On-line determination of the ash content of coal using a “Siroash” gauge based on the transmission of low and high energy γ-rays

Fookes, R.A.; Gravitis, V.L.; Watt, J.S.; Hartley, P.E.; Campbell, Carelyn E.; Howells, E.; McLennan, T.D.; Millen, M.J.

doi:10.1016/0020-708x(83)90117-5

Cited by 19 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the end of the last century, with the rapid development of civilian nuclear technology and computer technology, many rapid ash measurement techniques based on radiation methods have been proposed [2][3][4]. Radiation measurement methods utilize the attenuation pattern of the rays within coal to measure the content of high atomic number elements in coal, thereby estimating the ash content.…”

Section: Radiation Methodsmentioning

confidence: 99%

A Rapid Detection Method for Coal Ash Content in Tailings Suspension Based on Absorption Spectra and Deep Feature Extraction

Zhu,

Zhang,

Zhu

et al. 2024

Mathematics

View full text Add to dashboard Cite

Traditional visual detection methods that employ image data are often unstable due to environmental influences like lighting conditions. However, microfiber spectrometers are capable of capturing the specific wavelength characteristics of tail coal suspensions, effectively circumventing the instability caused by lighting variations. Utilizing spectral analysis techniques for detecting ash content in tail coal appears promising as a more stable method of indirect ash detection. In this context, this paper proposes a rapid detection method for the coal ash content in tailings suspensions based on absorption spectra and deep feature extraction. Initially, a preprocessing method, the inverse time weight function (ITWF), is presented, focusing on the intrinsic connection between the sedimentation phenomena of samples. This enables the model to learn and retain spectral time memory features, thereby enhancing its analytical capabilities. To better capture the spectral characteristics of tail coal suspensions, we designed the DSFN (DeepSpectraFusionNet) model. This model has an MSCR (multi-scale convolutional residual) module, addressing the conventional models’ oversight of the strong correlation between adjacent wavelengths in the spectrum. This facilitates the extraction of relative positional information. Additionally, to uncover potential temporal relationships in sedimentation, we propose a CLSM-CS (convolutional long-short memory with candidate states) module, designed to strengthen the capturing of local information and sequential memory. Ultimately, the method employs a fused convolutional deep classifier to integrate and reconstruct both temporal memory and positional features. This results in a model that effectively correlates the ash content of suspensions with their absorption spectral characteristics. Experimental results confirmed that the proposed model achieved an accuracy of 80.65%, an F1-score of 80.45%, a precision of 83.43%, and a recall of 80.65%. These results outperformed recent coal recognition models and classical temporal models, meeting the high standards required for industrial on-site ash detection tasks.

show abstract

Section: Radiation Methodsmentioning

confidence: 99%

A Rapid Detection Method for Coal Ash Content in Tailings Suspension Based on Absorption Spectra and Deep Feature Extraction

Zhu,

Zhang,

Zhu

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…The existing intelligent gangue separation systems mainly rely on ray recognition and machine vision recognition [1][2][3][4][5]. The dual-energy ray recognition has long been adopted for coal preparation.…”

Section: Figure 1 Manual Separation Of Ganguesmentioning

confidence: 99%

Visual Positioning and Recognition of Gangues Based on Scratch Feature Detection

Zou¹,

Ren²

2019

View full text Add to dashboard Cite

The coals and gangues in a raw coal image have similar visual features, due to the presence of coal ash on the surface. Thus, it is difficult to locate and recognize coals and gangues on the transmission line through visual recognition. To solve the problem, this paper proposes a visual positioning and recognition method for gangues based on scratch feature detection. Firstly, an image acquisition system was designed to capture the clear and suitable images. Next, scratched features were prepared on gangue surface with mechanical tools, laying the basis for visual positioning and recognition. Afterwards, the texture feature recognition method based on grey-level co-occurrence matrix (GLCM) was adopted to identify coal and scratched gangue blocks. The test results show that the GLCM correlation feature parameter is effective for scratch recognition. The parameter and the said method were proved effective through experiments.

show abstract

“…Although conventional spectral analysis might permit the subtraction of background peaks, it is inappropriate for on-line measurement for complicated calculations. To correct for this contribution, equation ( 4) is replaced by the following best fit [10]:…”

Section: The Measurement Equationmentioning

confidence: 99%

A novel automated separator based on dual energy gamma-rays transmission

Xu¹,

Zhang²

2000

Meas. Sci. Technol.

View full text Add to dashboard Cite

The paper describes a novel automated separator, of which the key point is the determination of ash in coal by a technique based on the attenuation of dual energy γ-rays transmission. The technique is often used to determine the concentration of a higher atomic number (Z) component in a lower Z matrix. We designed a novel apparatus to realize the on-line non-contact measurement of ash in the virgin lump coal during the process of conveying. As a result, the gangues in the virgin lump coal are recognized by using a threshold ash method and removed by the pneumatic actuator. In this paper the principle of this measurement and the arrangement for separation are presented. Both the main factors affecting the dynamic determination and methods for reducing errors in analysis are also proposed. Furthermore, the experimental verification of the analysis has been conducted. The results are in good agreement with the theoretical analysis.

show abstract

On-line determination of the ash content of coal using a “Siroash” gauge based on the transmission of low and high energy γ-rays

Cited by 19 publications

References 2 publications

A Rapid Detection Method for Coal Ash Content in Tailings Suspension Based on Absorption Spectra and Deep Feature Extraction

A Rapid Detection Method for Coal Ash Content in Tailings Suspension Based on Absorption Spectra and Deep Feature Extraction

Visual Positioning and Recognition of Gangues Based on Scratch Feature Detection

A novel automated separator based on dual energy gamma-rays transmission

Contact Info

Product

Resources

About