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在软传感器设计中整合数据驱动模型和过程专业知识,用于废水处理的数字双胞胎应用程序
Henri Haimi1, Alexis Awaitey2, Anmol Kiran3
1Department of Built Environment, Aalto University, P.O. Box 15200, FI-00076 AALTO, Espoo, Finland
概括
研究人员为废水处理厂开发了软传感器,成功地实时预测了- (NH4-N). 虽然COD预测显示出希望,但对于这两个参数实现同时准确仍然是一个挑战.
科学领域:
- 环境工程 环境工程
- 废水处理技术 废水处理技术
- 数字生应用程序 数字生应用程序
背景情况:
- 数字双胞胎模型需要废水处理厂 (WWTP) 的持续实时数据.
- 在严酷的WWTP头部中获取数据带来了重大挑战.
- 软传感器为实时过程监控提供了一个解决方案.
研究的目的:
- 设计软传感器,用于预测初级废水的化学氧气需求 (COD) 和- (NH4-N).
- 将数据驱动模型与专家知识相结合,以提高预测准确度.
- 在实时WWTP操作中评估软传感器的性能.
主要方法:
- 结合数据驱动模型 (普通最小方程,SARIMAX) 与流程专业知识.
- 使用流量和悬浮固体度作为输入变量.
- 实施了基于过程洞察力的权重来提高预测准确性.
主要成果:
- 实现了NH4-N的优异预测准确性,并通过权重进一步增强.
- 证明了良好的COD估计准确性或有效的变异性捕获,但不是同时.
- 软传感器在模拟中提供了与实验室数据相比或比实验室数据更好的实时预测.
结论:
- 软传感器集成数据驱动的方法和专家知识是有效的WWTP监控.
- 实时NH4-N预测非常成功,有可能改善COD预测.
- 开发的软传感器可以提高WWTP的运营效率和流程优化.
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