Relationship Study of Meltblowning Variables Based on Machine Learning Algorithm
DOI: 10.23977/autml.2022.030204 | Downloads: 14 | Views: 590
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 MaoABSTRACT
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 algorithmCITE 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: | 1539 |
---|---|
Visits: | 65029 |
Sponsors, Associates, and Links
-
Power Systems Computation
-
Internet of Things (IoT) and Engineering Applications
-
Computing, Performance and Communication Systems
-
Journal of Artificial Intelligence Practice
-
Advances in Computer, Signals and Systems
-
Journal of Network Computing and Applications
-
Journal of Web Systems and Applications
-
Journal of Electrotechnology, Electrical Engineering and Management
-
Journal of Wireless Sensors and Sensor Networks
-
Journal of Image Processing Theory and Applications
-
Mobile Computing and Networking
-
Vehicle Power and Propulsion
-
Frontiers in Computer Vision and Pattern Recognition
-
Knowledge Discovery and Data Mining Letters
-
Big Data Analysis and Cloud Computing
-
Electrical Insulation and Dielectrics
-
Crypto and Information Security
-
Journal of Neural Information Processing
-
Collaborative and Social Computing
-
International Journal of Network and Communication Technology
-
File and Storage Technologies
-
Frontiers in Genetic and Evolutionary Computation
-
Optical Network Design and Modeling
-
Journal of Virtual Reality and Artificial Intelligence
-
Natural Language Processing and Speech Recognition
-
Journal of High-Voltage
-
Programming Languages and Operating Systems
-
Visual Communications and Image Processing
-
Journal of Systems Analysis and Integration
-
Knowledge Representation and Automated Reasoning
-
Review of Information Display Techniques
-
Data and Knowledge Engineering
-
Journal of Database Systems
-
Journal of Cluster and Grid Computing
-
Cloud and Service-Oriented Computing
-
Journal of Networking, Architecture and Storage
-
Journal of Software Engineering and Metrics
-
Visualization Techniques
-
Journal of Parallel and Distributed Processing
-
Journal of Modeling, Analysis and Simulation
-
Journal of Privacy, Trust and Security
-
Journal of Cognitive Informatics and Cognitive Computing
-
Lecture Notes on Wireless Networks and Communications
-
International Journal of Computer and Communications Security
-
Journal of Multimedia Techniques
-
Computational Linguistics Letters
-
Journal of Computer Architecture and Design
-
Journal of Ubiquitous and Future Networks