Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 240900069-7.doi: 10.11896/jsjkx.240900069

• Artificial Intelligence • Previous Articles     Next Articles

Prediction of Moisture Content and Temperature of Tobacco Leaf Re-curing Outlet Based onImproved DBO-BP Neural Network

SUN Yongqian, TANG Shouguo   

  1. Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650504,China
    Yunnan Key Laboratory of Computer Technologies Application,Kunming 650504,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:SUN Yongqian,born in 1997,postgraduate.His main research interests include intelligent optimization algorithms and so on.
    TANG Shouguo,born in 1981,senior engineer.His main research interests include medical information technology and machine learning.
  • Supported by:
    Special Foundation for Basic Research Program of Yunnan(202201AS070029) and Major Project of Yunnan(202302AD080002).

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Abstract: In order to improve the quality of tobacco leaves after re-roasting,this paper proposes a prediction model based on the improved dung beetle optimisation algorithm(DBO)-BP neural network,which aims to accurately predict the moisture content and temperature of the roaster outlet during the re-roasting process.Firstly,the grey correlation analysis method is used to analyse the correlation degree between the process parameters and the moisture content and temperature at the outlet of the oven,and in order to improve the prediction accuracy and stability of the model,the Circle search strategy is introduced to optimise the dung beetle algorithm,so that it could explore the solution space more effectively and avoid falling into the local optimum.Secondly,the improved dung beetle algorithm is used to optimise the weights and thresholds of the BP neural network.Finally,a prediction model of outlet moisture content and temperature of Circle-DBO-BP re-baking oven is established.The prediction results are simu-lated by MATLAB and compared with the XGBOOST model,Tent-DBO-BP model and SSA-BP model.Experimental results show that the improved Circle-DBO-BP model has an MSE of 0.046 7 and 0.038 4 in the prediction of moisture content and temperature at the tobacco leaf re-roasting outlet,respectively,which provides strong support for the control of the tobacco leaf re-roasting process.

Key words: Tobacco re-roasting, BP neural network, Circle chaos mapping, Outlet moisture content, Outlet temperature

CLC Number: 

  • TP181
[1]LI L Q,CHEN S L.Discussion on the Factors Influencing Moisture Content of Redried Tobacco Leaves [J].Crop Research,2012,26(S1):74-77.
[2]TAO H.Expounding the Operation Countermeasures of PLC inTobacco Redrying Production Environment[J].Hebei Agricultural Machinery,2017(3):41-42,44.
[3]LI B,WANG Z L,WU Y C,et al.Design of the Process Parameters in the Damp Area of the Hot-ordering Process[J].Tianjin Agricultural Sciences,2023,29(S1):105-110.
[4]TANG J,ZHOU B,YI B,et al.Influence and Application Re-search of Ambient Temperature and Humidity in Primary Processing on Tobacco Moisture Content Between Key Processes[J].Hubei Agricultural Sciences,2023,62(8):175-181.
[5]YAO S S,ZENG X L,WANG H,et al.The Prediction Model of Balanced Moisture Contents as Well as the Analysis of Mildew of Flue-cured Leaves for Yunyan 87[J].Biological Disaster Science,2022,45(4):456-462.
[6]ZHANG H.Application of Big Data Technology in Prediction and Control of Moisture at the Drying Outlet[J].Telecom World,2017(6):249-250.
[7]JIN F G,WANG Y L,ZHANG P C,et al.Prediction of Inlet Moisture Content to Tobacco Dryer Based on Random Forest and DE-ELM[J].Control Engineering of China,2020,27(3):532-539.
[8]LI Z J,LIU B,GAO Y,et al.Establishment and Detection ofMotion Prediction Model Key Processes of Igarette Cutting Process[J].Food & Machinery,2020,36(10):190-195,205.
[9]LI X,QU L,TAN M,et al.Automated essay scoring based on the enhanced chimp optimization algorithm-back propagation(ENChOA-BP) and K-means[J].Multimedia Tools and Applications,2024,(prepublish):1-32.
[10]ASCHER M,JONATHAN B,JEANINE S,et al.An Ecological Approach to Modeling Vision:Quantifying Form Perception Using the Circle Map Equation[J].Ecological Psychology,2020,32(1):41-57.
[11]GENG X L,YANG Z.Scheme Recommendation Based on Grey Correlation Prediction and Trust Cloud Hybrid Algorithm[J].Computer Integrated Manufacturing Systems,2020,26(4):980-988.
[12]RACHANA C,DHWANI A,KRITIKA R,et al.Multi-output incremental back-propagation[J].Neural Computing and Applications,2023,35(20):14897-14910.
[13]HUANG L E,WU L S,CHEN H W.Image Blur Types and Parameters Estimation Using DCNN Fusion with the LSTM[J].Journal of Basic Science and Engineering,2018,26(5):1092-1100.
[14]AKIRA F.Evaluating Classifier Confidence for Surface EMGPattern Recognition[C]//Annual International Conference of the IEEE Engineering in Medicine and Biology Society.2023:1-4.
[15]JAGADISH K N,BALASUBRAMANIAN C.Hybrid Gradient Descent Golden Eagle Optimization(HGDGEO) Algorithm-Based Efficient Heterogeneous Resource Scheduling for Big Data Processing on Clouds[J].Wireless Personal Communications,2023,129(2):1175-1195.
[16]PAN J C,LI S B,ZHOU P,et al.Dung Beetle Optimization Algorithm Guided by Improved Sine Algorithm[J].Computer Engineering and Applications,2018,26(5):1092-1100.
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