Education, Science, Technology, Innovation and Life
Open Access
Sign In

Relationship Study of Meltblowning Variables Based on Machine Learning Algorithm

Download as PDF

DOI: 10.23977/autml.2022.030204 | Downloads: 14 | Views: 593

Author(s)

Peng Cheng 1, Fuyou Mao 2, Haomin Zhao 2

Affiliation(s)

1 Department of Water Conservancy Science and Engineering, Sichuan University, Sichuan, 610065, China
2 College of Computer Science and Technology, Shenyang Jianzhu University, Shenyang, 110168, China

Corresponding Author

Fuyou Mao

ABSTRACT

For the missing intercalation rate data, we first filled it in, and then split it according to the variable pairs to observe the changes of six indexes before and after intercalation. Then, we made grey correlation analysis between the changes and intercalation rate, and got the influence of intercalation rate on each index. In order to further explore the relationship between process parameters and structural variables, nine models, KNN, linear regression, ridge regression, lasso regression, decision tree, support vector machine, robust model, XGBoost and random forest, are used to train the data, and finally XGBoost regression model has the highest accuracy. Then, using the prediction results of structural variables obtained by XGBoost, we firstly make factor analysis on three indexes of structural variables and product performance, and the index with the largest factor load represents structural variables and product performance, and then make Pearson correlation analysis on these two indexes to get the relationship between structural variables and product performance. Through Pearson analysis of the three internal variables of structural variables and product performance, the internal correlation is obtained. For three indexes in the structural variables, respectively, they are linearly fitted with two variables of process parameters to obtain three fitting equations, and then they are fitted with filtration efficiency to obtain the fitting equation between filtration efficiency and structural variables. Finally, the linear regression equation between filtration efficiency and process parameters is sorted out. However, the effect of changing the linear regression model is not good in model testing, and we think it has a complex nonlinear relationship. Therefore, we use machine learning to carry out regression training on variables. The results show that when the result variables are used to regress the product performance, the effect of using random forest is better, but the filtering efficiency can't be achieved by using many kinds of machine learning. After that, considering the internal influence relationship of product performance, we used structural variables and all other indicators of product performance for regression training, and found that XGBoost algorithm had good effect, so we established a multiple regression model based on machine learning. By controlling the process parameters and observing the predicted structure, it is found that the filtration efficiency is the highest when the receiving distance is 10cm and the hot air speed is 1400r/min. Finally, we set up a multi-objective planning model, and globally optimize the planning model through the sand dune cat population optimization algorithm, and finally get the approximate optimal matching scheme of process parameters.

KEYWORDS

Color correlation analysis, XGBoost algorithm, Factor analysis, Pearson coefficient, Random forest algorithm, Multi-objective planning, Sand dune cat population optimization algorithm

CITE THIS PAPER

Peng Cheng, Fuyou Mao, Haomin Zhao, Relationship Study of Meltblowning Variables Based on Machine Learning Algorithm. Automation and Machine Learning (2022) Vol. 3: 17-26. DOI: http://dx.doi.org/10.23977/autml.2022.030204.

REFERENCES

[1] Jafari Mehran and Shim Eunkyoung and Joijode Abhay. (2021). Fabrication of Poly (lactic acid) filter media via the meltblowing process and their filtration performances: A comparative study with polypropylene meltblown. Separation and Purification Technology, 260.
[2] Hasolli Naim, Park Young Ok & Kim Kwang Deuk. (2021). Multi-layered nonwoven filter media for capture of nanoparticles in HVAC systems. Korean Journal of Chemical Engineering (5). doi:10.1007/S11814-021-0773-9.
[3] Tianqi Chen & Carlos Guestrin. (2016). XGBoost: A Scalable Tree Boosting System.. CoRR.
[4] Ye Yuguang et al. (2021). Management of Medical and Health Big Data Based on Integrated Learning-based Health Care System: A Review and Comparative Analysis. Computer Methods and Programs in Biomedicine, 209 (prepublish), pp. 106293.
[5] Abedi Rahebeh, Costache Romulus, Shafizadeh Moghadam Hossein & Pham Quoc Bao. (2022). Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto International (19). doi:10.1080/10106049.2021.1920636.
[6] Feng Tengfei, Shen Yunzhong, Chen Qiujie, Wang Fengwei & Zhang Xingfu. (2022). Groundwater storage change and driving factor analysis in north china using independent component decomposition. Journal of Hydrology.
[7] Zhu Weidong et al. (2022). Research on optimization of an enterprise financial risk early warning method based on the DS-RF model. International Review of Financial Analysis, 81.
[8] Wang Yongli, Huang Feifei, Tao Siyi, Ma Yang, Ma Yuze, Liu Lin & Dong Fugui. (2022). Multi-objective planning of regional integrated energy system aiming at exergy efficiency and economy. Applied Energy (PB). doi: 10.1016/J. APENERGY.2021.118120.
[9] Seyyedabbasi A, Kiani F. Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems [J]. Engineering with Computers, 2022: 1-25.

Downloads: 1542
Visits: 65077

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.