a good agreement between the model predictions and the oil well experimental data, with average relative errors of up to 2.5%.

Effect of microwave and conventional heat treatment on trypsin inhibitor activity and in vitro digestibility of edamame milk protein

AbstractEdamame milk is a protein‐dense milk derived from green soybeans harvested before they mature. Being a legume of soy origin, it contains antinutritional factors for example, serine protease inhibitors, which hinder its in vitro digestibility. The objective of this study was to evaluate the effect of microwave processing techniques in improving the in vitro digestibility (IVPD %) of edamame milk protein by varying processing time and temperature. Conventional and microwave‐assisted processing was employed to investigate the effect on in vitro protein digestibility (IVPD %), using temperatures 70°Ϲ, 85°Ϲ, and 100°Ϲ for 5, 10, and 15 min, respectively. Fourier‐transform infrared (FTIR) data showed microwave and conventional treatments significantly modified the Amide I region of the edamame milk protein and the extent of modification varied with variation in the treatment temperature. In the FTIR analysis β‐sheet content was observed to change little with an increase in the temperature, suggesting similarity in the surface hydrophobicity of the protein leading to similar IVPD % values for all treatment temperatures. The experiment resulted in increased in vitro digestibility with increasing time and temperature during microwave processing conditions and conventional thermal conditions. It was also observed that the trypsin inhibitor activity decreased with an increase in processing time and temperature.