{"title":"基于混合深度学习的污水处理系统在线软测量","authors":"Wenjie Mai, Zhenguo Chen, Xiaoyong Li, Xiaohui Yi, Yingzhong Zhao, Xinzhong He, Xiang Xu, Mingzhi Huang","doi":"10.1007/s11783-024-1780-y","DOIUrl":null,"url":null,"abstract":"<p>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 <i>R</i><sup>2</sup> 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 <i>R</i><sup><i>2</i></sup> 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.</p>","PeriodicalId":12720,"journal":{"name":"Frontiers of Environmental Science & Engineering","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Online soft measurement for wastewater treatment system based on hybrid deep learning\",\"authors\":\"Wenjie Mai, Zhenguo Chen, Xiaoyong Li, Xiaohui Yi, Yingzhong Zhao, Xinzhong He, Xiang Xu, Mingzhi Huang\",\"doi\":\"10.1007/s11783-024-1780-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 <i>R</i><sup>2</sup> 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 <i>R</i><sup><i>2</i></sup> 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.</p>\",\"PeriodicalId\":12720,\"journal\":{\"name\":\"Frontiers of Environmental Science & Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2023-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers of Environmental Science & Engineering\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1007/s11783-024-1780-y\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ENVIRONMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Environmental Science & Engineering","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s11783-024-1780-y","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
Online soft measurement for wastewater treatment system based on hybrid deep learning
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.
期刊介绍:
Frontiers of Environmental Science & Engineering (FESE) is an international journal for researchers interested in a wide range of environmental disciplines. The journal''s aim is to advance and disseminate knowledge in all main branches of environmental science & engineering. The journal emphasizes papers in developing fields, as well as papers showing the interaction between environmental disciplines and other disciplines.
FESE is a bi-monthly journal. Its peer-reviewed contents consist of a broad blend of reviews, research papers, policy analyses, short communications, and opinions. Nonscheduled “special issue” and "hot topic", including a review article followed by a couple of related research articles, are organized to publish novel contributions and breaking results on all aspects of environmental field.