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

IF 6.1 2区 环境科学与生态学 Q2 ENGINEERING, ENVIRONMENTAL
Wenjie Mai, Zhenguo Chen, Xiaoyong Li, Xiaohui Yi, Yingzhong Zhao, Xinzhong He, Xiang Xu, Mingzhi Huang
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引用次数: 0

Abstract

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.

Abstract Image

基于混合深度学习的污水处理系统在线软测量
现有的自动化废水处理控制系统面临着诸如使用专业测试仪器、设备维修复杂、运行成本高、操作错误大、检测精度低等挑战。有效的软测量模型为污水处理过程的实时监测和自动化控制的发展提供了可行的途径。因此,开发了一种新型的混合深度学习CNN-BNLSTM- attention (CBNLSMA)模型,该模型结合了卷积神经网络(CNN)、双向嵌套长短期记忆神经网络(BNLSTM)、注意机制(AM)和树结构Parzen Estimators (TPE),用于监测废水处理过程中的出水水质。CBNLSMA模型分为四个阶段:CNN模块用于特征提取和数据滤波,加快运算速度;BNLSTM模块用于时态数据的时态信息提取;模型权重重分配的AM模块;以及用于CBNLSMA模型超参数搜索优化的TPE优化算法。与其他模型(TPE-CNN-BNLSTM、TPE-BNLSTM-AM、TPE-CNN-AM、PSO-CBNLSTMA)相比,CBNLSMA模型预测出水COD的RMSE降低了25.4%,MAPE降低了32.9%,R2提高了14.9%。对于污水SS预测,CBNLSMA模型比其他模型RMSE降低26.4%,MAPE降低21.0%,R2提高35.7%。仿真结果表明,所提出的CBNLSMA模型在污水处理过程的实时水质监测方面具有很大的潜力,表明其具有很高的自动化控制潜力。
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来源期刊
Frontiers of Environmental Science & Engineering
Frontiers of Environmental Science & Engineering ENGINEERING, ENVIRONMENTAL-ENVIRONMENTAL SCIENCES
CiteScore
10.90
自引率
12.50%
发文量
988
审稿时长
6.1 months
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.
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