在微波加热过程中控制微波加热过程中的热均性通过BPNN和自适应粒子群集优化
Qing Yao1,2, Xin Zheng1,2, Rui Wang1,2
1Key Laboratory of Healthy & Intelligent Kitchen System Integration of Zhejiang Province, Ningbo, 315336, China.
Heliyon
|November 21, 2024
概括
适应性粒子群集优化 (APSO) 神经网络系统可提高工业应用中的微波加热统一性. 这种智能控制优化功率,最大限度地减少温度误差,以实现高效的绿色经济进步.
科学领域:
- 工业工程 工业工程 工业工程
- 控制系统 控制系统
- 绿色技术 绿色技术是一种绿色技术.
背景情况:
- 能源密集型行业寻求高效和可持续的加热方法.
- 微波加热提供了高效率和环境效益,但由于材料性能变化,因此难以实现均的加热.
- 像PID和BPNN这样的传统控制方法在动态条件下实现精确的温度控制方面存在局限.
研究的目的:
- 开发一种智能控制系统,用于工业应用中均的微波加热.
- 通过优化输入功率来解决可变微波吸收的挑战.
- 加强中国绿色经济倡议中的节能和效率.
主要方法:
- 提出了一个自适应粒子优化 (APSO) 神经网络微波系统.
- 集成的APSO与反向传播神经网络 (BPNN) 进行智能控制.
- 使用系统识别和反错误作为PSO调整神经网络参数的适应性函数.
主要成果:
- APSO控制器有效地将实际和确定的温度输出之间的误差降到最低.
- 该系统展示了精确的温度调节,紧密遵循预设的温度曲线.
- 实现了0.74的低根平均平方误差,表明了高精度和稳定性.
结论:
- 开发的基于APSO的智能控制系统显著提高了微波加热的均性和准确性.
- 这种创新方法为工业微波加热的能源优化提供了可行的解决方案.
- 该系统通过提高能源效率,有助于推进绿色经济原则.
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