Deep learning-based soft-sensing method for operation optimization of coke dry quenching process

In the modern industrial process control, the development of distributed control system (DCS) makes the application of data-driven soft-sensing methods available. Deep learning (DL), as a novel training strategy of deep neural networks, has large potential for soft sensor modeling. In comparison wit...

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Bibliographic Details
Main Authors: Wang, Jian-Guo, Zhao, Jing-Hui, Shen, Tiao, Ma, Shi-Wei, Yao, Yuan, Chen, Tao, Shen, Bing, Wu, Yi-Ping
Format: Conference Proceeding
Language:English
Subjects:
Biological neural networks
Boilers
Coke
coke dry quenching (CDQ)
deep learning (DL)
Drying
Economics
Machine learning
Modelling
Neural networks
Optimization
Poles and towers
Quenching
Soft sensor
Training
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Summary:In the modern industrial process control, the development of distributed control system (DCS) makes the application of data-driven soft-sensing methods available. Deep learning (DL), as a novel training strategy of deep neural networks, has large potential for soft sensor modeling. In comparison with shallow neural network, because DL can make full use of massive process by greedy layer-wise training approach, deep structure of neural network has better representation and generalization ability. Compared with the traditional coke wet quenching, coke dry quenching (CDQ) has the advantage of waste heat recovery, which is advanced, energy saving and environmentally friendly, and is the main coke quenching method adopted in iron and steel plant. A deep learning-based soft-sensing method for operation optimization of coke dry quenching process is put forward in this paper. By defining the economic efficiency, the data with high economic efficiency is used for modeling and optimizing the CDQ operating variable, i.e. supplementary air flow rate (F SA ). The experimental results show that, adopting the adjusted optimal operation, a remarkable raise (1.58%) of economic efficiency can be acquired on average. Thus, the presented deep learning-based soft-sensing method for operation optimization is effective and feasible for improving the waste heat recovery in CDQ system.
ISSN:2161-2927
1934-1768
DOI:10.1109/ChiCC.2016.7554805