Ash content prediction of coarse coal by image analysis and GA-SVM

Prominent regional differentiations of highway landslide disasters (HLDs) bring great difficulties in highway planning, designing and disaster mitigation, therefore, a comprehensive understanding of HLDs from the spatial perspective is a basis for reducing damages. Statistical prediction methods and machine learning methods have some defects in landslide susceptibility mapping (LSM), meanwhile, hybrid methods have been developed by combining the statistical prediction methods with machine learning methods in recent years, and some of them were reported to perform better than conventional methods. In view of this, the principal component analysis (PCA) method was used to extract the susceptibility evaluation indexes of HLDs; the particle swarm optimization-support vector machine (PSO-SVM) model and genetic algorithm-support vector machine (GA-SVM) model were implemented to the susceptibility mapping and zoning of HLDs in China. The research results show that the accumulative contribution rate of the four principal components is 92.050%; evaluation results of the PSO-SVM model are better than those of the GA-SVM model; micro dangerous areas, moderate dangerous areas, severe dangerous areas and extreme dangerous areas account for 24.24%, 19.49%, 36.53% and 19.74% of the total areas of China; among the 1543 disaster points in the HLDs inventory, there are 134, 182, 421 and 806 located in the above areas respectively.

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“…The processes of susceptibility evaluation of HLDs through the GA-SVM model are showed in Fig 5 [ 59 ].…”

Section: Susceptibility Evaluation Methodsmentioning

confidence: 99%

Susceptibility mapping and zoning of highway landslide disasters in China

Yin

Li²,

Che

et al. 2020

PLoS ONE

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“…Zhang and Yang showed different color and texture features (total thirty-eight features). Later, genetic algorithms (GA) and SVM were used to determine the most effective features for ash content prediction [13,17]. Thurley showed for limestone (a) a method using a laser scanner to determine the overlapped and non-overlapped fragments and (b) the particle size measurements with the difference in size ranges [9].…”

Section: Figure 1 Effect Of the Percentage Of -3 MM Fraction Inmentioning

confidence: 99%

Determination of surface moisture and particle size distribution of coal using online image processing

Choudhary¹,

Maloo²,

Parida³

et al. 2020

J Min & Metal A Mining

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Production of sponge iron requires iron ore, coal, and dolomite. The quality of sponge iron is affected by particle size variation and moisture content of the feed materials. In the present work, image processing was used to detect both particle size and moisture variation of the feed materials on an online basis. Noise and signal irregularities in images were removed by image analysis through MATLAB. Continuous (online, every 30 minutes) images were taken over a coal bed which was moving on a conveyor belt. It was a challenge to determine the particle size distribution and surface moisture of coal online. The distribution of reflectivity of coal in the image varied according to the moisture content and particle size. It affected the intensity information of the image which was then used to predict the surface moisture content of the coal. The method is now being used successfully in a processing plant.

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“…The linear terms, for example, d, c, quadratic terms such as d 2 , c 2 , and linear cross terms, such as dÂc, dÂr, as well as a constant term, were taken as candidates first, and a genetic algorithm (GA) [13,14] was adopted to select a optimized combination of them, considering both the accuracy and length of the models. The regression models to five decimal places are described in Equations 4 and 5: …”

Section: Application In the Taixi Coal Washerymentioning

confidence: 99%

Soft-Sensor Modeling for Separation Performance of Dense-Medium Cyclone by Field Data

Dou

Yang

Liu

et al. 2015

International Journal of Coal Preparation and Utilization

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Four measurable parameters, that is, density of dense-medium suspension (d), inlet pressure of dense-medium suspension (p), content of magnetic substance (c), and coal feed rate (r) were adopted to build a soft-sensor model for calculating the two performance index of a dense-medium cyclone in a Taixi plant. Uniform design was adopted to reduce the number of experiments. The models of actual separation density (d p ) and probable error (E p ) obtained by genetic algorithm and regression were proved to be basically right by the 12 training records and another test result. The accuracy of the d p model was 0.7% for the training set and 0.63% for the test data while that of the E p model was 9.41% and 13.54%, respectively. The behavior of the models were in accordance with field experiences, which showed that p had the most significant effect on E p and c affected d p most prominently in daily operation.

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Ash content prediction of coarse coal by image analysis and GA-SVM

Cited by 57 publications

References 16 publications

Susceptibility mapping and zoning of highway landslide disasters in China

Susceptibility mapping and zoning of highway landslide disasters in China

Determination of surface moisture and particle size distribution of coal using online image processing

Soft-Sensor Modeling for Separation Performance of Dense-Medium Cyclone by Field Data

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