Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

Abstract

Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems.

Classification and inspection of milling surface roughness based on a broad learning system

“…The external optimization search mainly includes the number of feature nodes 𝑁1 within each window of the mapping layer, number of mapping layer windows 𝑁2, number of enhancement nodes 𝑁3, regularization parameter 𝜆 and shrinkage parameter 𝑆. Among others, in the literature, [18], we find that the classification performance of the BLS depended greatly on 𝑁1, 𝑁2 and 𝑁3. Therefore, in order to construct a roughness measurement model with better performance, an orthogonal experiment [19] was used to analyze the effects of 𝑁1, 𝑁2, and 𝑁3 on the model performance.…”

A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions

“…Currently, various classification models have been developed using advanced machine learning techniques, including Convolutional Neural Network (CNN) 12 , 13 , Broad Learning System (BLS) 14 , and Bidirectional Long Short-Term Memory network (Bi-LSTM) 15 , as well as pre-trained Vision Transformer (ViT) 16 . Notably, Gaussian Support Vector Machine (SVM) 17 , Linear Discriminant Analysis (LDA) 18 , BLS 19 , Random Forest (RF) 20 , and Extreme Learning Machine (ELM) 21 have also been utilized to establish models for classifying coal types and provenance. Specifically, for the quantitative analysis of coal composition, researchers have employed methods such as XGBoost 22 and Partial Least Squares (PLS) 23 to construct regression models.…”

Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis

“…Zhang et al 9 combined the laser-induced breakdown spectra (LIBS) and independent component analysis-wavelet neural network for coal ash classification and achieved excellent performance, promoting the recovery and reuse of metallurgical waste. Lei et al 10 proposed a coal classification model combining generalized learning and the particle swarm optimization (PSO) algorithm, which overcomes the problems of data redundancy in the original spectral data and obtained 97.05% accuracy. Zhang et al 11 utilized the support vector machine (SVM) optimized by the genetic algorithm to categorize coal samples and utilized partial least-squares (PLS) regression to model each category of coal samples to obtain precise measurements of ash, volatile content, and calorific value.…”