Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in...Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in real time,thus influencing the judgment of the operators and may increase energy consumption and carbon emission.One of the solutions is using a soft-sensor prediction technique.This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit(BiGRU)combined with Gaussian Progress Regression(GPR)optimized by Tree-structured Parzen Estimator(TPE).TPE automatically optimizes the hyperparameters of BiGRU,and BiGRU is trained to obtain the point prediction with GPR for the interval prediction.Then,a case study applying this prediction method for an actual anaerobic process(2500 m^(3)/d)is carried out.Results show that TPE effectively optimizes the hyperparameters of BiGRU.For point prediction of CODeff and biogas yield,R^(2)values of BiGRU,which are 0.973 and 0.939,respectively,are increased by 1.03%–7.61%and 1.28%–10.33%,compared with those of other models,and the valid prediction interval can be obtained.Besides,the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation.It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.展开更多
In this study,metal-organic framework MIL-53(Al)was synthesized and studied to understand the different mechanisms between normal MIL-53(Al)and 2D metal-organic framework MIL-53(Al)for removing fluoride.Comparatively,...In this study,metal-organic framework MIL-53(Al)was synthesized and studied to understand the different mechanisms between normal MIL-53(Al)and 2D metal-organic framework MIL-53(Al)for removing fluoride.Comparatively,the 2D MIL-53(Al)had two-dimensional linear morphology rather than block shape,indicating more expose adsorptive sites than normal MIL-53(Al).The batch adsorption experiments were applied to investigate the performance of 2D MIL-53(Al),including pH,adsorption kinetics,and thermodynamics.The 2D MIL-53(Al)(75.50 mg/g)showed better adsorption capacity than normal MIL-53(Al)(35.63 mg/g).The adsorption process of 2D MIL-53(Al)followed the pseudo-firstorder model and Langmuir model.The adsorption mechanism of this material was further studied by using experimental characterization and density functional theory calculations in detail.The main adsorptive sites were Al and O in the 2D MIL-53(Al),and the relationship between fluoride binding with Al and O was HF^(-)_(2)>HF>F^(-).The species of fluoride were HF2,HF,F at different pH and concentrations.Hence,this study provides a significant way on the application of two-dimensional materials for removing fluoride.展开更多
The existing automated wastewater treatment control systems encounter challenges such as the utilization of specialized testing instruments, equipment repair complications, high operational costs, substantial operatio...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.展开更多
基金This research was supported by the National Natural Science Foundation of China(Nos.41977300 and 41907297)the Science and Technology Program of Guangzhou(China)(No.202002020055)the Fujian Provincial Natural Science Foundation(China)(No.2020I1001).
文摘Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in real time,thus influencing the judgment of the operators and may increase energy consumption and carbon emission.One of the solutions is using a soft-sensor prediction technique.This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit(BiGRU)combined with Gaussian Progress Regression(GPR)optimized by Tree-structured Parzen Estimator(TPE).TPE automatically optimizes the hyperparameters of BiGRU,and BiGRU is trained to obtain the point prediction with GPR for the interval prediction.Then,a case study applying this prediction method for an actual anaerobic process(2500 m^(3)/d)is carried out.Results show that TPE effectively optimizes the hyperparameters of BiGRU.For point prediction of CODeff and biogas yield,R^(2)values of BiGRU,which are 0.973 and 0.939,respectively,are increased by 1.03%–7.61%and 1.28%–10.33%,compared with those of other models,and the valid prediction interval can be obtained.Besides,the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation.It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.
基金This research was financially supported by Key R&D Program of Hunan Province(2018SK2026)the National Key R&D Program of China(2018YFC1802204)+1 种基金National Natural Science Foundation of China(51634010)Talent Cultivation Program of Guangzhou University(RP2021014).
文摘In this study,metal-organic framework MIL-53(Al)was synthesized and studied to understand the different mechanisms between normal MIL-53(Al)and 2D metal-organic framework MIL-53(Al)for removing fluoride.Comparatively,the 2D MIL-53(Al)had two-dimensional linear morphology rather than block shape,indicating more expose adsorptive sites than normal MIL-53(Al).The batch adsorption experiments were applied to investigate the performance of 2D MIL-53(Al),including pH,adsorption kinetics,and thermodynamics.The 2D MIL-53(Al)(75.50 mg/g)showed better adsorption capacity than normal MIL-53(Al)(35.63 mg/g).The adsorption process of 2D MIL-53(Al)followed the pseudo-firstorder model and Langmuir model.The adsorption mechanism of this material was further studied by using experimental characterization and density functional theory calculations in detail.The main adsorptive sites were Al and O in the 2D MIL-53(Al),and the relationship between fluoride binding with Al and O was HF^(-)_(2)>HF>F^(-).The species of fluoride were HF2,HF,F at different pH and concentrations.Hence,this study provides a significant way on the application of two-dimensional materials for removing fluoride.
基金funded by the National Natural Science Foundation of China (Nos. 41977300 and 41907297)the Science and Technology Program of Guangzhou (No. 202002020055)the Fujian Provincial Natural Science Foundation (No. 2020I1001).
文摘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.
