The issue of membrane fouling poses a significant challenge to the extensive adoption of nanofiltration membrane technology in public water supply systems. The occurrence of bottlenecks is a common issue in the implementation of nanofiltration production. The mitigation of fouling in nanofiltration membranes has emerged as a significant research focus within the water treatment field. Numerous scholars have dedicated their efforts to researching the mechanisms of membrane fouling and constructing intricate mathematical and physical models to explain the relevant mechanisms. The goal of these endeavors is to achieve long-term stability and high throughput operation of nanofiltration processes. However, traditional mathematical models rely on simplifying assumptions and are less likely to capture the dynamics of membrane contamination in practical applications. Machine learning is rapidly emerging as a novel approach for predicting membrane fouling, owing to the rapid progress in artificial intelligence. Machine learning can autonomously learn from historical data, fully harness the value of data, and comprehend the inherent correlation between membrane pollution and various influencing factors. This makes it possible to predict trends in pollution and even facilitates autonomous decision-making, automatic adjustment, and optimization of membrane process flow. Ultimately, it helps create an intelligent water ecosystem. The present study incorporates both local and international research to examine the key factors that contribute to nanofiltration membrane fouling. This review focuses on the influence of raw water quality parameters, membrane material characteristics, and operating conditions. Additionally, this paper presents a comprehensive analysis of the application of machine learning techniques in predicting nanofiltration membrane fouling. 

Research on the factors influencing nanofiltration membrane fouling and the prediction of membrane fouling

Distributed energy power prediction of the variational modal decomposition and Gated Recurrent Unit optimization model based on the whale algorithm

Aiming at the current situation of high temperature of transformer components and high accident rate of transformer bushing caused by closed environment and poor heat dissipation performance of box-type transformer, a prediction method of high-voltage bushing temperature of box-type transformer based on extreme learning machine (ELM) is proposed. Firstly, the sliding window method is used to construct the sample sets. Then, a transformer high-voltage bushing temperature prediction model based on ELM is established. ELM model has fast learning speed and good generalization performance. Finally, taking the relevant monitoring data of high-voltage bushing of a box-type transformer as an example, the field data are preprocessed. The effectiveness of the proposed method is verified through analysis, and the effects of the number of hidden layer neurons, activation function and sliding window width on the prediction effect are compared and analyzed.

Temperature Prediction of Box-type Transformer High-Voltage Bushing Based on ELM

Aiming at the current situation of high temperature of transformer components and high accident rate of transformer bushing caused by closed environment and poor heat dissipation performance of box-type transformer, a prediction method of high-voltage bushing temperature of box-type transformer based on extreme learning machine (ELM) is proposed. Firstly, the sliding window method is used to construct the sample sets. Then, a transformer high-voltage bushing temperature prediction model based on ELM is established. ELM model has fast learning speed and good generalization performance. Finally, taking the relevant monitoring data of high-voltage bushing of a box-type transformer as an example, the field data are preprocessed. The effectiveness of the proposed method is verified through analysis, and the effects of the number of hidden layer neurons, activation function and sliding window width on the prediction effect are compared and analyzed. 

As an indispensable power equipment in the power system, power transformers are widely used in various occasions. As an important hub in the system, its operation is related to people's production and life. As one of the influencing factors representing the condition of the transformer, predicting the top oil temperature of the transformer is critical to monitoring the transformer operation. In this paper, by using LSTM network for oil temperature prediction and increasing the hyperparameters in Bayesian optimization neural network, the optimized LSTM network can obtain smaller error ad prediction accuracy compared with the existing prediction strategy, which can perform more effective and fast prediction. Realize the early warning function for abnormal transformer status.

Prediction of Transformer Top Oil Temperature Based on Bayesian Optimization and LSTM Neural Network

In order to accurately predict the oil temperature of the transformer top layer, based on the traditional neural network algorithm, this paper proposes a transformer top oil temperature prediction method based on VMD (Variational Modal Decomposition) and GRU (Gated Recurrent Unit) neural network. Traditional prediction methods have less consideration of the interaction between different trend items in the oil temperature series, and the oil temperature prediction problem is a time series problem, so the historical oil temperature at the previous time also needs to be considered. In response to the above problems, VMD is introduced to decompose the original sequence into different modalities, and then use the superiority and efficiency of the GRU neural network in timing analysis to establish an oil temperature prediction model, which can avoid the gradient disappearance problem and ensure Prediction accuracy. The results of the calculation examples show that, compared with the traditional prediction method, this method has higher prediction accuracy and higher efficiency than the traditional neural network method.

Prediction method of transformer top oil temperature based on VMD and GRU neural network

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Prediction method of transformer top oil temperature based on VMD and GRU neural network