在使用LSTM神经网络的连续干燥系统中预测玉米水分含量
Marko Simonič1, Mirko Ficko1, Simon Klančnik1
1Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia.
Foods (Basel, Switzerland)
|April 15, 2025
概括
本研究引入了一种机器学习模型,用于预测玉米在干燥过程中的水分含量. 该模型提高了效率,减少了能源使用,并提高了农业4.0中的产品质量.
科学领域:
- 农业工程 农业工程
- 数据科学数据科学数据科学
- 食品加工技术 食品加工技术
背景情况:
- 优化粮食生产效率对于农业4.0至关重要.
- 玉米干燥效率影响长期储存和经济可行性.
- 需要创新技术来改善关键的食品工艺.
研究的目的:
- 使用机器学习开发玉米水分含量的预测模型.
- 评估模型在连续干燥系统中的准确性和实用性.
- 为可持续的干燥技术和数据驱动的过程改进做出贡献.
主要方法:
- 利用了各种干燥参数和天气条件的历史数据 (3826个样本).
- 应用数据归算技术,以确保模型培训的数据完整性.
- 实现了一个多层神经网络,其中有一个LSTM层和三个密集层.
主要成果:
- 实现了高预测准确度,RMSE为0.645,MSE为0.416,MAE为0.352,MAPE为2.555. 这两种情况的预测准确度都很高.
- 通过客观绩效指标和数据可视化来证明模型的有效性.
- 验证了模型在连续干燥中预测出口含水量的能力.
结论:
- 拟议的数据驱动模型是预测玉米水分含量的宝贵工具.
- 这种方法提高了工艺效率,降低了能源消耗,提高了产品质量.
- 这些发现支持在食品工业中推进可持续的连续干燥.
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