Laser-induced Fluorescence Technology Research Articles

Transformer fault diagnosis based on DBO-BiLSTM algorithm and LIF technology

Abstract In response to the deficiencies of traditional power transformer fault detection techniques, such as low sensitivity and the inability for online monitoring, a novel transformer fault diagnosis model combining Laser-Induced Fluorescence (LIF) technology with deep learning is proposed. Initially, the spectral data of transformer insulation oil is acquired using LIF technology, yielding spectral data for various fault types. Subsequently, MinMaxScaler (MMS) and Standard Normalized Variate (SNV) methods are employed for denoising and preprocessing the spectral data. The preprocessed data is then subjected to dimensionality reduction using Linear Discriminant Analysis (LDA) and T-distributed Stochastic Neighbor Embedding (T-SNE) to ensure that the spectral data retains maximal feature information while minimizing its dimensionality. Following this, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), Dung Beetle Optimizer-Bi-directional Long Short Term Memory (DBO-BiLSTM), Convolutional Neural Network (CNN), and Support Vector Machine (SVM) models are constructed. The reduced-dimensional data is fed into each of the five models for training to facilitate transformer fault diagnosis. Through comparative analysis among the five models, the optimal model is selected. Experimental results indicate that the DBO-BiLSTM model is the most suitable for transformer fault diagnosis in this experiment, underscoring its significant implications for ensuring the safety of power systems.

In situ visualization of polycyclic aromatic hydrocarbon hydrothermal decomposition process utilizing planar induced fluorescence: A mechanistic analysis

Hydrothermal decomposition of coal or biomass for hydrogen production is a clean and efficient technology. Comprehending the hydrothermal decomposition mechanism of Polycyclic Aromatic Hydrocarbons (PAHs) is pivotal for advancing biomass or coal hydrothermal reaction technology. Traditional non-in situ measurement methods present challenges in thoroughly exploring the intricacies of PAHs’ hydrothermal reaction mechanisms. To address this issue, this study establishes an in situ visualization platform leveraging planar laser-induced fluorescence (PLIF) technology. Fluorescence signals emanating from naphthalene are meticulously measured in situ across diverse temperature conditions (450 °C-650 °C). The decomposition rate characteristics of PAH in hydrothermal process are thereby elucidated. A proportional increase in the reaction rate of naphthalene with elevated temperatures, concomitant with an augmented risk of coking. Moreover, the generation of hydroxyl radicals (OH) during the hydrothermal decomposition process was observed in the in situ visualization platform. Afterwards, to promote the generation of OH radicals and improve the conversion of PAHs, small molecule additives (formic acid, acetic acid, methanol, and ethanol) were added into the reactions. The experimental results indicate that the addition of small molecules increases the conversion rate of PAH and significantly reduces coking. These radicals effectively occupy reactive sites on naphthalene, thereby suppressing the coking proclivity of PAHs. This work provides a novel method to investigate the mechanism of hydrothermal process and offers a potential way to improve the conversion efficiency.

Qualitative and quantitative detection of microplastics in soil based on LIF technology combined with OOA-ELM/SPA-PLS

Traditional methods for detecting soil microplastics include chemical digestion, density separation, staining, etc. These methods often only provide qualitative analysis of microplastics and are difficult to accurately quantify. To address this issue, laser induced fluorescence (LIF) technology is introduced, which can achieve high sensitivity, non-destructive, and rapid detection, and improve the accuracy and efficiency of qualitative and quantitative analysis of soil microplastics. Therefore, based on LIF technology, a qualitative prediction model for optimizing Extreme Learning Machine (ELM) using Osprey Optimization Algorithm (OOA) and a quantitative prediction model combining Continuous Projection Algorithm (SPA) with Partial Least Squares Algorithm (PLS) were established. Qualitative analysis used eight types of microplastics, with AS, PA66, ABS, PS, POM, PBT, PET, and PVC as experimental samples. Initially, spectral data is obtained using LIF technology and the raw spectral data is pre-processed using polynomial smoothing algorithm (SG) and moving average (MA) methods. Subsequently, factor analysis (FA) and local linear embedding (LLE) techniques are applied to dimensionality reduction and then input into the ELM model. At the same time, the ELM parameter is optimized by using OOA algorithm. In the quantitative analysis, PA66 powder and soil are mixed in different proportions (10:0, 8:2, 5:5, 2:8, 0:10), SG and MA pre-processing are performed on the spectral data obtained by LIF technology, and SPA is used to select the spectral characteristic wavelength for the original data and the pre-processed spectral data, and then a linear regression model is established combined with PLS. In order to facilitate the comparison of data, the experimental results are reserved to 5 decimal places, the experimental results indicate that LIF technology is faster than traditional methods in extracting raw data. The convergence of the OOA-optimized ELM model is better than that of the PSO-optimized ELM model. Among all the models of qualitative analysis, SG-LLE-OOA-ELM had the best prediction effect, with mean square error (MSE) of 0.00834, coefficient of determination (R2) of 0.99841, root mean square error (RMSE) of 0.09134, and mean absolute error (MAE) of 0.07038. Among the models of quantitative analysis, the best effect is SG-SPA-PLS model with R2 of 0.99951.

The application of laser‑induced fluorescence in oil spill detection.

Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.

A Mine Water Source Prediction Model Based on LIF Technology and BWO-ELM.

The traditional methods for identifying water sources in coal mines lack the ability to quickly detect water sources and are prone to causing secondary pollution of samples. In contrast, laser induced fluorescence (LIF) technology has been introduced for the identification of coal mine water sources due to its high sensitivity and real-time performance. However, extreme learning machine (ELM) have shortcomings in randomly selecting weights and biases. The Beluga Whale Optimization (BWO) algorithm has efficient optimization capability, global search capability, adaptability and parallelism, and can find the optimal weights and biases in a short time. The combination of LIF technology and BWO-ELM model can be applied to quickly identify the welling water source in coal mine. Select sandstone water and old goaf water from the Huainan mining area as experimental samples, and mix them in different proportions to prepare 7 mixed water samples for testing. Utilize LIF technology to obtain spectral curve images, preprocess them with polynomial smoothing algorithm (SG) and spectral multiple scattering correction (MSC), and perform dimensionality reduction using factor analysis (FA) and linear discriminant analysis (LDA) methods. Finally, construct ELM models, Long Short Term Memory (LSTM) models, BWO-ELM models, and Particle Swarm Optimization Extreme Learning Machine(PSO-ELM) models for the dimensionality reduced data. In order to improve the reliability and accuracy of the results, the experimental results were kept to 5 decimal places. From the experimental results, it can be seen that SG-LDA-BWO-ELM has the best fitting effect, with a fitting coefficient of 0.99990, a root mean square error of 0.00041, a mean square error approaching 0, and an average absolute error of 0.00021. It has the best convergence and the smallest absolute error among all models, making it the most suitable for identifying mine water inrush. It is of great significance for preventing and controlling mine water disasters and ensuring coal mine production safety.

Transformer fault diagnosis based on MPA-RF algorithm and LIF technology

Power transformers are essential components in power systems used to regulate voltage, transmit electrical energy, provide isolation, and match loads. They contribute to efficient and reliable electricity transmission and distribution. However, traditional methods for diagnosing transformer faults are time-consuming, not suitable for online monitoring, and greatly affected by environmental conditions. In this experiment, we propose the use of laser-induced fluorescence (LIF) technology for transformer fault detection. LIF technology is a method for analyzing and detecting specific molecules or atoms in samples. It combines laser technology with fluorescence measurements, making it a powerful analytical tool. It achieves high sensitivity and selectivity in analyzing molecules and atoms by exciting and detecting fluorescence in the sample. This makes it an important technology in scientific research and practical applications. Furthermore, LIF technology has not been previously applied to power transformer fault diagnosis. Therefore, this experiment introduces a transformer fault diagnosis model based on the marine predators algorithm (MPA) optimized random forest (RF) algorithm and LIF spectroscopy technology. Four different oil samples were selected for experimentation: crude oil, thermally faulty oil, partially moist oil, and electrically faulty oil. First, LIF technology for collect spectral images and data from the different fault oil samples. The obtained spectral data was preprocessed using two methods, multivariate scatter correction (MSC) and standardization method (SNV). Then, principal component analysis (PCA) and kernel principal component analysis (KPCA) for reducing the dimensionality of the preprocessed spectral data. Finally, the RF model, MPA-RF model, and PSO-RF model were established; and the reduced data was input into the model for training. Through comparisons of the predictions on the test set, evaluation metrics of the algorithm (including fitting coefficient, MSE, RMSE, and RMSE), and iteration convergence curves, the best transformer fault diagnosis model was identified. The results show that the MSC-KPCA-MPA-RF model has the best matching resule, with a fitting coefficient of 0.9963 and a mean square error of 0.0047. The SNV-PCA-MPA-RF model has the worst fitting effect, with a fitting coefficient of 0.9840 and a mean square error of 0.0199. Through comparisons of the convergence of different models, the MSC-KPCA-MPA-RF model has the best convergence and is the most applicable model for transformer fault diagnosis in this experiment. This model has significant implications for ensuring the safety of the power system.

Measurement of interfacial shear stress in gas–liquid two-phase stratified flow

Gas–liquid two-phase stratified flow exists in many industrial processes. Although the flow pattern is simple, the interfacial shear prediction of stratified flow is still the focus of the study. The calculation of the shear stress at the gas–liquid interface is closely related to the measurement of the void fraction and pressure drop of the stratified flow. In this study, a new method for the calculation of interfacial shear stress of gas–liquid two-phase stratified flow is proposed. Differential pressure measurement and planar laser-induced fluorescence technology are combined to obtain important parameters for stratified flow under low-speed flow conditions (Ql = 0.10–0.25 m3/h, Qg = 0.35–1.00 m3/h). The interfacial shear stress is successfully calculated using macroparameters. The uncertainty associated with the calculated parameters using the proposed method is 2.67%, and this study verifies the accuracy of the linear relationship. The method provides a new way to obtain the interfacial shear stress of gas–liquid stratified flow.

Laser induced fluorescence technology for early detection of diabetes via optical fiber microfluidic sensor

This research explores the use of direct write laser technology to reduce manufacturing costs and time by using Polymethyl methacrylate (PMMA) sheet. The process has a maximum channel width of 208 µm for zigzag channels and 205.66 µm for straight channels, limiting droplet miniaturization. Microfluidics technology offers potential for creating graphene, glucose, and rhodamine B biosensor devices for various applications. Combining these materials offers benefits like miniaturization, reduced response time and chemical consumption, and improved device reproducibility and sensitivity. The study also highlights advancements in chip materials, device assembly, and material preparation for graphene-based microfluidic sensors. The findings show that optical fiber microfluidic biosensor devices have higher spectral resolution than traditional fluorescence spectrometers, enabling precise identification of mixture components and fluorescence enhancement analyses.

Quantitative measurement and application of droplets on physical surfaces based on LIF technology

Abstract Contaminated surfaces play a significant role in the transmission of respiratory infectious diseases. To address this issue, we presented a novel quantitative detection method for droplets on physical surfaces, based on the laser-induced fluorescence technique. The proposed detection method was demonstrated in a realistic high-speed train compartment scenario by simulating the process of droplet release during passengers' breathing and coughing. The experimental results showed that this method could offer high precision (10−1 mg/m2) for detecting minute substance concentrations, and its ease of operation makes it suitable for complex engineering environments. The results also revealed that under the combined effects of the indoor airflow and breathing airflow, the range of droplets released by breathing activity exceeded two rows in front of and behind the release position. Simultaneously, we observed that a large number of droplets settled on the seat surfaces on both sides of the same row as the releaser, with over 36% of these droplets concentrated on the backrest area of the seats. As the respiratory jet velocity increased, the location with the most sediment droplets (accounting for 8% of the total sedimentation) occurred on the seat directly in front of the releaser, and approximately 48% of the droplets were found on the back of this seat. Our proposed method overcomes the shortcomings of existing experimental methods in quantitatively capturing the motion characteristics of droplets in complex flow fields.

Experimental investigation on the flow patterns in a T-junction under steady and rolling motion conditions using PLIF

Flow mixing experiments are performed on the Ocean Conditions Simulation Platform (OCSP) test facility to investigate the flow pattern evolution mechanisms in a T-junction with branch jet under steady and rolling motion conditions. Plane Laser-Induced Fluorescence (PLIF) technology is used to display and test the instantaneous flow pattern in the T-junction. The effects of the main fluid Reynolds number, inflow rate ratio and rolling Reynolds number on the flow pattern characteristics are discussed at a range of 2110–15850, 1–20 and 0–3520, respectively. Based on the experimental results, new classification criteria of flow pattern in the T-junction under steady condition are proposed from the perspective of the evolution of vortex structure. The flow patterns are classified into the single shear vortex, co-rotating vortex pair, transition region and counter-rotating vortex pair according to the fluid Reynolds number and inflow rate ratio. Under rolling motion condition, the branch jet periodically sweeps up and down in the T-junction due to the effect of additional inertial force. The sweeping phenomenon of the branch jet caused by rolling motion enhances the flow instability in the T-junction that can be inhibited by increasing the fluid Reynolds number or decreasing the inflow rate ratio and rolling Reynolds number. In addition, the influence mechanisms of fluid Reynolds number, inflow rate ratio and rolling Reynolds number on the flow pattern in the T-junction are investigated. These results can inform the analysis, design, safe operation and performance improvement of equipment in various industrial applications involving flow mixing in a T-junction.

Fast Identification Method of Mine Water Source Based on Laser-Induced Fluorescence Technology and Optimized LSTM

There is a great threat to the production safety of coal mines caused by mine water disasters. Traditional identification methods are not adapted to the efficiency of today’s coal mining and do not offer the advantage of accurate detection in real-time. In this study, the Mayfly Algorithm (MA) was used to optimize the Long Short-Term Memory (LSTM) network, combined with laser-induced fluorescence technology, to apply it to the identification of mine water sources for the prevention of mine water disasters and post-disaster relief work. Taking sandstone water and goaf water as the original samples, five mixed water samples were also prepared by mixing the sandstone water and goaf water in different proportions, giving a total of seven water samples to be tested. Laser-induced fluorescence technology was used to obtain the fluorescence spectral data of water samples, and then the Linear Discriminant Analysis (LDA) dimensionality reduction algorithm and the Principal Component Analysis (PCA) dimensionality reduction algorithm were used to reduce the dimensions of the original spectral data. Then, three architectures, including LSTM, GA-LSTM (optimization of the LSTM by genetic algorithm) and MA-LSTM were designed to identify mine water sources. Finally, from the results’ analysis, MA-LSTM performs best in many aspects after PCA dimensionality reduction and has the best identification effect. These results supported the feasibility of the novel method.

Research on transformer fault diagnosis based on an IWHO optimized MS1DCNN algorithm and LIF spectrum.

Accurate diagnosis of transformer faults can effectively improve the enduring reliability of power grid operation. Aiming at overcoming the problems of long time consumption and low diagnostic rate in the past diagnosis methods, this article designs a laser-induced fluorescence (LIF) detection system, which can be combined with a multi-scale one-dimensional convolution neural network (MS1DCNN) to diagnose transformer fault categories. The structural parameters of MS1DCNN are optimized using the improved wild horse optimizer (IWHO). Electrical fault oil, thermal fault oil, normal oil and locally damped oil are used as raw materials for the experiment. First, the LIF spectral data of the four kinds of oil samples are obtained, and the spectral data obtained are pretreated by standard normal variate (SNV) and multiple scattering correction (MSC), and the dimensions are reduced by linear discriminant analysis (LDA) and kernel principal component analysis (KPCA). Then the dimensionality reduced data are imported into the MS1DCNN algorithm for learning, and the parameters of MS1DCNN are optimized using the IWHO algorithm. Finally, the experiment shows that the efficiency and precision of LIF technology for raw data extraction are higher than for traditional methods; in comparison with the same type of algorithm, MSC has a better preprocessing effect, KPCA has a better dimensionality reduction effect, MS1DCNN has a better prediction effect, and IWHO has a better optimization effect. Compared to them, the MSC-KPCA-IWHO-MS1DCNN model has the best diagnostic ability, with a mean square error (MSE) of 4.9037 × 10-4, mean absolute error (MAE) of 0.0179, and goodness of fit (R2) of 0.9996. Transformer fault intelligent diagnosis is necessary for the sustained and stable operation of power networks.

Experimental study on spatial distribution characteristics of cylinder-wall oil films under fuel spray impinging condition of GDI engine

This research investigates GDI (Gasoline Direct Injection) fuel spray characteristics, and spray impingement on cylinder-wall oil film were carried out in a constant volume chamber. For studying free spray and spatial distribution characteristics of oil film under spray impinging conditions, the backlight method and LIF (Laser Induced Fluorescence) technology were used, respectively. The influence of injection pressure, ambient pressure, and ambient temperature on the distribution characteristics of oil film with spray impingement were examined. Detailed results on coalescence/splash thresholds criteria of oil film were obtained by considering the incident spray Weber number and the injected fuel mass. In addition, the formation mechanism of oil dilution and splash following the collision were analyzed. It has been found that the incident Weber number is not the only discriminant parameter that can be used to determine the splash criticality of a fuel droplet or spray hitting an oil film. Meanwhile, spray impingement at a higher injection pressure results in a wider spread of oil film, whereas a higher ambient pressure inhibits the spread of oil film.

Optimization of Corrugated Sheet Packing Structure Based on Analysis of Falling Film Flow Characteristics

The falling film flow characteristics of a liquid on the surface of corrugated sheet packing are crucial for its mass transfer performance in various industrial applications. In this study, a falling film flow experiment with laser-induced fluorescence technology was conducted to validate the flow characteristics of a falling film simulated using computational fluid dynamics (CFD). The influences of Reynolds number (Re) and the packing structure on flow characteristics were analyzed with quantitative film thickness and wetted area obtained through three-dimensional simulation. The results show that the CFD model can accurately predict the liquid falling–film flow behavior and calculate the characteristic parameters. For sinusoidal corrugated sheets, when Re reaches 500, the groove flow changes into a rivulet flow along the adjacent ripples and the wetted area is at its largest, about 0.022 m2. However, relative to the geometric area of the corrugated sheet, the wetted area can only reach 20% of the surface area, and the overall wetting performance is still poor. Triangular and trapezoidal corrugated sheets were further proposed and proved to improve the wetting area compared with the sinusoidal sheet, with maximum increases of 23% and 9%, respectively. On this basis, extensive research was carried out on the corrugation angle. The results show that a triangular corrugated sheet with a 75° corrugated angle was more conducive to the flow of the liquid film, and the wetted area was 38.8% of the surface area.

Simultaneous temperature field investigations of blockage accidents in a narrow rectangular channel by experiments and simulations

The temperature field distribution in the blockage accidents is critical to the safe operation of the reactor. Therefore, the Laser-Induced Fluorescence (LIF) technology is utilized to explore the simultaneous temperature distribution under the edge blockage accident in a narrow rectangular channel. The particle image velocimetry (PIV) technology is also employed to investigate the influence of flow field on temperature field. In particular, the simultaneous temperature distribution under various heat flux density conditions is investigated. According to the experimental and simulation results, a shear layer will be formed near the obstacle and the shear layer will be reattached to the right wall in the downstream of the wake to form vortical structures, where the heat transfer capacity of the fluid is weakened significantly. Moreover, the realizable k-ε model in CFD code can be used to predict the evolution of the fluids under a blockage accident in the narrow rectangular channel.

Capturing of particles in suspension flow through a micro cross-shaped channel

Capturing of microscale particles through microfluidics has been a flourishing research area in chemical and biological engineering applications. This work follows our previous report (Zhang et al., AIChE J., vol. 66, 2019, e16822) by using micro plane laser-induced fluorescence (μPLIF) and high-speed photography technology to study the flow of a diluted suspension of buoyant particles in a micro cross-shaped channel at 30≤Re≤350. Particular attention is paid to particle accumulation behaviors caused by vortex breakdown within the steady engulfment flow and vortex shedding flow. Three particle capture modes are identified in accumulation regions including the particle chain mode, the steady and unsteady particle cluster mode at 100≤Re≤300. Multiple stable equilibrium orbits formed by captured particles in accumulation regions are attributed to the axial force balance. In addition, perturbations in particle trajectories induced by the vortex shedding flow or particle-particle interactions contribute to the particle release.

Research on fault diagnosis of transformer based on laser induced fluorescence technology

Transformer fault diagnosis is an indispensable part of normal operation of power equipment. Aiming at the problem that traditional gas chromatography and other methods take a long time to detect fault types, this paper proposes a laser-induced fluorescence spectroscopy (LIF) technology, combined with differential mutation brainstorm optimization algorithm (DBSO) to optimize the ELM model to identify transformer fault diagnosis types. Four different transformer oils were selected as experimental samples, including thermal fault oil, electrical fault oil, local damp oil and crude oil. LIF technology was used to obtain different spectral images of four oil samples. Firstly, the obtained fluorescence spectra were pre-treated by MSC and Normalize, and the dimensionality was reduced by PLS. Then, the data after dimension reduction are input into the ELM model for training, and the BSO algorithm and DBSO algorithm are used to optimize the parameters of ELM. Finally, the experiments show that the DBSO-ELM model pre-processed by MSC has the highest recognition accuracy, the goodness of fit (R2) is 1, and the mean square error (MSE) reaches 3.205e-31, which is higher than that of ELM model and BSO-ELM model. In the case of the same pre-processing, the fitness values based on the mean square error rate of the training set are lower than those of the other types of recognition algorithm models. Therefore, the MSC-PLS-DBSO-ELM model has the best recognition effect, which can be extended to transformer fault diagnosis and improve the accuracy and safety of power equipment detection.

Rapid Detection of Three Common Bacteria Based on Fluorescence Spectroscopy.

As an important part of environmental water quality monitoring, efficient bacterial detection has attracted widespread attention. Among them, LIF (laser-induced fluorescence) technology has the characteristics of high efficiency and sensitivity for bacterial detection. To simplify the experimental process of bacterial detection, fluorescence emission spectra of E. coli (Escherichia coli) and its deactivated controls, K. pneumoniae (Klebsiella pneumoniae) and S. aureus (Staphylococcus aureus), were analyzed with fluorescence excitation by a 266 nm laser. By analyzing the results, it was found that the dominant fluorescence peaks of bacterial solutions at 335~350 nm were contributed by tryptophan, and the subfluorescence peaks at 515.9 nm were contributed by flavin; besides, K. pneumoniae and S. aureus had their own fluoresces characteristics, such as tyrosine contributing to sub-fluorescence peaks at 300 nm. The three species of bacteria can be differentiated with whole fluorescence spectrum by statistically analysis (p < 0.05), for various concentrations of aromatic amino acids and flavin in different bacteria. The experimental results also proved that the inactivation operation did not alter the spectral properties of E. coli. The indexes of fluorescence intensity and FIR (fluorescence intensity ratio, I335~350/I515.9) can be used to retrieve the bacteria concentration as well as for bacteria differentiation using the index of slopes. The detection limit of bacteria is less than ~105 cell/mL using laser induced fluorescence methods in the paper. The study demonstrated the rapid detection capability of the LIF bacterial detection system and its great potential for rapid quantitative analysis of bacteria. This may bring new insight into the detection of common bacteria in water in situ.

Determination of Oil Pollutants by Microchannel Laser Induced Fluorescence Technology

Laser-induced fluorescence technology is an effective method for detecting oil pollutants. The laser induced fluorescence detection system was developed, and four parallel microchannel sample pools were designed to improve the detection accuracy of fluorescence signals. The grating scanning system is improved by introducing grating feedback link to improve the wavelength scanning accuracy of the system.The experimental results show that the wavelength scanning resolution of the microchannel laser-induced fluorescence detection system is improved by 0.4 nm. Aiming at the defects of the traditional parallel factor algorithm which is sensitive to the sample components, slow operation speed and easy to fall into local convergence, the generalized inverse singular value decomposition is used to improve the parallel factor algorithm. Configure different concentrations of diesel, gasoline and kerosene solution samples. The experimental verification shows that the excitation-emission spectra of each component obtained by the improved parallel factor algorithm are highly consistent with the measured spectral characteristics, indicating that the microchannel laser-induced fluorescence detection system combined with the improved parallel factor analysis method can accurately identify oil pollutants.

Heat Release Rate and Flame Thickness of n-butane/Air Jet Flame Measured by Planar Laser-Induced Fluorescence Technology

Heat Release Rate and Flame Thickness of n-butane/Air Jet Flame Measured by Planar Laser-Induced Fluorescence Technology