Measurement Of Oxygen Content Research Articles

A Flame Image Soft Sensor for Oxygen Content Prediction Based on Denoising Diffusion Probabilistic Model

High-precision oxygen content measurement is crucial for statistical analysis of combustion chemical reaction. Deep learning based soft sensor is a new class of intelligent tools for monitoring combustion oxygen content. But in the actual production, data for sensors are often insufficient. A new soft sensing model is proposed to display the excellent performance of denoising diffusion probabilistic model (DDPM) in data generation. Firstly, a UNet based soft sensor is designed by integrating self-attention mechanism into the convolution layers. Then, a denoising loss function is designed to link the feature extraction process of soft sensor model with the reverse denoising process of DDPM, and the noise prediction neural network of DDPM is used to improve the feature extractability of the soft sensor model. Finally, the proposed model is compared with common models. The effectiveness and superiority of the proposed soft sensing model for oxygen content prediction, especially in the case with a small sample size, are both confirmed by the results.

Plasma Functionalization for Enhanced Natural Dye Affinity on Bioplastics: A Novel Approach

ABSTRACTBioplastics offer biodegradability and reduced environmental impact but often use nondegradable synthetic dyes. This study introduces a novel application of glow discharge plasma (GDP) technology to modify cassava starch‐based bioplastic sheets, specifically optimizing conditions to enhance their hydrophilicity and adhesion for natural dyes. This research systematically tailors plasma treatment parameters to maximize the interaction between bioplastics and eco‐friendly natural dyes. Optimal conditions, identified as 30 W for 3 min using oxygen, air, and nitrogen plasma treatments, demonstrate significant advancement in sustainable material development. Analyses (ATR‐FTIR, EDX, XPS) showed increased concentration of polar functional groups and oxygen content, with FE‐SEM and water contact angle measurements revealing improved surface roughness and wettability, respectively. Oxygen plasma treatment yielded the highest hydrophilicity and crystallinity of the polymer. Tensile tests indicated increased strength and decreased elongation. UV‐Vis spectroscopy confirmed enhanced dye retention and reduced leaching. This study demonstrates GDP's potential to incorporate eco‐friendly natural dyes into bioplastics, thereby contributing to the development of more sustainable materials.

Oxygen Measurement in Cuprate Superconductors Using the Dissolved Oxygen/Chlorine Method

We have developed a dissolved oxygen (DO) method with differential equation (DE) correction. We measured the oxygen content in La-based and Y-based superconductors, and succeeded in measuring the oxygen content simply in one-third of the time required by the iodometric titration method. However, there was a problem with Bi-based superconductors where the measured oxygen content was smaller compared to the iodometric titration method. We hypothesized that not only O2 but also Cl2 gas is generated when dissolving Bi-based superconductors and developed a dissolved oxygen/chlorine (DO/Cl) method with DE correction. This method uses only a dissolved oxygen sensor and a dissolved chlorine sensor to measure the dissolved oxygen and dissolved chlorine content in Bi2Sr2−xLaxCuOy, allowing for the calculation of copper valence and oxygen content. The results from the DO/Cl method with DE correction show that the measured copper valence and oxygen content differ very little from those obtained using the iodometric titration method, with discrepancies within 0.016 and 0.008, respectively. Additionally, this method reduces the measurement time by one-third compared to the iodometric titration method. The results demonstrate that the DO/Cl method with DE correction can effectively measure the copper valence and oxygen content in cuprate superconductors, and using hydrochloric acid as the experimental solution is superior to sulfuric acid and nitric acid.

Estimation of differential pathlength factor from NIRS measurement in skeletal muscle

The utilization of continuous wave (CW) near-infrared spectroscopy (NIRS) device to measure non-invasively muscle oxygenation in healthy and disease states is limited by the uncertainties related to the differential path length factor (DPF). DPF value is required to quantify oxygenated and deoxygenated heme groups’ concentration changes from measurement of optical densities by NIRS. An integrated approach that combines animal and computational models of oxygen transport and utilization was used to estimate the DPF value in situ. The canine model of muscle oxidative metabolism allowed measurement of both venous oxygen content and tissue oxygenation by CW NIRS under different oxygen delivery conditions. The experimental data obtained from the animal model were integrated in a computational model of O2 transport and utilization and combined with Beer-Lambert law to estimate DPF value in contracting skeletal muscle. A 2.1 value was found for DPF by fitting the mathematical model to the experimental data obtained in contracting muscle (T3) (Med.Sci.Sports.Exerc.48(10):2013–2020,2016). With the estimated value of DPF, model simulations well predicted the optical density measured by NIRS on the same animal model but with different blood flow, arterial oxygen contents and contraction rate (J.Appl.Physiol.108:1169–1176, 2010 and 112:9–19,2013) and demonstrated the robustness of the approach proposed in estimating DPF value. The approach used can overcome the semi-quantitative nature of the NIRS and estimate non-invasively DPF to obtain an accurate concentration change of oxygenated and deoxygenated hemo groups by CW NIRS measurements in contracting skeletal muscle under different oxygen delivery and contraction rate.

A Hybrid Soft Sensor Model for Measuring the Oxygen Content in Boiler Flue Gas.

As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas's oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants. This study proposes a prediction model for the oxygen content in the flue gas that combines the whale optimization algorithm (WOA) and long short-term memory (LSTM) networks. Among them, the whale optimization algorithm (WOA) was used to optimize the learning rate, the number of hidden layers, and the regularization coefficients of the long short-term memory (LSTM). The data used in this study were obtained from a 350 MW power generation unit in a coal-fired power plant to validate the practicality and effectiveness of the proposed hybrid model. The simulation results demonstrated that the whale optimization algorithm-long short-term memory (WOA-LSTM) model achieved an MAE of 0.16493, an RMSE of 0.12712, an MAPE of 2.2254%, and an R2 value of 0.98664. The whale optimization algorithm-long short-term memory (WOA-LSTM) model demonstrated enhancements in accuracy compared with the least squares support vector machine (LSSVM), long short-term memory (LSTM), particle swarm optimization-least squares support vector machine (PSO-LSSVM), and particle swarm optimization-long short-term memory (PSO-LSTM), with improvements of 4.93%, 4.03%, 1.35%, and 0.49%, respectively. These results indicated that the proposed soft sensor model exhibited more accurate performance, which can meet practical requirements of coal-fired power plants.

Modeling of the blood oxygen and heart rate measurement system

Monitoring heart rate and blood oxygen content is an integral part of monitoring health and physiological parameters of a person’s condition. Thus, measurement of oxygen content and heart rate becomes a rather popular problem. To solve the task set in this work, it is necessary to conduct a detailed analysis of the area and determine the main physical properties of blood at different indicators of the oxygen content in it. Also, when conducting this study, attention should be paid to the influence of the oxygen content in the blood on the heart rhythm of a person.

Structural features and thermodynamic properties of Pr0.5Ba0.25Sr0.25MnO3–δ

The structure, oxygen non-stoichiometry, defect equilibrium, and thermodynamic properties of perovskite-type Pr0.5Ba0.25Sr0.25MnO3–δ manganite were studied. It was shown that the manganite can be air synthesized in one step at 1450 °C. Though having a disordered arrangement of Pr, Ba, and Sr atoms, the compound crystallizes with a tetragonal structure (S.G. I4/mcm) due to the regular tilts of MnO6 octahedra. The oxygen content in oxide as a function of oxygen partial pressure and temperature was measured by coulometric titration, and the data were used for the modeling of defect equilibrium. The oxygen content measurements show stability of the manganite at 973–1223 K and oxygen pressure decrease down to 10−16–10−12 atm. The equilibration of defects in Pr0.5Ba0.25Sr0.25MnO3-δ involves oxygen exchange with the gaseous ambient, intrinsic electron excitation, and oxygen disordering reactions. According to thermodynamic calculations, oxygen sites available for oxygen exchange are characterized by praseodymium-rich coordination. The disordering caused by the half substitution of barium by strontium leads to a lower energy of chemical bonding of the labile oxygen in Pr0.5Ba0.25Sr0.25MnO3–δ compared to the parent Pr0.5Ba0.5MnO3–δ manganite.

Measurement of powder bed oxygen content by image analysis in laser powder bed fusion

Costs and resource efficiency of laser powder bed fusion (L-PBF) are highly dependent on the ability to produce high quality parts with recycled powders. There is a need to control the quality of the material, which has a direct influence on the performance of the printed parts. Particles oxidation is known to increase with repeated powder recycling and can be a good indicator of powder degradation. The characterization of powders oxygen content is time-consuming, expensive, and usually carried out ex-situ on non-reusable quantities that are not necessarily representative of the entire feedstock. In this work, a new methodology was developed to measure the oxygen content of powders by in-line scanning of powder bed layers. The method takes advantage of stainless steel particles coloration related to their oxidation level in order to assess their oxygen concentration as a function of Red, Green and Blue channel values of image scans. The calibration procedure once carried out, several recycled powder samples were scanned and analyzed, and the determined powder beds oxygen contents were demonstrated to be in accordance with ex-situ measurements. The results highlight a new opportunity to monitor and evaluate powder degradation in-situ on powder bed layers by image analysis.

Measurement of oxygen content in water with purity through soft sensor model

The development and commercial use of virtualized sensing (smooth) in the manufacturing of Polyethylene Terephthalate (PET) and sewage treatment are both covered in this paper. The major purpose of using based on an input Artificial Neural Network (ANN) to give operations with real-time estimates of the PET permeability. Additionally, the redundancy viscosity measures provided by the ANN-based soft-sensor (ANNSES) were used to compare the outcomes of the procedure fully equipped. Everything was established that the proposed ANNSES could accurately infer the polymeric viscosity, allowing for the implementation of this soft sensor in real-time system integration. Multidisciplinary modeling, ANN, & a composite method that includes a primary clustering technique are used for data from industrial wastewater operations. The combined ANN approach decreases the overfitting problem with biological systems & improves forecasting accuracy. The results show that the hybrid ANN method can be used to both explain why difficult wastewater treatment processes are nonlinear & to obtain knowledge in imbalanced datasets.

Augmented flame image soft sensor for combustion oxygen content prediction

Oxygen content is one of the most critical factors for high-efficiency combustion. Online measurement of oxygen content from flame images is important but still challenging. For construction of an oxygen content prediction model, most current feature extraction methods are not straightforward. Additionally, there are always sufficient data for common operating conditions in practice, while only limited data for other operating conditions. The data collection process for model training is costly and time-consuming. To tackle the problem, this work presents an augmented flame image soft sensor for automated combustion oxygen content prediction. A convolutional neural network (CNN) regression model is designed to predict the oxygen content directly from flame images, without a single feature extraction process. Moreover, a regression generative adversarial network with gradient penalty is proposed to generate flame images with oxygen content labels. It overcomes the imbalanced and insufficient data problem arising in the CNN regression model training. The proposed soft sensor is compared with several common regression methods for oxygen content prediction. Experimental results show that the proposed method can predict the combustion oxygen content with high accuracy from flame images although the original datasets are imbalanced.

Development of a solar-powered small autonomous surface vehicle for environmental measurements

Nowadays, the use of autonomous surface vehicles has increased in popularity. Autonomous surface vehicles can be adapted to perform water quality measurements, water contamination and hazards detection, garbage collection, aquatic animal population control, shore control, and other applications. This paper shows the novel construction of a small solar-powered autonomous surface vehicle. The vehicle was manufactured of composite materials of natural origin. It was equipped with an electric engine powered by ultralight photovoltaic panel, and a set of sensors and actuators used for autonomous operation and measurements of such factors, as pH, turbidity, purity, and oxygen content in water. The investigations carried out have been divided into three main parts. First, the geometry of the proposed autonomous surface vehicle was modelled using computational fluid dynamics and optimal shape of hull and deck was determined. Then, a dedicated construction of the photovoltaic panel was developed and tested under real conditions. The maximum power generated was 41.7 W when solar radiation was 850 W/m2. The matched power related to the area of 1 m2 was higher by 9.7% – 34.3% compared to other tested market-available panels. Finally, simulations in the TRNSYS software were carried out to determine the operational parameters of the developed autonomous surface vehicle under different operating scenarios. Simulations showed that the operation scenario of the autonomous surface vehicle significantly affects its primary energy consumption due to the energy supplied from the grid to recharge the system after each day of operation. The change in location can increase the primary energy savings, since among the investigated locations the primary energy saving ratio varied between about 30 and 50%. The analysis pointed out that the developed construction of the solar-powered small autonomous surface vehicle is a really promising solution and can be considered as an addition or replacement to the actual available water quality monitoring systems.

Numerical investigation of the influence of the source and detector position for optical measurement of lung volume and oxygen content in preterm infants.

There is an urgent need for improved respiratory surveillance of preterm infants. Gas in scattering media absorption spectroscopy (GASMAS) is emerging as a potential clinical cutaneous monitoring tool of lung functions in neonates. A challenge in the clinical translation of GASMAS is to obtain sufficiently high signal-to-noise ratios in the measurements, since the light attenuation is high in human tissue. Previous GASMAS studies on piglets have shown higher signal quality with an internal source, as more light propagates through the lung and the loss due to scattering and absorption is less. In this article we simulated light propagation with an intratracheal and a dermal source, and investigated the signal quality and lung volume probed. The results suggest that GASMAS has the potential to measure respiratory volumes; and the sensitivity is higher for an intratracheal source which also enables to probe most of the lung.

Voltammetry in sheep's blood: Membrane-free amperometric measurement of O2 concentration

An amperometric method was applied for the electroanalytical measurement of oxygen content in sheep's blood. This method was based on a bare platinum microdisc electrode coupled with the use of chronoamperometry. A linear relationship between the chronoamperometric current and the oxygen concentration was observed in both saline solution and sheep's blood. The developed method was able to measure the oxygen percentage with an error of ca. 1.3% in sheep's blood. In addition, this article presents the first study on direct voltammetry in sheep's blood and a dissociative CE process was proposed to explain the electrochemical behaviour of oxygen reduction in blood on a platinum electrode in which the ‘free’ oxygen was first dissociated from oxyhaemoglobin prior to electron transfer with the magnitude of the observed current controlled by the diffusion of oxyhaemoglobin to the electrode where for sufficiently large electrodes (greater than ca. 1 μm in radius) the dissociation proceeds to completion on the voltammetric timescale allowing quantitative measurements.

Efficiency of different flows for apneic oxygenation when using high flow nasal oxygen application \u2013 a technical simulation

BackgroundPreoxygenation and application of apneic oxygenation are standard to prevent patients from desaturation e.g. during emergency intubation. The time before desaturation occurs can be prolonged by applying high flow oxygen into the airway. Aim of this study was to scientifically assess the flow that is necessary to avoid nitrogen entering the airway of a manikin model during application of pure oxygen via high flow nasal oxygen.MethodsWe measured oxygen content over a 20-min observation period for each method in a preoxygenated test lung applied to a human manikin, allowing either room air entering the airway in control group, or applying pure oxygen via high flow nasal oxygen at flows of 10, 20, 40, 60 and 80 L/min via nasal cannula in the other groups. Our formal hypothesis was that there would be no difference in oxygen fraction decrease between the groups.ResultsOxygen content in the test lung dropped from 97 ± 1% at baseline in all groups to 43 ± 1% in the control group (p < 0.001 compared to all other groups), to 92 ± 1% in the 10 L/min group, 92 ± 1% in the 20 L/min group, 90 ± 1% in the 40 L/min group, 89 ± 0% in the 60 L/min group and 87 ± 0% in the 80 L/min group. Apart from comparisons 10 l/ min vs. 20 L/min group (p = .715) and 10/L/min vs. 40 L/min group (p = .018), p was < 0.009 for all other comparisons.ConclusionsSimulating apneic oxygenation in a preoxygenated manikin connected to a test lung over 20 min by applying high flow nasal oxygen resulted in the highest oxygen content at a flow of 10 L/min; higher flows resulted in slightly decreased oxygen percentages in the test lung.

Relation between crystal structure and lattice oxygen content of sintered reaction‐bonded silicon nitride

AbstractWhen reaction‐bonded silicon nitride containing MgO/Y2O3 additives is sintered at three different temperatures to form sintered reaction‐bonded silicon nitride (SRBSN), the thermal conductivity increases with sintering temperature. The β‐Si3N4 (silicon nitride) crystals of SRBSN ceramics were synthesized and characterized to investigate the relation between the crystal structure and the lattice oxygen content. The hot‐gas extraction measurement result and the crystal structure obtained using Rietveld analysis suggested that the unit cell size of the β‐Si3N4 crystal increases with the decrease in the lattice oxygen content. This result is reasonable considering that the lattice oxygen with the smaller covalent radius substitutes nitrogen with the larger one in the β‐Si3N4 crystals. The lattice oxygen content decreased with increasing sintering temperature which also correlated with increase in thermal conductivity. Moreover, it is noteworthy from the viewpoint that it may be possible to apply the lattice constant analysis for the nondestructive and simple measurement of the lattice oxygen content that deteriorates the thermal conductivity of the β‐Si3N4 ceramics.

Effect of Molybdenum Layer Formed at the Inner Surface in Mo/MoO&lt;sub&gt;2&lt;/sub&gt;–type Zirconia Oxygen Sensor on Continuous Measurement of Oxygen Content in Molten Steel

Effect of Molybdenum Layer Formed at the Inner Surface in Mo/MoO&lt;sub&gt;2&lt;/sub&gt;–type Zirconia Oxygen Sensor on Continuous Measurement of Oxygen Content in Molten Steel

Chemically modified Jatropha curcas oil for biolubricant applications

Jatropha curcas oil is one of interesting renewable resources for preparation of biolubricants. However, direct application of this oil as a biolubricant is restricted due to its low oxidative stability. This drawback can be overcome by molecule structural redesign through a chemical modification process at its unsaturated functional groups. Jatropha curcas oil was modified via epoxidation, ring opening and esterification processes. Its conversion to the epoxidized oil was performed by using in situ performic acid as a catalyst, then reaction with oleic acid in the presence of p-toluenesulfonic acid as a catalyst in the ring opening process. The final esterification process with oleic acid was catalyzed by sulfuric acid. Molecular structures of the modified oil were determined by measurements of the oxirane oxygen content and by Fourier-transform infrared (FTIR), proton and carbon nuclear magnetic resonance (1H NMR and 13C NMR) spectroscopy analyses. The results showed that the oxidative stability, viscosity, flash point and pour point of the final product were significantly improved. In specific, the ring opening and esterification processes inducing branching and bending in the final oil molecular structure have resulted in the improved viscosity index of 135, the pour point of -29?C and the increased flash point of 250?C.

Prediction of Oxygen Content Using Weighted PCA and Improved LSTM Network in MSWI Process

The accurate and real-time measurement of oxygen content in flue gas is the cornerstone of high incineration efficiency and economic benefits for municipal solid waste incineration (MSWI) plants. However, conventional hardware oxygen analyzers are difficult to obtain the oxygen content in flue gas timely and precisely. In this article, a weighted principal component analysis (WPCA) algorithm combined with improved long short-term memory (ILSTM) network is proposed for oxygen content prediction. First, to reduce the model complexity, a WPCA is developed to calculate mutual information correlation coefficients between principal components and the quality variable. Second, the LSTM network is exploited to establish a prediction model, and its hyperparameters are obtained with the particle swarm optimization (PSO) algorithm to improve the generalization ability of the prediction model. Finally, the effectiveness of the proposed prediction method is validated by a benchmark simulation and the real industrial data. And the comparison results with other methodologies demonstrate the considerable prediction performance of the proposed WPCA-ILSTM model.

Anthropomorphic optical phantom of the neonatal thorax: a key tool for pulmonary studies in preterm infants.

.Significance: Gas in scattering media absorption spectroscopy (GASMAS) is a technique for gas sensing in cavities surrounded by scattering materials. GASMAS could be translated to the clinic to monitor lung function continuously and noninvasively in neonates. Accurate tissue phantoms are essential to assess the strengths and limitations of gas spectroscopy in gas-containing cavities in the human body.Aim: The aim is to develop a detailed protocol to produce a long-lasting, multistructure tissue phantom of the thorax of a neonate. The phantom mimics the geometry and the optical properties of the main organs of the thorax and has an empty pulmonary cavity that facilitates GASMAS monitoring of gas content.Approach: The anatomic geometry of heart, lungs, bones, muscle, fat, and skin was obtained from a neonatal computed tomography scan. Once segmented, organs were 3D printed and used to create negative rubber molds. The entire thorax was built in phantom material (silicone as matrix, black ink as absorber, and silica microspheres as scatters) by placing all phantom organs inside the muscle structure. Our phantom recipe was customized by mixing specific ratios of ink and spheres to match the optical properties of the different organs that were consider to be homogeneous.Results: An anthropomorphic thorax phantom with the desired optical properties ( and ) at 760 nm was built and used to obtain “transdermal” GASMAS measurements of oxygen content within the lung cavity.Conclusion: A protocol to build a robust optical phantom of the thorax of a neonate was used to conduct benchtop studies. This recipe can be implemented to reproduce the geometry and optical properties of any human or animal tissue.

Effect of nickel and calcium substitution on the superconducting properties of the Y1–x Ca x Ba2Cu3–y Ni y O7 – δ system with 0.1 ≤ x ≤ 0.3 and 0.0 ≤ y ≤ 0.2

The effect of calcium (Ca) substitution in yttrium (Y) site and nickel (Ni) substitution in copper (Cu) site of Y1–x Ca x Ba2Cu3–y Ni y O7– δ (x = 0.1, 0.2, 0.3 and y = 0.0, 0.1, 0.2), materials prepared by solid state reaction method was investigated. Structural, average oxygen content and electrical resistivity measurements were studied using X-ray Diffraction (XRD), iodometric titration and four-probe method. From XRD analysis, all samples showed the orthorhombic symmetry of YBa2Cu3O7 – δ (YBCO). The average oxygen content δ increases with the substitution of Cu by Ni and Y by Ca. Such a phenomenon allows the appearance of oxygen vacancies in the samples. From the electrical resistivity results, critical temperature TC decreases with the content of substitution y of Ni in Y0.9 Ca0.1Ba2Cu3–y Ni y O7 – δ system. In Y0.9Ca0.1Ba2Cu2.9Ni0.1O7 – δ sample (y = 0.1), the variation ΔTC falls only of 7% approximately, whereas a reduction in ΔTC of 40% is observed in the YBa2Cu3–y Ni y O7 – δ system. The critical temperatures TC of the Y1–x Ca x Ba2Cu2.9Ni0.1O7 – δ system (x = 0.1, 0.2 and 0.3) decreases with the content of substitution x of Ca until reaching a minimum at 67 K when x = 0.2, then it increases again.