Comparative study of regression modeling methods for online coal calorific value prediction from flame radiation features

Xu, Lijun; Cheng, Yanting; Yin, Rui; Zhang, Qi

doi:10.1016/j.fuel.2014.10.081

Cited by 19 publications

(6 citation statements)

References 28 publications

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“…The correlation of determination (R-2) for models was 0.99. Several statistical approaches solved the online coal calorific value prediction based on the flame radiation features in linear and nonlinear regression analyses [24]. The partial least squares analysis-based nonlinear regression model showed the best performance for coal calorific value prediction.…”

Section: Introductionmentioning

confidence: 99%

Regression Models Utilization to the Underground Temperature Determination at Coal Energy Conversion

Durdán¹,

Benková²,

Laciak³

et al. 2021

Energies

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The underground coal gasification represents a technology capable of obtaining synthetic coal gas from hard-reached coal deposits and coal beds with tectonic faults. This technology is also less expensive than conventional coal mining. The cavity is formed in the coal seam by converting coal to synthetic gas during the underground coal gasification process. The cavity growth rate and the gasification queue’s moving velocity are affected by controllable variables, i.e., the operation pressure, the gasification agent, and the laboratory coal seam geometry. These variables can be continuously measured by standard measuring devices and techniques as opposed to the underground temperature. This paper researches the possibility of the regression models utilization for temperature data prediction for this reason. Several regression models were proposed that were differed in their structures, i.e., the number and type of selected controllable variables as independent variables. The goal was to find such a regression model structure, where the underground temperature is predicted with the greatest possible accuracy. The regression model structures’ proposal was realized on data obtained from two laboratory measurements realized in the ex situ reactor. The obtained temperature data can be used for visualization of the cavity growth in the gasified coal seam.

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Section: Introductionmentioning

confidence: 99%

Regression Models Utilization to the Underground Temperature Determination at Coal Energy Conversion

Durdán¹,

Benková²,

Laciak³

et al. 2021

Energies

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“…Liu et al explored the variation law of furnace core temperature by analyzing the existing state of dead material column, 10,11) Zhou et al analysis the activity of blast furnace hearth by monitoring flame temperature of raceway zone. [12][13][14] But these laws are based on the theoretical basis and a good quantitative model is not given, so the hearth activity cannot be intuitively characterized. Chen developed a new index based on the flow resistance coefficient of slag and hot metal to estimate hearth activity, Upadhyay et al develop a mathematical model to simulate the variation in hot metal/slag accumulation and temperature during the taping of the blast furnace based on the heat transfer between metal and slag, metal and solids et al [15][16][17] But the new index is based on the judgment of the flow resistance coefficient of the slag-iron, and this mathematical model can only reflect the temperature variation of the hot metal, so they cannot accurately reflect the hearth activity, and the description of the hearth activity state is vague.…”

Section: Introductionmentioning

confidence: 99%

Prediction Method of Core Dead Stock Column Temperature Based on PCA and Ridge Regression

Wang

Zhang

et al. 2021

ISIJ Int.

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The change of core temperature of blast furnace reflects the working status of hearth. However, the temperature of core dead stock column can not be measured by sensors directly. Therefore, a prediction model of Core Dead Stock Column Temperature is proposed in this work based on primary component analysis (PCA) and ridge regression algorithms, where PCA and person correlation coefficient are used for feature extraction and ridge regression is employed to solve multi-collinearity problems. Based on an inhouse dataset collected within a successive three months, experimental results show that the R-squared of model on the training data set can achieve 88% and the average relative error on the test data set is only 0.33%, which shows the effectiveness of the proposed model.

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“…Therefore, the digital imaging system based on the color CCD (Charge Coupled Device) is one of the effective methods applying in the temperature measurement field. [16][17][18] The characteristic parameters of the flame radiations could be obtained from images which are captured by a vision-based radiation monitoring system. Then the temperature distribution of radiation images could be calculated.…”

Section: Introductionmentioning

confidence: 99%

Uniformity and Activity of Blast Furnace Hearth by Monitoring Flame Temperature of Raceway Zone

et al. 2017

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Comparative study of regression modeling methods for online coal calorific value prediction from flame radiation features

Cited by 19 publications

References 28 publications

Regression Models Utilization to the Underground Temperature Determination at Coal Energy Conversion

Regression Models Utilization to the Underground Temperature Determination at Coal Energy Conversion

Prediction Method of Core Dead Stock Column Temperature Based on PCA and Ridge Regression

Uniformity and Activity of Blast Furnace Hearth by Monitoring Flame Temperature of Raceway Zone

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