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−1, 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.

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

AI in apiculture: A novel framework for recognition of invasive insects under unconstrained flying conditions for smart beehives

Rapid and stable analysis of coal quality for fine management of coal is essential for the clean and efficient utilization of coal in thermal power plants. In this work, a...

Development and industrial application of LIBS-XRF coal quality analyzer by combining PCA and PLS regression methods

Accurate determination of ash content in coal is paramount for quality control in the coal industry. However, conventional methods are time-consuming and ill-suited for real-time process control. To address this challenge, our study proposed a novel approach, GFNet, which integrated graph convolution and a feedforward network for ash content estimation. The image was divided into patches and transformed into a graph structure using a k-nearest Neighbor (k-NN) function. We introduced relative position encoding to each node feature to enhance feature representation. The GFNet architecture consisted of a stack of GF (graph convolution and FFN), facilitating feature extraction from the graph representation of image patches. Graph convolution captured intricate spatial relationships and interdependencies among patches, enabling effective information aggregation and updating within the graph. The FFN module, featuring two linear layers, facilitated node feature transformation. We developed pyramid (PGFNet) and isotropic (IGFNet) architectures by stacking GF blocks. Extensive experiments substantiated the superior performance of the proposed approach compared to traditional methods, emphasizing accuracy and efficiency. The GFNet achieved remarkable results, with PGFNet-S attaining a 96.36 accuracy and a mean absolute error (MAE) of 0.145%. IGFNet-Ti demonstrated a higher accuracy of 98.30% with an MAE of 0.093%. Visualization of the graph structure elucidated the model's proficiency in processing graph structure and node features, illustrating its capability to capture content-relevant nodes and gradually connect nodes with similar semantics. This pioneering work presented a transformative approach to ash content estimation in coal quality analysis, offering an invaluable tool for the coal industry's quality control needs. 

GFNet: A pioneering approach for precisely estimating ash content in coal through the fusion of graph convolution and feedforward network

Ash content, as a crucial indicator of coal quality, its rapid and accurate determination is pivotal to improve the energy utilization of coal and reduce environmental pollution. Traditional spectroscopic methods face significant challenges in acquiring accurate information from coal samples due to the notorious matrix effects arising from their complex composition, vast molecular structure, and diverse coal types. In this study, the feasibility of total reflection X-ray fluorescence (TXRF) combined with partial least squares (PLS) for the determination of coal ash was firstly investigated based on the TXRF being unaffected by matrix effects. Firstly, coal samples were prepared as suspensions, and the effects of sample particle size and different dispersants on the results of TXRF analyses were evaluated. The accuracy and applicability of the chosen sample preparation strategies were further validated using inductively coupled plasma mass spectrometry (ICP-MS) and two certified reference materials (CRMs). Subsequently, based on the analysis of 19 coal samples, the impact of three different predictive variables on the performance of the PLS model was investigated: (a) TXRF full spectrum normalized by the net intensity of the internal standard; (b) net intensity of characteristic peaks for 12 elements (Al, Si, K, Ca, Ti, Fe, Cr, Mn, Cu, Ni, and Sr) normalized by the net intensity of the internal standard; (c) concentrations of the aforementioned 12 elements. The results demonstrate that the PLS model constructed usingthe TXRF full spectrum normalized by the net intensity of the internal standard exhibits the best predictive capabilities, with the determination coefficient of calibration set (R 

Exploring the analytical potential of total reflection X-ray fluorescence (TXRF) combined with partial least square (PLS) for simple determination of ash content in various coal types

The combination of laser-induced breakdown spectroscopy and energy dispersive X-ray fluorescence spectroscopy in the coal quality analysis was reported formerly. But in the practical test of the prototype instrument in the real power plant, the X-ray fluorescence signals suffered from intensity fluctuations over long-time measurements. The long-term signal fluctuations cause lower efficiency on the establishment of the calibration model and relatively larger root-mean-squared error of prediction (RMSEP) for unknown samples. Therefore, the spectral intensity correction was performed in the measurements; a randomly selected sample was measured several times in the whole measurements, including the modeling samples and unknown samples, recording the signal fluctuations and searching for a set of factors suitable for the intensity correction of a full-spectrum–based partial least square calibration model. In addition, as the signals of the coal samples of the power plant showed the potential of classification, the piecewise models were also established in case of further enhancement of the model or prediction accuracy. The RMSEPs of the calorific value, ash, volatile, and sulfur were lowered from 0.68 MJ/kg, 1.62%, 0.32%, and 0.24% to 0.51 MJ/kg, 1.34%, 0.16%, and 0.14% after spectral intensity correction, respectively. The piecewise modeling with spectral intensity correction achieved similar RMSEP for volatile and sulfur prediction but with more accurate models. The spectral intensity correction showed the ability to reduce the long-term signal fluctuation, and piecewise modeling also showed more efficiency in the model establishments for volatile and ash determination.

/pdf/accuracy-enhancement-of-libs-xrf-coal-quality-analysis-3vy8q1ap.pdf

Accuracy Enhancement of LIBS-XRF Coal Quality Analysis Through Spectral Intensity Correction and Piecewise Modeling

Recently, Vespa mandarinia have been found frequently in Washington state, causing severe potential damage to the local ecosystem. To establish effective pest management programs, Washington State collected reports of people witnessing these wasps. However, due to the existence of a large number of wrong reports and the limited resources of government agencies, it is not possible to conduct field visits to all reports. In recognition of report images, since the image data set is extremely unbalanced, we use data enhancement, cutting and rotating the positive images to increase the number of positive images. In addition, due to the fact that the traditional neural network is easy to overfit in this kind of imbalanced data set, we use the One-Class SVM model to transform the classification problem into the outlier test problem and the experimental results show that the accuracy of our algorithm is 98%.

/pdf/research-on-false-eyewitness-detection-algorithm-of-asian-4c3omdn6jb.pdf

Research on false eyewitness Detection algorithm of Asian giant hornet Image based on support Vector Machine

Multi-Label Learning Through Label-Specific Features with Entropy Guided Clustering

Tone is one of the biggest differences between Mandarin as a tonal language and English, meaning that changes in frequency will appear in every character and determine their meaning. Therefore, the accurate identification of intonation plays an important role in Chinese language processing and learning. In the learning and training process of Mandarin Chinese, graphical feedback can provide valuable assistance to linguists or learners, providing them with more insight than simple accuracy ratings as they can determined what to change to improve their intonation based on this feedback. In this paper, we refer to the process of generating Mel-frequency cepstrum coefficients and employ a machine learning-aided time-slot filtering approach to obtain an intuitive transformation of pitch from Mel spectrum to geometric graphs as feedback to Chinese language workers and learners, and to help them improve their mastery of Mandarin tones. Our preliminary results show that these illustrations can accurately represent frequency changes in relation to time. At the same time, the lower computational cost of the optimization allows it to be used as learning software on various mobile platforms.

Machine Learning-based Time-slot Time-varying Filtering for Mandarin Tone Processing

Multi-Label Learning Through Label-Specific Features with Entropy Definition

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jiaxu Li

Author Tools

Chat about Author