从典型污染企业的过程监测中进行数据挖掘
Wenya Zhao1,2, Peili Zhang3,4, Da Chen1,2
1Taizhou Pollution Control Technology Center Co. LTD, Taizhou , Zhejiang, 318000, China.
Environmental monitoring and assessment
|August 29, 2023
概括
本研究介绍了一种反向传播的人工神经网络 (BP-ANN) 模型,用于预测工业污染. 该模型准确预测了化学氧气需求和烟气等排放量,有助于环境管理.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 越来越多的环境监测数据需要先进的分析来做出明智的决策.
- 关于用于污染企业过程监测的多变量数据分析的研究有限.
研究的目的:
- 开发和评估一个反向传播的人工神经网络 (BP-ANN) 模型,用于预测废水和废气排放.
- 调查废水和废气处理之间的相关性,使用可变重要性测量 (VIM) 辅助的BP-ANN.
- 提高工业污染监测BP-ANN模型中特征映射的可解释性.
主要方法:
- 利用来自污染企业的物联网 (IoT) 数据进行过程监控.
- 开发了一个BP-ANN模型来预测关键的污染指标:化学氧气需求 (COD),的潜力 (pH),电导率 (EC),烟气排放 (FGE) 和非甲碳化合物度 (NMHC).
- 雇员VIM协助BP-ANN分析污染处理过程中的内部和外部相关性.
主要成果:
- 对所有监测的污染物实现了高预测系数 (R2) 值,包括0.8510的COD和0.9677的FGE.
- 对于pH (-0.62到0.30) 和FGE (-0.21到0.15 m3/s) 的预测误差很低,平均相对误差分别为1.05%和9.60%.
- 成功识别了为BP-ANN模型做出贡献的重要变量,从而促进了特征变量选择.
结论:
- 该BP-ANN模型为废水和废气排放提供了准确的预测,这对于环境管理至关重要.
- VIM辅助的方法提高了模型的解释性,并有助于理解污染处理动态.
- 这种方法有效地解决了污染企业中不同处置过程的数据分析分离的挑战.
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