Technological parameter optimization for walnut shell-kernel winnowing device based on neural network
The detection method for technological parameter is outdates as the traditional test cycle is long as well as the measurement error and the test amount are huge. Moreover, it is difficult to disclose the operation mechanism of devices as the operation is time-consuming and laborious. Therefore, nume...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2023.1107836/full |
| _version_ | 1797935248381575168 |
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| author | Hao Li Hao Li Yurong Tang Yurong Tang Hong Zhang Hong Zhang Yang Liu Yang Liu Yongcheng Zhang Yongcheng Zhang Hao Niu Hao Niu |
| author_facet | Hao Li Hao Li Yurong Tang Yurong Tang Hong Zhang Hong Zhang Yang Liu Yang Liu Yongcheng Zhang Yongcheng Zhang Hao Niu Hao Niu |
| author_sort | Hao Li |
| collection | DOAJ |
| description | The detection method for technological parameter is outdates as the traditional test cycle is long as well as the measurement error and the test amount are huge. Moreover, it is difficult to disclose the operation mechanism of devices as the operation is time-consuming and laborious. Therefore, numerical simulation was used in this study to reveal the mechanism of the walnut shell-kernel winnowing device. Moreover, the influence of baffle opening combinations, inlet wind velocity and inlet angle on cleaning rate and loss rate was predicted by the neural network model. The results demonstrated that inlet wind velocity was the primary influencing factor of cleaning rate, followed by baffle opening and inlet angle. Besides, inlet wind velocity was the primary influencing factor of loss rate, followed by inlet angle and baffle opening. The winnowing device performed best (79.91% cleaning rate, 14.37% loss rate) when the baffle opening, inlet wind velocity and inlet angle were 7.01 cm, 24.36 m/s, and 9.47°. In addition, 1/8 walnut shells and 1/4 walnut kernels were incorrectly classified due to the increase in inlet wind velocity. The inlet wind velocity was considered the major cause behind the deteriorating winnowing performance of the device. Finally, the bench test and simulation optimization results were compared. The cleaning rate and loss rate relative error during the simulation test was lower than 1.06%, which ascertained the feasibility and validity of the neural network as well as the combined numerical simulation method. This study could be useful for future research and development of shell-kernel winnowing devices for hard nuts. |
| first_indexed | 2024-04-10T18:12:32Z |
| format | Article |
| id | doaj.art-5deb470329f7495b8acc05d52c8153ea |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-04-10T18:12:32Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-5deb470329f7495b8acc05d52c8153ea2023-02-02T10:28:22ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852023-02-011110.3389/fbioe.2023.11078361107836Technological parameter optimization for walnut shell-kernel winnowing device based on neural networkHao Li0Hao Li1Yurong Tang2Yurong Tang3Hong Zhang4Hong Zhang5Yang Liu6Yang Liu7Yongcheng Zhang8Yongcheng Zhang9Hao Niu10Hao Niu11College of Mechanical Electrification Engineering, Tarim University, Alar, ChinaAgricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar, ChinaCollege of Mechanical Electrification Engineering, Tarim University, Alar, ChinaAgricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar, ChinaCollege of Mechanical Electrification Engineering, Tarim University, Alar, ChinaAgricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar, ChinaCollege of Mechanical Electrification Engineering, Tarim University, Alar, ChinaAgricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar, ChinaCollege of Mechanical Electrification Engineering, Tarim University, Alar, ChinaAgricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar, ChinaCollege of Mechanical Electrification Engineering, Tarim University, Alar, ChinaAgricultural Engineering Key Laboratory, Ministry of Higher Education of Xinjiang Uygur Autonomous Region, Tarim University, Alar, ChinaThe detection method for technological parameter is outdates as the traditional test cycle is long as well as the measurement error and the test amount are huge. Moreover, it is difficult to disclose the operation mechanism of devices as the operation is time-consuming and laborious. Therefore, numerical simulation was used in this study to reveal the mechanism of the walnut shell-kernel winnowing device. Moreover, the influence of baffle opening combinations, inlet wind velocity and inlet angle on cleaning rate and loss rate was predicted by the neural network model. The results demonstrated that inlet wind velocity was the primary influencing factor of cleaning rate, followed by baffle opening and inlet angle. Besides, inlet wind velocity was the primary influencing factor of loss rate, followed by inlet angle and baffle opening. The winnowing device performed best (79.91% cleaning rate, 14.37% loss rate) when the baffle opening, inlet wind velocity and inlet angle were 7.01 cm, 24.36 m/s, and 9.47°. In addition, 1/8 walnut shells and 1/4 walnut kernels were incorrectly classified due to the increase in inlet wind velocity. The inlet wind velocity was considered the major cause behind the deteriorating winnowing performance of the device. Finally, the bench test and simulation optimization results were compared. The cleaning rate and loss rate relative error during the simulation test was lower than 1.06%, which ascertained the feasibility and validity of the neural network as well as the combined numerical simulation method. This study could be useful for future research and development of shell-kernel winnowing devices for hard nuts.https://www.frontiersin.org/articles/10.3389/fbioe.2023.1107836/fullneural networkwinnowing deviceCFD-DEMwalnuttechnological parameter optimization |
| spellingShingle | Hao Li Hao Li Yurong Tang Yurong Tang Hong Zhang Hong Zhang Yang Liu Yang Liu Yongcheng Zhang Yongcheng Zhang Hao Niu Hao Niu Technological parameter optimization for walnut shell-kernel winnowing device based on neural network Frontiers in Bioengineering and Biotechnology neural network winnowing device CFD-DEM walnut technological parameter optimization |
| title | Technological parameter optimization for walnut shell-kernel winnowing device based on neural network |
| title_full | Technological parameter optimization for walnut shell-kernel winnowing device based on neural network |
| title_fullStr | Technological parameter optimization for walnut shell-kernel winnowing device based on neural network |
| title_full_unstemmed | Technological parameter optimization for walnut shell-kernel winnowing device based on neural network |
| title_short | Technological parameter optimization for walnut shell-kernel winnowing device based on neural network |
| title_sort | technological parameter optimization for walnut shell kernel winnowing device based on neural network |
| topic | neural network winnowing device CFD-DEM walnut technological parameter optimization |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2023.1107836/full |
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