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.

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.

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.

Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness

A non-contact method for millimeter-scale inspection of material surface flatness via Laser-Induced Breakdown Spectroscopy(LIBS)is investigated experimentally.The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness,ranging from 0 to 4.4 mm,by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm.It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases,implying that the method investigated here is feasible.It is also found that,for achieving the inspection of surface flatness within such a wide range,when univariate analysis is applied,a piecewise calibration model must be constructed.This is due to the complex dependence of plasma formation conditions on the surface flatness,which inevitably complicates the inspection procedure.To solve the problem,a multivariate analysis with the help of Back-Propagation Neural Network(BPNN)algorithms is applied to further construct the calibration model.By detailed analysis of the model performance,we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.

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.

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.

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.

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.

Rapid online analysis of trace elements in steel using a mobile fiber-optic laser-induced breakdown spectroscopy system

A mobile fiber-optic laser-induced breakdown spectrometer(FO-LIBS) prototype was developed to rapidly detect a large quantity of steel material online and quantitatively analyze the trace elements in a large-diameter steel tube.Twenty-four standard samples and a polynomial fitting method were used to establish calibration curve models.The R^2 factors of the calibration curves were all above 0.99,except for Cu,indicating the elements’ strong self-absorption effect.Five special steel materials were rapidly detected in the steel mill.The average absolute errors of Mn,Cr,Ni,V,Cu,and Mo in the special steel materials were 0.039,0.440,0.033,0.057,0.003,and0.07 wt%,respectively,and their average relative errors fluctuated from 2.9% to 15.7%.The results demonstrated that the performance of this mobile FO-LIBS prototype can be compared with that of most conventional LIBS systems,but the more robust and flexible characteristics of the FO-LIBS prototype provide a feasible approach for promoting LIBS from the laboratory to the industry.

Elemental and proximate analysis of coal by x-ray fluorescence assisted laser-induced breakdown spectroscopy

Although laser-induced breakdown spectroscopy(LIBS),as a fast on-line analysis technology,has great potential and competitiveness in the analysis of chemical composition and proximate analysis results of coal in thermal power plants,the measurement repeatability of LIBS needs to be further improved due to the difficulty in controlling the stability of the generated plasmas at present.In this paper,we propose a novel x-ray fluorescence(XRF) assisted LIBS method for high repeatability analysis of coal quality,which not only inherits the ability of LIBS to directly analyze organic elements such as C and H in coal,but also uses XRF to make up for the lack of stability of LIBS in determining other inorganic ash-forming elements.With the combination of elemental lines in LIBS and XRF spectra,the principal component analysis and the partial least squares are used to establish the prediction model and perform multi-elemental and proximate analysis of coal.Quantitative analysis results show that the relative standard deviation(RSD) of C is 0.15%,the RSDs of other elements are less than 4%,and the standard deviations of calorific value,ash content,sulfur content and volatile matter are 0.11 MJ kg,0.17%,0.79% and 0.41%respectively,indicating that the method has good repeatability in determination of coal quality.This work is helpful to accelerate the development of LIBS in the field of rapid measurement of coal entering the power plant and on-line monitoring of coal entering the furnace.

Improvement in classification accuracy of stainless steel alloys by laser-induced breakdown spectroscopy based on elemental intensity ratio analysis

Laser-induced breakdown spectroscopy(LIBS) is a useful technique for accurate sorting of metal scrap by chemical composition analysis.In this work,a method for intensity-ratiobased LIBS classification of stainless steel applicable to highly fluctuating LIBS signal conditions is proposed.The spectral line pairs for intensity ratio calculation are selected according to elemental concentration and upper levels of emission lines.It is demonstrated that the classification accuracy can be significantly improved from that of full-spectra principal component analysis or intensity-based analysis.The proposed method is considered to be suited to an industrial scrap sorting system that requires minimal maintenance and low system price.

A comparison of single shot nanosecond and femtosecond polarization-resolved laser-induced breakdown spectroscopy of Al

Aluminum samples have been analyzed by femtosecond polarization-resolved laser-induced breakdown spectroscopy （fs-PRLIBS）. We compare the obtained spectra with those obtained from nanosecond PRLIBS （ns-PRLIBS）. The main specific features of fs-PRLIBS are that a lower plasma temperature leads to a low level of continuum and no species are detected from the ambient gas. Furthermore, signals obtained by fs-PRLIBS show a higher stability than those of ns-PRLIBS. However, more elements are detected in the ns-PRLIBS spectra.

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.

Determination of Iron in Water Solution by Time-Resolved Femtosecond Laser-Induced Breakdown Spectroscopy

The influence of the energy of femtosecond laser pulses on the intensity of Fe I （371.99 nm） emission line and the continuous spectrum of the plasma generated on the surface of Fe^3＋ water solution by a Ti： sapphire laser radiation with pulse duration 〈45 fs and energies up to 7 mJ is determined. A calibration curve was obtained for Fe3＋ concentration range from 0.5 g/L to the limit of detection in water solution, and its saturation was detected for concentrations above 0.25 g/L, which is ascribed to self-absorption. The 3σ- limit of detection obtained for Fe in water solution is 2.6 mg/L in the case of 7 mJ laser pulse energy. It is found that an increase of laser pulse energy insignificantly affects on LOD in the time-resolved LIBS and leads to a slight improvement of the limit of detection.

A Rising Force for the World-Wide Development of Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy （LIBS） has attracted many academic and industrial interests world-wide due to its unique advantages, such as little or no sample preparation requirement, in-situ/online and multi-elemental analysis, and remote sensing etc., and it has been regarded as a ＂future super star＂ for chemical analysis for many years . In China,

Improved Measurement Performance of Inorganic Elements in Coal by Laser-Induced Breakdown Spectroscopy Coupled with Internal Standardization

Laser-induced breakdown spectroscopy was employed to determine the inorganic elements in coal. To improve the measurement＇s accuracy and precision, a new internal standardization scheme, which we named changed internal standardization, was compared with the traditional internal standardization and no internal standardization for the analysis of inorganic elements. The new internal standardization scheme used the atomic line of carbon at 247.86 nm and the molecular band of CN at 388.34 nm and C2 at 516.32 nm to normalize the lines of inorganic elements that were distributed in the same spectral channel. The performance of the utilization of the new internal standardization scheme was evaluated using a set of coal samples, including twenty calibration samples and five validation samples. The results show that the coefficients of determination R2 and the slope of calibration models coupled with changed internal standard- ization are better than that of the calibration models coupled with fixed internal standardization and no internal standardization. Moreover, the measurement accuracy and reproducibility of changed internal standardization for the analysis of five validation samples also yielded further improvement. The results that we obtained suggest that changed internal standardization could compensate for the matrix effects, as well as the influence of the difference in the spectral response of the light collection system.

Monitoring the Heavy Element of Cr in Agricultural Soils Using a Mobile Laser-Induced Breakdown Spectroscopy System with Support Vector Machine

Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the analysis speed and accuracy, two calibration models are built with the support vector machine method： one considering the whole spectra and the other based on the segmental spectra input. Considering the results of the multiple linear regression analysis, three segmental spectra are chosen as the input variables of the support vector regression （SVR） model. Compared with the results of the SVR model with the whole spectra input, the relative standard error of prediction is reduced from 3.18% to 2.61% and the running time is saved due to the decrease in the number of input variables, showing the robustness in rapid soil analysis without the concentration gradient samples.

Experimental study on mercury content in flue gas of coal-fired units based on laser-induced breakdown spectroscopy

Accurate measurement of trace heavy metal mercury(Hg) in flue gas of coal-fired units is great significance for ecological and environmental protection.Mixed gas was used to simulate the actual flue gas of a power plant in this study.A laser-induced breakdown spectroscopy(LIBS)system for Hg measurement in mixed gas was built to study the effect of mixed gas pressure,Hg concentration in mixed gas and delay time on Hg measurement.The experimental results show that the appropriate low mixed gas pressure can obtain high Hg signal intensity and signal to noise ratio.The Hg signal intensity and signal to noise ratio increased with the increase of Hg concentration in mixed gas.The Hg signal intensity and signal to noise ratio decreased with the increase in delay time.According to the above results,the optimized measurement conditions can be determined.Different Hg concentrations in mixed gas were quantitatively analyzed by the internal standard method and traditional calibration method respectively.The relative error of prediction of the test sample obtained by the internal standard method was within 11.11%.The relative error of prediction of the traditional calibration method was less than 14.54%.This proved that the internal standard method can improve the accuracy of quantitative analysis of Hg concentration in flue gas using LIBS.

Development of a Laboratory Cement Quality Analysis Apparatus Based on Laser-Induced Breakdown Spectroscopy

Determination of the chemical composition of cement and ratio values of clinker plays an important role in cement plants as part of the optimal process control and product quality evaluation. In the present paper, a laboratory laser-induced breakdown spectroscopy （LIBS） apparatus mainly comprising a sealed optical module and an analysis chamber has been designed for possible application in cement plants for on-site quality analysis of cement. Emphasis is placed on the structure and operation of the LIBS apparatus, the sealed optical path, the temperature controlled spectrometer, the sample holder, the proper calibration model established for minimizing the matrix effects, and a correction method proposed for overcoming the ＇drift＇ obstacle. Good agreement has been found between the laboratory measurement results from the LIBS method and those from the traditional method. The absolute measurement errors presented here for oxides analysis are within 0.5%, while those of ratio values are in the range of 0.02 to 0.05. According to the obtained results, this laboratory LIBS apparatus is capable of performing reliable and accurate, composition and proximate analysis of cement and is suitable for application in cement plants.

Selection of Spectral Data for Classification of Steels Using Laser-Induced Breakdown Spectroscopy

Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.