Application of spectrometric nuclear borehole logging for reserves estimation and mine planning at Callide coalfields open-cut mine

Charbucinski, J.; Nichols, Wes

doi:10.1016/s0306-2619(02)00191-5

Cited by 16 publications

(4 citation statements)

References 2 publications

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“…As illustrated above, geophysical borehole logging measurements can be used for coal seam identification and correlation and geotechnical rock mass characterisation. They can also be used for specific coal-quality assessment [6,7,[34][35][36][37][38][39]. Zhou and Esterle [37] also investigated ways to improve the coal density and quality estimation from geophysical logs.…”

Section: Coal Quality Estimation From Geophysical Logsmentioning

confidence: 99%

Applications of Geophysical Logs to Coal Mining—Some Illustrative Examples

Zhou

Guo

2020

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Geophysical logs can be used not only for qualitative interpretation such as strata correlation but also for geotechnical assessment through quantitative data analysis. In an emerging digital mining age, such a use of geophysical logs helps to establish reliable geological and geotechnical models, which reduces safety and financial risks due to geological and geotechnical uncertainty for new and existing coal mining projects. This paper presents some examples of geological and geotechnical characterizations from geophysical logs at various coal mines in Australia and India. The applications include rock strength and coal quality estimations, automated lithological/geotechnical interpretation and geophysical strata rating, all based on geophysical logs. These derived parameters could provide input to modelling, control, even ‘digital twin’ generation in a form of geological and geotechnical models as part of the future digital mining. The outcomes can be visualized in 3D space and used for identifying the key geotechnical strata units that are responsible for caving behaviors during longwall mining. This could assist site geologists and planning and production engineers predict and manage mining conditions on an ongoing basis. Both conventional logs such as density, natural gamma and sonic and less common logging data, such as full waveform sonic, televiewer and SIROLOG spectrometric natural gamma logging data are examined for their potential applications. The geotechnical strata classification and rock strengths predicted from the geophysical logs match the laboratory tests, drill core geotechnical strata classification, core photos and the mining condition/behavior observed. These illustrate the usefulness and effectiveness of using geophysical logs for geological and geotechnical characterizations.

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Section: Coal Quality Estimation From Geophysical Logsmentioning

confidence: 99%

Applications of Geophysical Logs to Coal Mining—Some Illustrative Examples

Zhou

Guo

2020

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“…Common rapid detection techniques include instantaneous gamma neutron activation analysis, X-ray fluorescence technology, inductively coupled plasma emission spectrometry, and so on. However, these techniques have a series of intractable problems, such as radiation hazards [1], limited analytical range [2], and large amount of argon gas consumption [3]. In this context, laser-induced breakdown spectroscopy (LIBS) technology emerged as an emerging rapid detection technology, which has significant advantages such as real-time detection and no need for sample pretreatment, etc.…”

Section: Introductionmentioning

confidence: 99%

Application of Semi-Supervised Learning Model to Coal Sample Classification

Wang,

Xu,

Gao

et al. 2024

Applied Sciences

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As an extremely important energy source, improving the efficiency and accuracy of coal classification is important for industrial production and pollution reduction. Laser-induced breakdown spectroscopy (LIBS) is a new technology for coal classification which has the ability to rapidly analyze coal compared with traditional coal analysis methods. In the practical application of LIBS, a large amount of labeling data is usually required, but it is quite difficult to obtain labeling data in industrial sites. In this paper, to address the problem of insufficient labeled data, a semi-supervised classification model (SGAN) based on adversarial neural network is proposed, which can utilize unlabeled data to improve the classification accuracy. The effects of labeled and unlabeled samples on the classification accuracy of the SGAN model are investigated, and the results show that the number of labeled and unlabeled samples are positively correlated, and the highest average classification accuracy that the model can achieve is 98.5%. In addition, the classification accuracies of SGAN and other models (e.g., CNN, RF) are also compared, and the results show that, with the same number of labeled samples in the three models, SGAN performs better after the number of unlabeled samples reaches a certain level, with an improvement of 0.7% and 2.5% compared to the CNN and RF models, respectively. This study provides new ideas for the application of semi-supervised learning in LIBS.

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“…In fact, the "smart boiler" can achieve real-time optimization according to the coal information; thus, a rapid detection method is urged to be applied in coal-fired power plants for the improvement of combustion efficiency and pollution reduction. Although there are some existing rapid detection methods for coal quality, these methods have some limitations for the application to remove measurement due to high installation and maintenance costs and strict safety supervision, for example, prompt gamma-ray neutron activation analysis (PGNAA) has potential radiation hazards [1]; X-ray fluorescence (XRF) cannot analyze low atomic number elements, such as C and H [2]; and inductively coupled plasma emission spectrometer (ICP-OES) needs to consume a large amount of argon [3]. However, LIBS has great advantages in the application of the rapid detection of coal with simple sample preparation and multi-element simultaneous measurement [4].…”

Section: Introductionmentioning

confidence: 99%

Rapid Classification and Quantification of Coal by Using Laser-Induced Breakdown Spectroscopy and Machine Learning

Zheng¹,

Lu²,

Chen

et al. 2023

Applied Sciences

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Coal is expected to be an important energy resource for some developing countries in the coming decades; thus, the rapid classification and qualification of coal quality has an important impact on the improvement in industrial production and the reduction in pollution emissions. The traditional methods for the proximate analysis of coal are time consuming and labor intensive, whose results will lag in the combustion condition of coal-fired boilers. However, laser-induced breakdown spectroscopy (LIBS) assisted with machine learning can meet the requirements of rapid detection and multi-element analysis of coal quality. In this work, 100 coal samples from 11 origins were divided into training, test, and prediction sets, and some clustering models, classification models, and regression models were established for the performance analysis in different application scenarios. Among them, clustering models can cluster coal samples into several clusterings only by coal spectra; classification models can classify coal with labels into different categories; and the regression model can give quantitative prediction results for proximate analysis indicators. Cross-validation was used to evaluate the model performance, which helped to select the optimal parameters for each model. The results showed that K-means clustering could effectively divide coal samples into four clusters that were similar within the class but different between classes; naive Bayesian classification can distinguish coal samples into different origins according to the probability distribution function, and its prediction accuracy could reach 0.967; and partial least squares regression can reduce the influence of multivariate collinearity on prediction, whose root mean square error of prediction for ash, volatile matter, and fixed carbon are 1.012%, 0.878%, and 1.409%, respectively. In this work, the built model provided a reference for the selection of machine learning methods for LIBS when applied to classification and qualification.

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Application of spectrometric nuclear borehole logging for reserves estimation and mine planning at Callide coalfields open-cut mine

Cited by 16 publications

References 2 publications

Applications of Geophysical Logs to Coal Mining—Some Illustrative Examples

Applications of Geophysical Logs to Coal Mining—Some Illustrative Examples

Application of Semi-Supervised Learning Model to Coal Sample Classification

Rapid Classification and Quantification of Coal by Using Laser-Induced Breakdown Spectroscopy and Machine Learning

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