Online soft measurement for wastewater treatment system based on hybrid deep learning

扫码查看

原文链接

万方数据
维普

外文摘要：The existing automated wastewater treatment control systems encounter challenges such as the utilization of specialized testing instruments,equipment repair complications,high operational costs,substantial operational errors,and low detection accuracy.An effective soft measure model offers a viable approach for real-time monitoring and the development of automated control in the wastewater treatment process.Consequently,a novel hybrid deep learning CNN-BNLSTM-Attention(CBNLSMA)model,which incorporates convolutional neural networks(CNN),bidirectional nested long and short-term memory neural networks(BNLSTM),attention mechanisms(AM),and Tree-structure Parzen Estimators(TPE),has been developed for monitoring effluent water quality during the wastewater treatment process.The CBNLSMA model is divided into four stages:the CNN module for feature extraction and data filtering to expedite operations;the BNLSTM module for temporal data's temporal information extraction;the AM module for model weight reassignment;and the TPE optimization algorithm for the CBNLSMA model's hyperparameter search optimization.In comparison with other models(TPE-CNN-BNLSTM,TPE-BNLSTM-AM,TPE-CNN-AM,PSO-CBNLSTMA),the CBNLSMA model reduced the RMSE for effluent COD prediction by 25.4％,decreased the MAPE by 32.9％,and enhanced the R2 by 14.9％.For the effluent SS prediction,the CBNLSMA model reduced the RMSE by 26.4％,the MAPE by 21.0％,and improved the R2 by 35.7％compared to other models.The simulation results demonstrate that the proposed CBNLSMA model holds significant potential for real-time effluent quality monitoring,indicating its high potential for automated control in wastewater treatment processes.

外文关键词：

Prediction modelSoft measurementCNN-BNLSTM-AM modelTPE optimization algorithm

作者：

Wenjie Mai、Zhenguo Chen、Xiaoyong Li、Xiaohui Yi、Yingzhong Zhao、Xinzhong He、Xiang Xu、Mingzhi Huang

展开 >

作者单位：

SCNU Environmental Research Institute,Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment,School of Environment,South China Normal University,Guangzhou 510006,China

SCNU Qingyuan Institute of Science and Technology Innovation Co.Ltd.,Qingyuan 511517,China

Fujian Environmental Protection Design Institute Co.Ltd.,Fuzhou 350000,China

Huashi(Fujian)Environment Technology Co.Ltd.,Quanzhou,362001,China

Econ Technology Co.Ltd.,Yantai 265503,China

展开 >

基金：

国家自然科学基金国家自然科学基金广州市科技计划福建省自然科学基金

项目编号：

41977300419072972020020200552020I1001

出版年：

2024

DOI：

10.1007/s11783-024-1780-y

环境科学与工程前沿

高等教育出版社

环境科学与工程前沿

影响因子：0.545

ISSN：2095-2201

年,卷(期)：2024.18(2)

参考文献量52