基于机器学习的真空查期间影响水分含量的主要因素的预测和分析
Ling Nie1,2, Weiguo Ma3, Xiangdong Xie4
1School of Computer Science, Yangtze University, Jingzhou, 434000, Hubei, China.
Scientific reports
|August 6, 2024
概括
这项研究确定了在真空过过程中影响过蛋糕水分的关键因素. 发现粒子比率,网和空气流量率占主导地位,指导真空选的优化.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 过技术过技术的使用
背景情况:
- 研究真空过机制需要了解影响水分含量的因素.
- 关于影响过蛋糕湿度的主导因素的实验数据有限.
研究的目的:
- 设计和制造一个真空过装置.
- 在真空选过程中识别影响过蛋糕水分含量的主要因素.
- 建立湿度含量的预测模型.
主要方法:
- 使用定制设计的真空过装置与石英砂.
- 在各种条件下收集了300个过蛋糕湿度数据集.
- 采用多线性回归,人工神经网络,决策树,随机森林和极端梯度提升用于建模.
主要成果:
- 确定了粒子比率,屏幕网格和空气流量率作为影响水分含量的主要因素.
- 分析的特征重要性和相关性效应.
- 开发了真空选湿度含量的预测模型.
结论:
- 粒子比率,屏幕网格和空气流量率显著影响过器蛋糕的湿度.
- 这些发现为优化真空选技术提供了科学基础.
- 结果促进了选效率的提高和真空选机制的理解.
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