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学习一种基于神经网络的软传感器,具有双误差并行优化,用于废水处理厂的废水变量预测

Dong Li¹, Chunhua Yang¹, Yonggang Li¹

¹The School of Automation, Central South University, Changsha, 410083, China.

Journal of environmental management

|July 24, 2024

概括

这项研究引入了改进的神经网络软传感器,用于废水处理厂 (WWTP). 它通过优化模型训练和变量选择来提高水质指标的预测准确性和效率.

科学领域:

环境工程环境工程
人工智能的人工智能
水质监测水质监测

背景情况:

机器学习和人工智能模型越来越多地用于预测废水处理厂 (WWTP) 的水质.
由于废水处理过程的复杂,动态性质,现有的模型在准确性和效率方面面临挑战.
在WWTP中的时间变化,非线性和高维数据损害了预测性能和计算速度.

研究的目的:

开发一种基于神经网络的软传感器,用于在WWTP中实时预测废水变量.
解决当前数据驱动软传感器在处理复杂废水处理动态方面的局限性.
改善对水质关键指标的及时预测,以便有效地管理水质.

主要方法:

基于基于活动的分类 (ABC) 原则的Pearson相关系数 (PCC) 和相互信息 (MI) 的组合变量选择方法被使用.
最佳的过程变量被选择为辅助变量,以减少数据维度和模型复杂性.
提出了一种新的双误差并行优化方法,以同时最大限度地减少点预测错误和分布错误,提高神经网络训练效率和配件质量.

主要成果:

拟议的软传感器在基准模拟模型 no. 2 和基准模拟模型 no. 3 两方面都证明了精确的废水变量预测. 1 (BMS1) 和一个实际的氧化沟WWTP数据集.
定量评估显示RMSE,MAE和R2值的表现非常出色.
该方法显著加快了神经网络训练的融合速度,并改善了整体预测性能.

结论:

开发的软传感器有效地提高了WWTP中的水质指标的预测准确性和计算效率.
双误差并行优化和集合变量选择有助于优越的模型性能.
这种方法为有效优化和管理废水处理过程提供了有价值的工具.

关键词:

废水变量预测的预测.神经网络优化神经网络优化软传感器是一种软传感器.变量选择变量选择污水处理污水处理污水处理

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