Characteristics of laser-induced breakdown spectroscopy of liquid slag

Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350℃, 1400℃, and 1450℃. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350℃, 1400℃, and 1450℃.The normalized full spectrum combined with partial least squares(PLS) quantification modeling was used, using the Ca Ⅱ 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450℃ has been found to yield superior results compared to both 1350℃ and 1400℃, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Estimating the yield stress of soft materials via laser-induced breakdown spectroscopy

Taking three typical soft samples prepared respectively by loose packings of 77-,95-,and 109-μm copper grains as examples,we perform an experiment to investigate the energy-dependent laser-induced breakdown spectroscopy(LIBS)of soft materials.We discovered a reversal phenomenon in the trend of energy dependence of plasma emission intensity:increasing initially and then decreasing separated by a well-defined critical energy.The trend reversal is attributed to the laser-induced recoil pressure at the critical energy just matching the sample's yield strength.As a result,a one-to-one correspondence can be well established between the samples'yield stress and the critical energy that is easily obtainable from LIBS measurements.This allows us to propose an innovative method for estimating the yield stress of soft materials via LIBS with attractive advantages including in-situ remote detection,real-time data collection,and minimal destructive to sample.

Estimating the grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms

Recent work has validated a new method for estimating the grain size of microgranular materials in the range of tens to hundreds of micrometers using laser-induced breakdown spectroscopy(LIBS).In this situation,a piecewise univariate model must be constructed to estimate grain size due to the complex dependence of the plasma formation environment on grain size.In the present work,we tentatively construct a unified calibration model suitable for LIBS-based estimation of those grain sizes.Specifically,two unified multivariate calibration models are constructed based on back-propagation neural network(BPNN)algorithms using feature selection strategies with and without considering prior information.By detailed analysis of the performances of the two multivariate models,it was found that a unified calibration model can be successfully constructed based on BPNN algorithms for estimating the grain size in the range of tens to hundreds of micrometers.It was also found that the model constructed with a priorguided feature selection strategy had better prediction performance.This study has practical significance in developing the technology for material analysis using LIBS,especially when the LIBS signal exhibits a complex dependence on the material parameter to be estimated.

Quantitative Determination of Cr in Ink by Laser-induced Breakdown Spectroscopy（LIBS） Using ZnO as Adsorbent

Laser-induced breakdown speetroscopy（LIBS） technique was applied to detecting chromium in ink with ZnO as adsorbent, and the LIBS spectra were preproeessed by wavelet denoising. The laser energy and delay time were optimized depending on the signal-to-noise ratio（SNR） and intensity of three analysis atomic lines（Cr 425.43 nm, Cr 427.48 nm and Cr 428.97 nm）. Compared with other analysis lines, atomic line of Cr 427.48 nm was selected as the analysis line for the quantitative analysis of Cr in ink as the calibration curve of it showed a better linear rela- tionship （correlation coefficient R^2=0.9778）, and the relative error of Cr in the measured ink was 52.96%. Since the single spectral line used for calibration curve method is often influenced by matrix effect and other factors, partial least squares regression（PLS） as multivariate calibration method has been applied to predicting the concentration of Cr in ink, and the relative error of Cr in the measured ink was 10.48%. The result obtained from the PLS method was better than that from the calibration curve when comparing the relative error, demonstrating that, based on adsorbent, LIBS combined with PLS provides an effective, practical and convenient technique for the determination of trace element in aqueous solution.

A data selection method for matrix effects and uncertainty reduction for laser-induced breakdown spectroscopy

Severe matrix effects and high signal uncertainty are two key bottlenecks for the quantitative performance and wide applications of laser-induced breakdown spectroscopy(LIBS).Based on the understanding that the superposition of both matrix effects and signal uncertainty directly affects plasma parameters and further influences spectral intensity and LIBS quantification performance,a data selection method based on plasma temperature matching(DSPTM)was proposed to reduce both matrix effects and signal uncertainty.By selecting spectra with smaller plasma temperature differences for all samples,the proposed method was able to build up the quantification model to rely more on spectra with smaller matrix effects and signal uncertainty,therefore improving final quantification performance.When applied to quantitative analysis of the zinc content in brass alloys,it was found that both accuracy and precision were improved using either a univariate model or multiple linear regression(MLR).More specifically,for the univariate model,the root-mean-square error of prediction(RMSEP),the determination coefficients(R^(2))and relative standard derivation(RSD)were improved from 3.30%,0.864 and 18.8%to 1.06%,0.986 and 13.5%,respectively;while for MLR,RMSEP,R^(2)and RSD were improved from 3.22%,0.871 and 26.2%to 1.07%,0.986 and 17.4%,respectively.These results prove that DSPTM can be used as an effective method to reduce matrix effects and improve repeatability by selecting reliable data.

The comparison of manganese spectral lines for self-absorption reduction in LIBS using laser-induced fluorescence

The detection of manganese(Mn)in steel by laser-induced breakdown spectroscopy(LIBS)provides essential information for steelmaking.However,self-absorption greatly disrupts the LIBS spectral lines of Mn with high content.In this study,to minimize self-absorption for Mn spectral lines in LIBS,laser-induced fluorescence(LIF)was applied.Compared with conventional LIBS,the self-absorption factors(α)of Mn I 403.08,403.31,and 403.45 nm lines were reduced by 90%,88%,and 88%,respectively;the root mean square errors of crossvalidation were decreased by 88%,85%,and 87%,respectively;the average relative errors were reduced by 93%,90%,and 91%,respectively;and average relative standard deviations were decreased by 29%,32%,and 33%,respectively.The LIBS-LIF was shown to successfully minimize the self-absorption effect and spectral intensity fluctuation and improve detection accuracy.

Self-absorption effects of laser-induced breakdown spectroscopy under different gases and gas pressures

The self-absorption effect is one of the main factors affecting the quantitative analysis accuracy of laser-induced breakdown spectroscopy.In this paper,the self-absorption effects of laserinduced 7050 Al alloy plasma under different pressures in air,Ar,and N2have been studied.Compared with air and N2,Ar significantly enhances the spectral signal.Furthermore,the spectral self-absorption coefficient is calculated to quantify the degree of self-absorption,and the influences of gas species and gas pressure on self-absorption are analyzed.In addition,it is found that the spectral intensity fluctuates with the change of pressure of three gases.It can also be seen that the fluctuation of spectral intensity with pressure is eliminated after correcting,which indicates that the self-absorption leads to the fluctuation of spectral intensity under different pressures.The analysis shows that the evolution of optical thin spectral lines with pressure in different gases is mainly determined by the gas properties and the competition between plasma confinement and Rayleigh–Taylor instability.

Laser-Induced Breakdown Spectroscopy (LIBS) Applied in the Differentiation of <i>Arabica</i>and <i>Robusta</i>Coffee

Coffee is one of the most consumed and commercialized crops in the world, which is why there is a potential risk to find arabica coffee (an expensive variety) adulterated with robusta coffee (a cheaper variety). The currently used technique for certifying coffee, High Performance Liquid Chromatography (HPLC), requires the sample to be subjected to a chemical treatment prior to analysis;in addition, the equipment is bulky and can not be moved easily. Laser Induced Breakdown Spectroscopy (LIBS) is a technique which does not require that samples be subjected to a chemical pretreatment, and equipment is small and portable, this can save valuable time in coffee trading. The purpose of this research was to demonstrate that LIBS can be applied to solve various problems related with the coffee authentication. Green coffee pills with different concentrations of arabica and robusta varieties were analyzed by LIBS, the results were used in the construction of calibration curves for the detection of the degree of simulated adulteration in coffee. It was found that the relative intensities of Ca (392.4 nm), Sr (407.1 nm), N (500.5 nm) and Na (588.7 nm), as well as the intensity ratios of Ca II (392.4 nm)/N I (500.5 nm), Sr I (407.1 nm)/N I (500.5 nm)and N I (500.5 nm)/Na I (588.7 nm) can be used for this purpose. It is concluded that the differentiation of coffee and the detection of its adulteration is possible with the use of LIBS. Further, with the use of an Artificial Neural Network (ANN) of type Multilayer Perceptron, it was possible to correctly classify the spectra of arabica and robusta roasted coffee.

Detection and quantification of Pb and Cr in oysters using laser-induced breakdown spectroscopy

The quantitative determination of heavy metals in aquatic products is of great importance for food security issues.Laser-induced breakdown spectroscopy(LIBS)has been used in a variety of foodstuff analysis,but is still limited by its low sensitivity when targeting trace heavy metals.In this work,we compare three sample enrichment methods,namely drying,carbonization,and ashing,for increasing detection sensitivity by LIBS analysis for Pb and Cr in oyster samples.The results demonstrate that carbonization can remove a significant amount of the contributions of organic elements C,H,N and O;meanwhile,the signals of the metallic elements such as Cu,Pb,Sr,Ca,Cr and Mg are enhanced by3–6 times after carbonization,and further enhanced by 5–9 times after ashing.Such enhancement is not only due to the more concentrated metallic elements in the sample compared to the dried ones,but also the unifying of the matter in carbonized and ashed samples from which higher plasma temperature and electron density are observed.This condition favors the detection of trace elements.According to the calibration curves with univariate and multivariate analysis,the ashing method is considered to be the best choice.The limits of detection of the ashing method are 0.52 mg kg-1 for Pb and0.08 mg kg-1 for Cr,which can detect the presence of heavy metals in the oysters exceeding the maximum limits of Pb and Cr required by the Chinese national standard.This method provides a promising application for the heavy metal contamination monitoring in the aquatic product industry.

Matrix effect suppressing in the element analysis of soils by laser-induced breakdown spectroscopy with acoustic correction

Laser-induced breakdown spectroscopy(LIBS)has been used for soil analysis,but its measurement accuracy is often influenced by matrix effects of different kinds of soils.In this work,a method for matrix effect suppressing was developed using laser-induced plasma acoustic signals to correct the original spectrum,thereby improving the analysis accuracy of the soil elements.A good linear relationship was investigated firstly between the original spectral intensity and the acoustic signals.The relative standard deviations(RSDs)of Mg,Ca,Sr,and Ba elements were then calculated for both the original spectrum and the spectrum with the acoustic correction,and the RSDs were significantly reduced with the acoustic correction.Finally,calibration curves of MgⅠ285.213 nm,CaⅠ422.673 nm,SrⅠ460.733 nm and BaⅡ455.403 nm were established to assess the analytical performance of the proposed acoustic correction method.The values of the determination coefficient(R~2)of the calibration curves for Mg,Ca,Sr,and Ba elements,corrected by the acoustic amplitude,are improved from 0.9845,0.9588,0.6165,and 0.6490 to 0.9876,0.9677,0.8768,and 0.8209,respectively.The values of R~2 of the calibration curves corrected by the acoustic energy are further improved to 0.9917,0.9827,0.8835,and 0.8694,respectively.These results suggest that the matrix effect of LIBS on soils can be clearly improved by using acoustic correction,and acoustic energy correction works more efficiently than acoustic amplitude correction.This work provides a simple and efficient method for correcting matrix effects in the element analysis of soils by acoustic signals.

Rock and Soil Classification Using PLS-DA and SVM Combined with a Laser-Induced Breakdown Spectroscopy Library

Laser-induced breakdown spectroscopy （LIBS） has become a powerful technology in geological applications. The correct identification of rocks and soils is critical to many geological projects. In this study, LIBS database software with a user-friendly and intuitive interface is developed based on Windows, consisting of a database module and a sample identification module. The database module includes a basic database containing LIBS persistent lines for elements and a dedicated geological database containing LIBS emission lines for several rock and soil reference standards. The module allows easy use of the data. A sample identification module based on partial least squares discriminant analysis （PLS-DA） or support vector machine （SVM） algorithms enables users to classify groups of unknown spectra. The developed system was used to classify rock and soil data sets in a dedicated database and the results demonstrate that the system is capable of fast and accurate classification of rocks and soils, and is thus useful for the detection of geological materials.

高精度煤质成分快速定量分析LIBS系统研究

煤质成分的快速、高精度定量分析是工厂对能源高效利用的重要环节。本文通过利用搭建的激光诱导击穿技术煤质快速分析仪,对所采集煤样工业特性固定碳(FixC),灰分(Ash),干燥无灰基的挥发分(Vdaf),热值(Q)进行了定量测量,并对测得的数据与实验室人工测量数据进行对比分析,检验得出该方法在煤质分析方面准确快速。通过对设备稳定性和动态精密度的测试,结果表明了快速煤质分析仪具有较高的稳定性,满足国标规定值。在预测灰分值分布跨度为5%~60%的煤样时,通过增加样品量,增加模型中对大灰分煤样的预测权重,实现预测均方根误差RMSEP<1%的预测精度,挥发分和全硫的RMSEP<1%,热值的RMSEP<0.18 MJ/kg。预测结果均达到工业分析要求,可以满足工业现场的应用需求,具有应用于煤质在线检测方面的广阔前景。

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Laser-induced breakdown spectroscopy（LIBS） is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis（PCA） and support vector machine（SVM） is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy（91.38% for the real rock） is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.

基于LIBS光谱实现燃煤企业碳排放量高精度快速预测

准确预测煤炭的碳排放数据是实现中国“双碳”目标的重要基础。在煤炭入炉前完成碳排放量数据和煤质指标的精确、快速预测,将转变目前“先排放,后检测”的碳排放数据计算方式,对落实“双碳”战略意义重大。以煤炭碳含量和煤质指标的精准预评估为研究目标,通过自行搭建激光诱导击穿光谱(Laser Induced Breakdown Spectroscopy,LIBS)系统和LIBS光谱统计分析,利用主成分分析-偏最小二乘(PCA-PLS)结合小样本算法对训练集进行回归训练,建立了一种煤炭的碳排放预测模型,并通过在新疆A电厂和山东B电厂中的实际应用,证明了LIBS系统在煤质指标分析中的可靠性。结果表明:所提出的预测方法与传统方法相比绝对误差最大为0.0256 t,表现出较好的碳排放预测能力。所提方法可以用于燃煤企业碳排放量的精准预测,为实现煤炭碳排放和煤质指标的预评估提供了一种较为高效的技术解决路径。

A theoretical study of the signal enhancement mechanism of coaxial DP-LIBS

In the field of dual-pulse laser-induced breakdown spectroscopy(DP-LIBS)research,the pursuit of methods for determining pulse intervals and other parameters quickly and conveniently in order to achieve optimal spectral signal enhancement is paramount.To aid researchers in identification of optimal signal enhancement conditions and more accurate interpretation of the underlying signal enhancement mechanisms,theoretical simulations of the spatiotemporal processes of coaxial DP-LIBS-induced plasma have been established in this work.Using a model based on laser ablation and two-dimensional axisymmetric fluid dynamics,plasma evolutions during aluminum–magnesium alloy laser ablation under single-pulse and coaxial dualpulse excitations have been simulated.The influences of factors,such as delay time,laser fluence,plasma temperature,and particle number density,on the DP-LIBS spectral signals are investigated.Under pulse intervals ranging from 50 to 1500 ns,the time evolutions of spectral line intensity,dual-pulse emission enhancement relative to the single-pulse results,laser irradiance,spatial distribution of plasma temperature and species number density,as well as laser irradiance shielded by plasma have been obtained.The study indicates that the main reason behind the radiation signal enhancement in coaxial DP-LIBS-induced plasma is attributed to the increased species number density and plasma temperature caused by the second laser,and it is inferred that the shielding effect of the plasma mainly occurs in the boundary layer of the stagnation point flow over the target surface.This research provides a theoretical basis for experimental research,parameter optimization,and signal enhancement tracing in DP-LIBS.

Study on Cluster Analysis Used with Laser-Induced Breakdown Spectroscopy

Supervised learning methods（eg.PLS-DA,SVM,etc.） have been widely used with laser-induced breakdown spectroscopy（LIBS） to classify materials;however,it may induce a low correct classification rate if a test sample type is not included in the training dataset.Unsupervised cluster analysis methods（hierarchical clustering analysis,K-means clustering analysis,and iterative self-organizing data analysis technique） are investigated in plastics classification based on the line intensities of LIBS emission in this paper.The results of hierarchical clustering analysis using four different similarity measuring methods（single linkage,complete linkage,unweighted pair-group average,and weighted pair-group average） are compared.In K-means clustering analysis,four kinds of choosing initial centers methods are applied in our case and their results are compared.The classification results of hierarchical clustering analysis,K-means clustering analysis,and ISODATA are analyzed.The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS.

Quantitative analysis of C, Si, Mn, Ni, Cr and Cu in low-alloy steel under ambient conditions via laser-induced breakdown spectroscopy

A diode-pumped solid-state laser （DPSSL） with a high energetic stability and long service life is applied to ablate the steel samples instead of traditional Nd：YAG laser pumped by a xenon lamp, and several factors, such as laser pulse energy, repetition rate and argon flow rate, that influence laser-induced breakdown spectroscopy （LIBS） analytical performance are investigated in detail. Under the optimal experiment conditions, the relative standard deviations for C, Si, Mn, Ni, Cr and Cu are 3.3%-8.9%, 0.9%-2.8%, 1.2%-4.1%, 1.7%-3.0%, 1.1%-3.4% and 2.5%-8.5%, respectively, with the corresponding relative errors of 1.1%-7.9%, 1.0%-6.3%, 0.4%-3.9%, 1.5%-6.3%, 1.2%-4.0% and 1.2%-6.4%. Compared with the results of the traditional spark discharge optical emission spectrometry technique, the analytical performance of LIBS is just a little inferior due to the less stable laser-induced plasma and smaller amount of ablated sample by the laser. However, the precision, detection limits and accuracy of LIBS obtained in our present work were sufficient to meet the requirements for process analysis. These technical performances of higher stability of output energy and longer service life for DPSSL, in comparison to the Q-switch laser pumped by xeon lamp, qualify it well for the real time online analysis for different industrial applications.

A rapid classification method of aluminum alloy based on laser-induced breakdown spectroscopy and random forest algorithm

As an important non-ferrous metal structural material most used in industry and production,aluminum(Al) alloy shows its great value in the national economy and industrial manufacturing.How to classify Al alloy rapidly and accurately is a significant, popular and meaningful task.Classification methods based on laser-induced breakdown spectroscopy(LIBS) have been reported in recent years. Although LIBS is an advanced detection technology, it is necessary to combine it with some algorithm to reach the goal of rapid and accurate classification. As an important machine learning method, the random forest(RF) algorithm plays a great role in pattern recognition and material classification. This paper introduces a rapid classification method of Al alloy based on LIBS and the RF algorithm. The results show that the best accuracy that can be reached using this method to classify Al alloy samples is 98.59%, the average of which is 98.45%. It also reveals through the relationship laws that the accuracy varies with the number of trees in the RF and the size of the training sample set in the RF. According to the laws, researchers can find out the optimized parameters in the RF algorithm in order to achieve,as expected, a good result. These results prove that LIBS with the RF algorithm can exactly classify Al alloy effectively, precisely and rapidly with high accuracy, which obviously has significant practical value.

Analysis of Pulverized Coal by Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy （LIBS） has been used to detect atomic species in various enviromnents. The quantitative analysis （C, H, O, N and S） of representative coal samples are being carried out with LIBS, and the effects of particle size are analyzed. A powerful pulse Nd：YAG laser is focused on the coal sample at atmosphere pressure, and the emission spectra from laser-induced plasmas are measured by time-resolved spectroscopy, and the intensity of analyzed spectral lines is obtained through observing the laser plasma with a delay time of 0.4 #s. The experimental results show that the slope of calibration curve is nearly 1 when the concentration of the analyzed element is relatively low, and the slope of curve is nearly 0.5 when the concentration of C is higher than other elements. In addition, using the calibration-free model without self-absorption effect, the results show that the decreasing of particle size leads to an increase of the plasma temperature.