Dynamic prediction of cylinder wall temperature for drum dryer based on DGRU network

Abstract

Keywords:
• drum dryer,
• cylinder wall temperature,
• deep gated recurrent unit,
• dynamic prediction,
• mutual information

References

1. [1]

   邹泉,陈冉,赵云川,等.基于干燥脱水量的叶丝滚筒干燥加工强度品质校正技术[J].烟草科技,2020,53 (7):70-79.
   

2. [2]

   SUN Q Q,GE Z Q.A Survey on deep learning for data-driven soft sensors[J].IEEE Transactions on Industrial Informatics,2021,17 (9):5853-5866.

3. [3]

   ZHANG X G,ZHANG L,CHEN H,et al.Prediction of coal feeding during sintering in a rotary kiln based on statistical learning in the phase space[J].ISA Transactions,2018,83:248-260.

4. [4]

   ZHANG L,ZHANG X G,CHEN H,et al.A robust temperature prediction model of shuttle kiln based on ensemble random vector functional link network[J].Applied Thermal Engineering,2019,150:99-110.

5. [5]

   JIN L,LI S,HU B.RNN models for dynamic matrix inversion:A control-theoretical perspective[J].IEEE Transactions on Industrial Informatics,2018,14 (1):189-199.

6. [6]

   HWANG C L,JAN C.Recurrent-Neural-Network-Based multivariable adaptive control for a class of nonlinear dynamic systems with time-varying delay[J].IEEE Transactions on Neural Networks and Learning Systems,2016,27 (2):388-401.

7. [7]

   WANG J L,ZHANG J,WANG X X.Bilateral LSTM:A two-dimensional long short-term memory model with multiply memory units for short-term cycle time forecasting in re-entrant manufacturing systems[J].IEEE Transactions on Industrial Informatics,2018,14 (2):748-758.

8. [8]

   LI X C,DUAN F,LOUKOPOULOS P,et al.Canonical variable analysis and long short-term memory for fault diagnosis and performance estimation of a centrifugal compressor[J].Control Engineering Practice,2018,72:177-191.

9. [9]

   TAN P,HE B,ZHANG C,et al.Dynamic modeling of NOX emission in a 660 MW coal-fired boiler with long short-term memory[J].Energy,2019,176:429-436.

10. [10]

    LE T H N,QUACH K G,LUU K,et al.Reformulating level sets as deep recurrent neural network approach to semantic segmentation[J].IEEE Transactions on Image Processing,2018,27(5):2393-2407.

11. [11]

    LIU J T,WU C H,WANG J W.Gated recurrent units based neural network for time heterogeneous feedback recommendation[J].Information Sciences,2018,423:50-65.

12. [12]

    ZHAO R,WANG D Z,YAN R Q,et al.Machine health monitoring using local feature-based gated recurrent unit networks[J].IEEE Transactions on Industrial Electronics,2018,65 (2):1539-1548.

13. [13]

    QUE Z J,JIN X H,XU Z G.Remaining useful life prediction for bearings based on a gated recurrent unit[J].IEEE Transactions on Instrumentation and Measurement,2021,70:1-11.

14. [14]

    PENG H C,LONG F H,DING C.Feature selection based on mutual information: Criteria of max-dependency,max-relevance,and min-redundancy[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(8):1226-1238.

15. [15]

    ZHOU P,GUO D W,WANG H,et al.Data-Driven robust M-LS-SVR-based NARX modeling for estimation and control of molten iron quality indices in blast furnace ironmaking[J].IEEE Transactions on Neural Networks and Learning Systems,2018,29(9):4007-4021.

16. [16]

    XIONG W L,LI Y J,ZHAO Y J,et al.Adaptive soft sensor based on time difference Gaussian process regression with local time-delay reconstruction[J].Chemical Engineering Research and Design,2017,117:670-680.

Proportional views