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Technological parameter optimization for walnut shell-kernel winnowing device based on neural network.

Hao Li1,2, Yurong Tang1,2, Hong Zhang1,2

  • 1College of Mechanical Electrification Engineering, Tarim University, Alar, China.

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|February 23, 2023
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Summary
This summary is machine-generated.

Numerical simulation optimized walnut shell-kernel winnowing devices. Inlet wind velocity significantly impacts cleaning and loss rates, with optimal performance achieved at specific settings, validating the simulation method.

Keywords:
CFD-DEMneural networktechnological parameter optimizationwalnutwinnowing device

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Area of Science:

  • Agricultural Engineering
  • Mechanical Engineering
  • Computational Fluid Dynamics

Background:

  • Traditional methods for detecting technological parameters in winnowing devices are outdated, characterized by long test cycles, significant measurement errors, and large test volumes.
  • Disclosing the operational mechanisms of these devices is challenging due to time-consuming and labor-intensive processes.

Purpose of the Study:

  • To reveal the operational mechanism of a walnut shell-kernel winnowing device using numerical simulation.
  • To predict the influence of baffle opening combinations, inlet wind velocity, and inlet angle on cleaning rate and loss rate using a neural network model.

Main Methods:

  • Numerical simulation was employed to investigate the winnowing mechanism.
  • A neural network model was developed to predict the impact of key parameters on performance metrics.
  • Bench tests were conducted to compare and validate simulation results.

Main Results:

  • Inlet wind velocity was identified as the primary factor affecting both cleaning rate and loss rate.
  • Optimal performance (79.91% cleaning rate, 14.37% loss rate) was achieved with specific parameter settings: 7.01 cm baffle opening, 24.36 m/s inlet wind velocity, and 9.47° inlet angle.
  • Increased inlet wind velocity led to misclassification of walnut shells and kernels, degrading winnowing performance.

Conclusions:

  • The combined numerical simulation and neural network method is feasible and valid for optimizing shell-kernel winnowing devices, with a relative error below 1.06%.
  • Inlet wind velocity is a critical parameter influencing winnowing device performance.
  • This research provides valuable insights for the future development of efficient shell-kernel separation technologies for hard nuts.