基于双平行结构的集成模型的新型f-CaO软传感器用于水泥
Ting Xu1, Yucheng Zou1, Ruizhen Wang1
1College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China.
The Review of scientific instruments
|May 31, 2024
概括
这项研究引入了一种新的软传感器,用于用于水泥泥无氧化,克服生产的不确定性. 先进的双平行模型提高了水泥制造业的预测准确性和稳定性.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 水泥生产面临的挑战包括工艺不确定性,时间延迟和可变合.
- 精确监测自由氧化 (f-CaO) 对于水泥质量至关重要.
研究的目的:
- 开发一种新的软传感器,用于估计水泥块中的自由氧化.
- 解决水泥生产过程中固有的复杂性和不确定性.
主要方法:
- 提出了一种双平行集成软传感器模型.
- 集成的一维卷积神经网络 (1D-CNN),长期和短期记忆网络 (LSTM),图形神经网络 (GNN) 和极端梯度增强 (XGBoost).
- 优化模型结构以减轻过度装配和利用个别模型的优势.
主要成果:
- 拟议的模型有效地从时间序列数据中提取本地/全球特征和时间/空间特征.
- 与单个模型相比,证明了优越的稳定性,预测准确性和概括能力.
- 在水泥生产过程中监测自由氧化的稳定性.
结论:
- 双平行集成软传感器为水泥生产中的实时监控提供了重大进步.
- 这种方法有效地处理水泥制造的复杂性,从而提高了质量控制.
- 该模型的性能突出显示了将各种深度学习技术集成到工业应用中的潜力.
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