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...
详细信息
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.
In the context of low-carbon development, the establishment of a reliable carbon emission prediction model is of great significance for the accurate monitoring of carbon emissions and the formulation and implementatio...
详细信息
ISBN:
(纸本)9798350373486;9798350373479
In the context of low-carbon development, the establishment of a reliable carbon emission prediction model is of great significance for the accurate monitoring of carbon emissions and the formulation and implementation of future carbon reduction goals. This paper proposes a carbon emission prediction method based on information entropy feature selection and tree-structured Parzen estimator optimized neural network. Firstly, the raw data is normalized and the mutual information analysis is conducted on the feature variables to obtain the information entropy between carbon emissions and each feature. Secondly, strong features are selected, and an extreme learning machine network structure is constructed. Thirdly, the tree-structured Parzen estimator is used to optimize the hyperparameters of the network, and the optimal hyperparameters are fed into the extreme learning machine. Additionally, a regularization term is added to the model to prevent overfitting and obtain the prediction of carbon emissions. Finally, the effectiveness of the proposed model is validated on a carbon emission dataset from 120 steel enterprises. The results show that the proposed model, which optimizes the mutual information extreme learning machine with the tree-structured estimator, outperforms the control group in all evaluation indicators, demonstrating its superiority in carbon emission prediction in the steel industry. The fast optimization capability of the tpealgorithm and the high accuracy and robustness of ensemble learning improve the accuracy and stability of the prediction model.
暂无评论