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Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model.

Zhengyuan Li1, Jie Chen1, Yanmei Meng1

  • 1College of Mechanical Engineering, Guangxi University, Nanning 530004, China.

Foods (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework for optimizing sugarcane juice extraction. The new model enhances grinding capacity, reduces energy consumption, and improves sucrose extraction rates in sugar production.

Keywords:
deep deterministic policy gradientdeep kernel extreme learning machinemulti-objective chicken swarm optimizationsugarcane milling systemsynergetic theory

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Area of Science:

  • Agricultural Engineering
  • Process Optimization
  • Data-Driven Modeling

Background:

  • Sugarcane juice extraction relies on experienced workers, limiting adaptive process adjustments.
  • Current methods for optimizing sugarcane milling lack data-driven precision.

Purpose of the Study:

  • To develop a multi-objective optimization framework for sugarcane milling systems.
  • To enable adaptive adjustment of operating parameters for improved efficiency.

Main Methods:

  • Abstracting the milling process using synergetic theory (material, energy, information flow).
  • Developing a deep kernel extreme learning machine (DK-ELM) model for process simulation.
  • Applying improved multi-objective chicken swarm optimization (IMOCSO) and deep deterministic policy gradient (DDPG) algorithms.

Main Results:

  • Achieved a 3.2 t/hour increase in grinding capacity.
  • Reduced electric consumption by 660 W per ton of sugarcane.
  • Increased sucrose extraction by 0.03%.

Conclusions:

  • The proposed data-driven framework effectively optimizes sugarcane milling operations.
  • The multi-objective optimization approach enhances key performance indicators.
  • Adaptive control strategies improve system performance under varying conditions.