An End-to-End Multisource Information Fusion Framework for f-CaO Content Soft Sensing in Cement Clinker Burning Process
Author:
Affiliation:
1. State Key Laboratory of Industrial Control Technology (SKLICT), College of Control Science and Engineering, Zhejiang University, Hangzhou, China
2. Alibaba Cloud, Alibaba Group, Hangzhou, China
Funder
Alibaba-Zhejiang University Joint Institute of Frontier Technologies
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10216937.pdf?arnumber=10216937
Reference33 articles.
1. A Survey on Deep Learning for Data-Driven Soft Sensors
2. Supervised Nonlinear Dynamic System for Soft Sensor Application Aided by Variational Auto-Encoder
3. SSP-WGAN-Based Data Enhancement and Prediction Method for Cement Clinker f-CaO
4. R-WGAN-based Multi-timescale Enhancement Method for Predicting f-CaO Cement Clinker
5. The passage of granular solids through inclined rotary kilns
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