Optimization of Control Parameters of Leaf-wetting Machine Based on Improved Gray Wolf Algorithm

ZHANG Liping; LI Yingna; YANG Yi; DONG Junmin; LUO Danping; XU Xiaoping

doi:10.13976/j.cnki.xk.2024.3296

Volume 53 Issue 6

Dec. 2024

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INFORMATION AND CONTROL > 2024 > 53(6): 793-803. > DOI: 10.13976/j.cnki.xk.2024.3296 CSTR: 32166.14.xk.2024.3296

ZHANG Liping, LI Yingna, YANG Yi, DONG Junmin, LUO Danping, XU Xiaoping. Optimization of Control Parameters of Leaf-wetting Machine Based on Improved Gray Wolf Algorithm[J]. INFORMATION AND CONTROL, 2024, 53(6): 793-803. DOI: 10.13976/j.cnki.xk.2024.3296

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Optimization of Control Parameters of Leaf-wetting Machine Based on Improved Gray Wolf Algorithm

ZHANG Liping^{1, 2},
LI Yingna^{1, 2, ,},
YANG Yi³,
DONG Junmin³,
LUO Danping^{1, 2},
XU Xiaoping^{1, 2}

1.
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
2.
Computer Technology Application Key Lab of the Yunnan Province, Kunming 650500, China
3.
Yunnan Province Tobacco Compounding and Roasting Co., Chuxiong 675000, China

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Received Date: August 06, 2023
Revised Date: June 20, 2024
Accepted Date: October 09, 2024

Graphical Abstract

Abstract

Abstract

We address the challenge of adapting to external factors and fluctuations in incoming material characteristics in leaf-wetting machines, which traditionally rely on manual experience to regulate control parameters. Our approach predicts tobacco quality prediction and adaptively optimizes process parameters. First, we identify key control parameters and factors affecting their regulation by systematically analyzing the process flow. We then apply a Bayesian optimization extreme gradient boosting tree algorithm to model the relationship between process parameters and the moisture content and temperature of exported tobacco. Finally, using the standard quality of exported tobacco as our optimization objective, we determine the global optimal solution. To stabilize tobacco quality after wetting and improve the leaf-wetting machine's operation, we introduce an improved gray wolf algorithm with bounded stability and an adaptive penalty function. This approach accelerates convergence speed and reduces control parameter fluctuations. Experimental results show that our method reduces the moisture content and temperature fluctuation ranges of exported tobacco by 42.5% and 29.9%, respectively, compared to manual adjustments, ensuring smoother operation of the leaf-wetting machine.
- parameter optimization,
- objective function,
- extreme gradient boosting tree,
- gray wolf algorithm,
- leaf-wetting machine

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References (25)

References

[1]	张玉海, 胡宗玉, 卢敏瑞, 等. 基于烟叶力学特性的打叶水分适宜性分析及应用[J]. 安徽农业科学, 2022, 50(6): 164-167, 185. ZHANG Y H, HU Z Y, LU M R, et al. Analysis and application of water suitability for threshing based on mechanical properties of tobacco leaves[J]. Anhui Agricultural Science, 2022, 50(6): 164-167, 185.
[2]	尼奇峰, 李方新, 王泽理. 二润模式优化对烟草水分和温度的影响研究[J]. 农产品加工, 2022(2): 40-43. NI Q F, LI F X, WANG Z L. Study on the effect of two-wetting process mode optimization on moisture content and temperature of tobacco[J]. Agricultural Product Processing, 2022(2): 40-43.
[3]	谭昆, 郭杰, 李林朋, 等. 基于中棵烟高油分烟叶二润润叶工艺研究[J]. 消费导刊, 2022(11): 110-113. TAN K, GUO J, LI L P, et al. A study on the two-run leaf wetting process based on the high oil content of middle tree tobacco[J]. Journal of Consumption, 2022(11): 110-113.
[4]	LONG M, HUA Y, WANG X, et al. Effect of different combined moistening and redrying treatments on the physicochemical and sensory capabilities of smoking food tobacco material[J]. Drying Technology, 2017, 36(1): 52-62.
[5]	WANG H Y, WANG L H, JIANG W, et al. Analysis of rewetting characteristics and process parameters in tobacco strip redrying stage[J/OL]. Applied Sciences-Basel, 2022, 12(22)[2023-07-29]. https://www.mdpi.com/2076-3417/12/22/11510. DOI: 10.3390/app122211510.
[6]	周永长, 黄亚宇. 基于BP神经网络建立二次润叶工艺参数的预测模型[J]. 电子科技, 2022, 35(9): 79-86. ZHOU Y C, HUANG Y Y. Establishment of a predictive model of the process parameters of secondary moisturizing based on BP neural network[J]. Electronic Science and Technology, 2022, 35(9): 79-86.
[7]	WANG X Q, WANG H J, WANG P G, et al. Multi-objective optimization of process parameters for ultrasonic rolling extrusion of 42CrMo material[J/OL]. Mechanics & Industry, 2023, 24[2023-07-15]. https://www.mechanics-industry.org/articles/meca/full_html/2023/01/mi210162/mi210162.html. DOI: 10.1051/meca/2023004.
[8]	HAN Y, YUAN H, SHAO Y, et al. Capacity consistency prediction and process parameter optimization of lithium-ion battery based on neural network and particle swarm optimization algorithm[J/OL]. Advanced Theory and Simulations, 2023, 6(8)[2023-07-20]. https://onlinelibrary.wiley.com/doi/10.1002/adts.202300125. DOI: 10.1002/adts.202300125.
[9]	刘传玉, 熊伟丽. 基于超标抑制策略的污水处理过程多目标优化控制[J]. 信息与控制, 2024, 53(2): 250-260, 272. doi: 10.13976/j.cnki.xk.2023.2568 LIU C Y, XIONG W L. Multi-objective optimization control of wastewater treatment process based on overshoot suppression strategy[J]. Information and Control, 2024, 53(2): 250-260, 272. doi: 10.13976/j.cnki.xk.2023.2568
[10]	SIBALIJA T V. Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008-2018)[J/OL]. Applied Soft Computing, 2019, 84(11)[2023-07-19]. https://linkinghub.elsevier.com/retrieve/pii/S1568494619305241. DOI: 10.1016/j.asoc.2019.105743.
[11]	WANG L C, CHEN C C, HSU C C. Applying machine learning and GA for process parameter optimization in car steering wheel manufacturing[J]. International Journal of Advanced Manufacturing Technology, 2022, 122(11/12): 4389-4403. doi: 10.1007/s00170-022-09870-1?utm_source=xmol&utm_content=meta
[12]	MAJUMDER A, DAS A, DAS P K. A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel[J]. Neural Computing & Applications, 2018, 29(3): 665-77. http://www.xueshufan.com/publication/2500400143
[13]	NAGARAJAN V, SOLAIYAPPAN A, MAHALINGAM S K, et al. Meta-heuristic technique-based parametric optimization for electrochemical machining of monel 400 alloys to investigate the material removal rate and the sludge[J/OL]. Applied Sciences-Basel, 2022, 12(6)[2023-08-02]. https://www.mdpi.com/2076-3417/12/6/2793. DOI: 10.3390/app12062793.
[14]	FARIS H, ALJARAH I, AL-BETAR M A, et al. Grey wolf optimizer: A review of recent variants and applications[J]. Neural Computing & Applications, 2018, 30(2): 413-435. http://www.onacademic.com/detail/journal_1000040130840410_4dc3.html
[15]	陈秋荣, 郭飞麒. 顺逆流组合式润叶工艺[J]. 湖北农业科学, 2013, 52(4): 937-939. CHEN Q R, GUO F Q. Downstream-upstream combined moisture technology[J]. Hubei Agricultural Science, 2013, 52(4): 937-939.
[16]	刘余里, 张勇, 刘艳芳, 等. 打叶复烤出片率影响因素分析[J]. 安徽农业科学, 2021, 49(5): 185-188. LIU Y L, ZHANG Y, LIU Y F, et al. Analysis of influencing factors on threshing and redrying strips yield[J]. Anhui Agricultural Science, 2021, 49(5): 185-188.
[17]	白寅良, 王翔飞, 雷翔, 等. 打叶复烤成品长梗率的影响因素分析[J]. 安徽农业科学, 2022, 50(9): 175-178. BAI Y L, WANG X F, LEI X, et al. Influencing factors analysis of the long-stem rate of threshing and redrying products[J]. Anhui Agricultural Science, 2022, 50(9): 175-178.
[18]	李跃锋, 姜焕元, 刘志平, 等. 烟叶温度和含水率与打叶质量的关系[J]. 烟草科技, 2005(2): 5-6, 18. LI Y F, JIANG H Y, LIU Z P, et al. Relationship of threshing quality with temperature and moisture content of tobacco leaf[J]. Tobacco Science & Technology, 2005(2): 5-6, 18.
[19]	YUAN C, JIAN-GUO C, TAO W, et al. The cold rolling load distribution of the nuclear power zirconium alloy based on the self-adaptive particle swarm optimization algorithm[J]. The International Journal of Advanced Manufacturing Technology, 2022, 119(9/10): 6007-6016. doi: 10.1007/s00170-021-08272-z
[20]	ZHOU J, QIU Y G, ZHU S L, et al. Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization[J]. Underground Space, 2021, 6(5): 506-515. http://engine.scichina.com/doi/pdf/89E4D35287944FEDBFD49C73B3200CA2
[21]	XIONG S, LIU Z X, MIN C D, et al. Compressive strength prediction of cemented backfill containing phosphate tailings using extreme gradient boosting optimized by whale optimization algorithm[J/OL]. Materials, 2023, 16(1)[2023-08-02]. https://www.mdpi.com/1996-1944/16/1/308. DOI: 10.3390/ma16010308.
[22]	SHENG C, YU H. An optimized prediction algorithm based on XGBoost[C/OL]//2022 International Conference on Networking and Network Applications. Piscataway, USA: IEEE, 2022[2024-08-01]. https://ieeexplore.ieee.org/document/9985004. DOI: 10.1109/NaNA56854.2022.00082
[23]	LIU Y, JIANG Y, ZHANG X, et al. An improved grey wolf optimizer algorithm for identification and location of gas emission[J/OL]. Journal of Loss Prevention in the Process Industries, 2023, 82[2024-07-14]. https://linkinghub.elsevier.com/retrieve/pii/S0950423023000335. DOI: 10.1016/j.jlp.2023.105003.
[24]	RAUF H T, GAO J C, ALMADHOR A, et al. Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems[J/OL]. Applied Soft Computing, 2023, 132[2024-07-14]. https://linkinghub.elsevier.com/retrieve/pii/S1568494622009589. DOI: 10.1016/j.asoc.2022.109909.
[25]	WANG J Y, SONG X H, ABD EL-LATIF A A. Single-objective particle swarm optimization-based chaotic image encryption scheme[J/OL]. Electronics, 2022, 11(16)[ 2024-07-22]. https://www.mdpi.com/2079-9292/11/16/2628. DOI: 10.3390/electronics11162628.

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