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Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted

Taoqing Yang1,2,3, Xia Zheng1,2,3, Sriram K Vidyarthi4

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.

Foods (Basel, Switzerland)
|May 13, 2023
PubMed
Summary

This study optimized walnut breaking using artificial neural networks (ANN) and genetic algorithms (GA). The combined model accurately predicted optimal parameters for improved kernel quality and energy efficiency.

Keywords:
artificial neural networkdryinggenetic algorithmmulti-objective optimizationshell breakingwalnut

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Area of Science:

  • Agricultural Engineering
  • Food Science and Technology
  • Computational Intelligence

Background:

  • Optimizing post-harvest processing of walnuts is crucial for maximizing kernel yield and quality.
  • Traditional walnut breaking methods often face challenges with efficiency and energy consumption.
  • Developing predictive models for processing parameters can enhance industrial applications.

Purpose of the Study:

  • To develop a predictive model for walnut breaking using artificial neural networks (ANN) and genetic algorithms (GA).
  • To determine the optimal process parameters for drying-assisted walnut breaking, focusing on maximizing kernel rates and minimizing energy use.
  • To validate the efficacy of the combined ANN-GA approach for process optimization in similar commodities.

Main Methods:

  • Walnuts were dried using air-impingement technology at varying infrared temperatures and air velocities.
  • Dried walnuts were subjected to breaking tests under different loading directions.
  • An artificial neural network (ANN) optimized by a genetic algorithm (GA) was employed to model and optimize response variables like drying time, energy consumption, and kernel rates.

Main Results:

  • The ANN model demonstrated high prediction accuracy for all response variables (R² > 0.99).
  • Optimized parameters included 54.9 °C IR temperature, 3.66 m/s air velocity, 10.9% moisture content, and vertical loading direction.
  • The combined ANN-GA method achieved a low relative error (2.51-3.96%) in predicting experimental outcomes.

Conclusions:

  • The integrated ANN-GA approach is effective for predicting and optimizing walnut breaking processes.
  • The study provides a framework for improving walnut processing efficiency, energy conservation, and kernel quality.
  • This methodology offers valuable insights for optimizing the processing of other similar agricultural products.