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通过数据增强的序列对序列改进水泥生产过程 - - 沃斯斯坦生成对抗网络模型用于准确预测f-CaOO
Ying Zhang1, Jinbo Liu1, Hui Dang1
1School of Electrical Engineering, Yanshan University, 438 Hebei Avenue, Qinhuangdao 066004, China.
The Review of scientific instruments
|October 11, 2023
概括
本研究引入了一种Seq2Seq-WGAN模型,以改善水泥生产中的时间依赖性捕获. 该方法准确地预测自由的连续时间序列,有助于生产规划.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
- 工业过程控制 工业过程控制
背景情况:
- 水泥生产过程表现出复杂的时间依赖.
- 现有的方法很难充分捕捉这些时间关系.
- 准确预测像自由这样的关键变量对于高效生产至关重要.
研究的目的:
- 提出一种新的方法,以提高在水泥生产中的时间依赖性捕获.
- 开发一种能够准确预测自由连续时间序列的模型.
- 为改进水泥生产计划提供数据驱动的方法.
主要方法:
- 开发了一个数据增强的Sequence to Sequence-Wasserstein生成对抗网络 (Seq2Seq-WGAN) 模型.
- 瓦斯斯坦生成对抗网络 (WGAN) 用于生成f-CaO标签数据.
- 使用Seq2Seq架构来处理不平等的输入-输出序列长度.
- 编码器包含时间特征的隐藏向量初始化了解码器的门循环单元.
主要成果:
- Seq2Seq-WGAN模型证明了对自由的连续时间序列的准确预测.
- 该方法有效地探索了输入和输出变量之间的时间依赖关系.
- 实验结果验证了模型捕捉复杂的时间模式的能力.
结论:
- 拟议的Seq2Seq-WGAN模型为预测水泥生产中的自由提供了一个实际的解决方案.
- 这种方法为随后的生产规划和调度提供了宝贵的见解.
- 该方法显示了支持水泥行业生产优化的巨大潜力.
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