基于时间序列单维卷积神经网络的水泥熟料游离钙软测量方法

doi:10.3969/j.issn.1000-1158.2020.09.19

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Abstract The content of free calcium oxide (fCaO) in cement clinker has an important impact on cement quality and production energy consumption.At this stage, the fCaO content of cement clinker is measured offline by chemical analysis method, but this method has obvious hysteresis for the operation guidance of the production process.Aiming at the problem that clinker fCaO could not be monitored online, a soft measurement modeling method of clinker fCaO based on multi-variable time series single-dimensional convolutional neural network (TS-CNN) was proposed.The time series of a certain historical time period that affects multiple variables of clinker fCaO was used as the models input, and the cement data was combined to extract the characteristics of each variable by using single-dimensional convolution pooling method to reduce the complexity of the network, and finally the extracted local information was integrated by the fully connected layer.Through the experimental comparison, the results show that the soft measurement method based on TS-CNN has higher prediction accuracy and more generalization ability.

Key words： metrology cement clinker fCaO single-dimensional convolutional neural network time series soft-sensing model

Received: 30 August 2018 Published: 28 August 2020

PACS:

TB99

Fund:Foundation of Hebei Educational Committee.

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	Articles by authors
	ZHAO Yan-tao
	HE Yong-qiang
	JIA Li-ying
	YANG Li-ming
	HAO Xiao-chen

Cite this article:

ZHAO Yan-tao,HE Yong-qiang,JIA Li-ying, et al. Soft Measurement Method for Cement Clinker fCaO Based on Time Series Single-dimensional Convolutional Neural Network[J]. Acta Metrologica Sinica, 2020, 41(9): 1152-1162.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.09.19 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I9/1152

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