Research on the membrane fouling diagnosis of MBR membrane module based on ECA-CNN

Shi, Yaoke; Wang, Zhiwen; Du, Xianjun; Ling, Guobi; Jia, Wenchao; Lu, Yanrong

doi:10.1016/j.jece.2022.107649

Cited by 23 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to be able to better illustrate the comprehensive performance of the network model in this paper, this paper will be compared with the PV array fault diagnosis methods based on deep learning research proposed in recent years (CNN [39], CBAM-CNN [40]). At the same time, in order to avoid the influence of experimental randomness, the actual test experiments are run five times after taking the average value of each evaluation index as the analysis value, and the summary results are shown in table 6.…”

Section: Real Experiments and Results Analysismentioning

confidence: 99%

Photovoltaic arrays fault diagnosis based on an improved dilated convolutional neural network with feature-enhancement

Gong,

An,

Shi

2023

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Photovoltaic (PV) arrays are installed outdoors and prone to abnormalities and various faults under harsh natural conditions, reducing power conversion efficiency and the life of the PV modules, and even causing electric shock and fire. Current fault diagnosis methods are unable to accurately identify and locate faults in PV arrays in PV power systems, leading to increased operation and maintenance costs. Therefore, the feature-enhancement improved dilated convolutional neural network is proposed for fault diagnosis of PV arrays in this paper. Firstly, aim at the problem of information loss due to data structure and spatial hierarchy within the traditional convolutional neural network, and the loss of data after down-sampling, which leads to the inability to reconstruct information, a dilated convolution (DC) is introduced to obtain a larger perceptual field while reducing the computational effort. Meanwhile, the adaptive dual domain soft threshold group convolution attention module (ADSGA) is proposed to enhance the essential features of faults and reduce the information redundancy given the ambiguity and blindness of the feature data in PV array fault extraction. Finally, the model performance of the proposed model is validated and the operability and effectiveness of the proposed method are verified experimentally. The diagnostic results show that the average diagnostic accuracy of the proposed model is 98.95% compared with other diagnostic models, with better diagnostic accuracy and more stable diagnostic performance.

show abstract

Section: Real Experiments and Results Analysismentioning

confidence: 99%

Photovoltaic arrays fault diagnosis based on an improved dilated convolutional neural network with feature-enhancement

Gong,

An,

Shi

2023

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Studies have revealed that the channel attention mechanism effectively enhances the performance of neural networks ( Shi et al., 2022 ). However, existing attention modules often exhibit complexity, which can lead to the problem of model overfitting.…”

Section: Methodsmentioning

confidence: 99%

Efficient residual network using hyperspectral images for corn variety identification

Li,

Zhai,

Zheng

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.

show abstract

“…The properties of the membrane mainly include membrane materials, surface roughness, and the pore value of the membrane. Operating conditions include temperature, aeration, sludge retention time (SRT), and hydraulic retention time (HRT) [ 34 ]. The characteristics of sludge mixtures include total suspended solid (TSS), sludge load, mixed liquor suspended solids (MLSS), microbial products such as soluble microbial products (SMP), and extracellular polymeric substances (EPS).…”

Section: Membrane Fouling Prediction Modelmentioning

confidence: 99%

Membrane Fouling Prediction Based on Tent-SSA-BP

et al. 2022

Self Cite

View full text Add to dashboard Cite

In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent–PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value.

show abstract

Research on the membrane fouling diagnosis of MBR membrane module based on ECA-CNN

Cited by 23 publications

References 35 publications

Photovoltaic arrays fault diagnosis based on an improved dilated convolutional neural network with feature-enhancement

Photovoltaic arrays fault diagnosis based on an improved dilated convolutional neural network with feature-enhancement

Efficient residual network using hyperspectral images for corn variety identification

Membrane Fouling Prediction Based on Tent-SSA-BP

Contact Info

Product

Resources

About