Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature

doi:10.6046/zrzyyg.2020399

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Abstract

The iron and steel industry is a very important part in economic development. Obtaining the knowledge of the monthly production of steel companies is conducive to the macro control of the economy and the rational allocation of resources. In this paper, a monthly production estimation model for steel companies was proposed based on the grading results of the surface temperature obtained from the inversion of satellite thermal infrared data as well as the theory and method of landscape pattern indices. The surface temperature anomalous values and the thermal landscape distribution parameters of steel companies can be calculated according to the vector data of the spatial framework of steel companies. Based on this and the actual monthly production data of two typical steel companies in Central China and North China, the estimation model was established through the least-squares fitting, and the coefficient of determination (R²) of the model was greater than 0.9. According to the posterior variance test results, the accuracy of the estimation model proposed in this study is level 2. Meanwhile, the actual production values all fall within the 95% confidence interval of the estimation values. All these comprehensively reflect the monthly production model proposed in this paper are highly accurate.

Keywords thermal infrared remote sensing landscape pattern index steelmaking monthly production estimation model posterior variance test

ZTFLH:

TP79

Corresponding Authors: SONG Yan E-mail: liteya@cug.edu.cn;songyan@cug.edu.cn

Issue Date: 23 December 2021

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	Teya LI
	Yan SONG
	Xinli YU
	Yuanxiu ZHOU

Cite this article:

Teya LI,Yan SONG,Xinli YU, et al. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020399 OR https://www.gtzyyg.com/EN/Y2021/V33/I4/121

Fig.1 Schematic diagram of thermal radiation of steelmaking workshop

Fig.2 The modeling workflow of steelmaking monthly production estimation model

Tab.1 Correlation between parameters and actual monthly production

Tab.2 Grade of fitting accuracy

Tab.3 Isolated temperature region and four-neighborhood temperature

Fig.3 Boxplot analysis of surface isolated temperature region

时间	残差Y_j- $Y j 0$	残差百分比(Y_j- $Y j 0$ )/ Y_j×100%
2017年2月	-4.409	-3.50%
2017年10月	-1.889	-1.35%
2017年12月	-0.601	-0.45%
2018年7月	-4.372	-3.13%
2018年9月	-3.800	-2.76%
2018年10月	1.736	1.24%
2018年11月	1.554	1.12%
2020年2月	-7.575	-6.47%
均值	-2.419	-1.91%

Tab.4 The residual and residual percentage between actual values for validation and SMPE values

Fig.4 The curves of actual values for validation and SMPE values

参数	$y -$	$e -$	S₁	S₂	C	P
数值	134.080	-2.419	7.772	3.019	0.388	1

Tab.5 The SMPE precision verification parameters of enterprise A

Fig.5 Confidence interval of SMPE (Confidence level = 95%)

Fig.6 Boxplot analysis of surface isolated temperature region

时间	残差Y_j- $Y j 0$	残差百分比(Y_j- $Y j 0$ )/ Y_j×100%
2013年7月	3.163	4.52%
2013年8月	3.369	4.22%
2014年7月	0.770	1.01%
2016年5月	1.019	1.39%
2017年4月	-0.826	-1.06%
均值	1.499	2.02%

Tab.6 The residual and residual percentage between actual values for validation and SMPE values

Fig.7 The curves of actual values for validation and SMPE values

参数	$y -$	$e -$	S₁	S₂	C	P
均值	75.528	1.499	3.604	1.576	0.438	1

Tab.7 The SMPE precision verification parameters of enterprise B

Fig.8 Confidence interval of SMPE (Confidence level = 95%)

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