软传感器模型在家用废水处理中进行比较分析和应用,以促进可持续性
Cheng Qiu1,2, Fang-Qian Huang3, Yu-Jie Zhong4
1Department of Material and Environmental Engineering, Chengdu Technological University, Chengdu, People's Republic of China.
Environmental technology
|October 23, 2024
概括
循环神经网络擅长预测废水处理中的氨 (NH3-N). 这种软传感器方法优化了流程,提高了能源效率和可持续性.
科学领域:
- 环境工程 环境工程
- 废水处理技术 废水处理技术
- 环境监测中的人工智能
背景情况:
- 精确监测氨 (NH3-N) 对于有效的废水处理至关重要.
- 传统的NH3-N测量方法可能耗时且资源密集.
- 软传感器为实时过程监控和控制提供了一个有希望的替代方案.
研究的目的:
- 开发和评估软传感器模型,用于预测废水处理过程中的NH3-N值.
- 为了比较线性回归,神经网络和随机森林回归模型的性能.
- 使用人工智能和自动控制系统优化测序批量反应堆过程.
主要方法:
- 使用电子导电性和温度传感器数据开发软传感器模型.
- 线性回归 (LR),神经网络 (NN) 和随机森林回归 (RFR) 模型的比较.
- 使用人工智能驱动的自动控制系统优化测序批量反应堆 (SBR) 过程.
主要成果:
- 基于循环神经网络 (RNN) 的软传感器 (NNR[0.5Y]H) 显示出卓越的性能.
- 该NNR[0.5Y]H模型实现了0.921的R2,6.110的RMSE和4.558.5的MAE.
- 开发的软传感器可以在NH3-N水平达到废水标准时自动终止处理周期,从而提高能源效率.
结论:
- 循环神经网络对于NH3-N软传感器的开发是非常有效的,因为它们能够捕捉时间依赖.
- NNR[0.5Y]H软传感器为优化废水处理提供了准确可靠的NH3-N监测.
- 这种人工智能驱动的方法提高了废水处理厂的过程控制,能源效率和可持续性.
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