Membrane Fouling Prediction Based on Tent-SSA-BP

Existing Filtration Treatment on Drinking Water Process and Concerns Issues

Water is one of the main sources of life's survival. It is mandatory to have good-quality water, especially for drinking. Many types of available filtration treatment can produce high-quality drinking water. As a result, it is intriguing to determine which treatment is the best. This paper provides a review of available filtration technology specifically for drinking water treatment, including both conventional and advanced treatments, while focusing on membrane filtration treatment. This review covers the concerns that usually exist in membrane filtration treatment, namely membrane fouling. Here, the parameters that influence fouling are identified. This paper also discusses the different ways to handle fouling, either based on prevention, prediction, or control automation. According to the findings, the most common treatment for fouling was prevention. However, this treatment required the use of chemical agents, which will eventually affect human health. The prediction process was usually used to circumvent the process of fouling development. Based on our reviews up to now, there are a limited number of researchers who study membrane fouling control based on automation. Frequently, the treatment method and control strategy are determined individually.

Membrane Fouling Diagnosis of Membrane Components Based on MOJS-ADBN

Given the strong nonlinearity and large time-varying characteristics of membrane component fouling in the membrane water treatment process, a membrane component-membrane fouling diagnosis method based on the multi-objective jellyfish search adaptive deep belief network (MOJS-ADBN) is proposed. Firstly, the adaptive learning rate is introduced into the unsupervised pre-training phase of DBN to improve the convergence speed of the network. Secondly, the MOJS method is used to replace the gradient-based layer-by-layer weight fine-tuning method in traditional DBN to improve the ability of network feature extraction. At the same time, the convergence of the MOJS-ADBN learning process is proven by constructing the Lyapunov function. Finally, MOJS-ADBN is used in the membrane packaging diagnosis to verify the performance of the model diagnosis. The experimental results show that MOJS-ADBN has a fast convergence speed and a high diagnostic accuracy, and can provide a theoretical basis for membrane fouling diagnosis in the actual operation of membrane water treatment.

Fault Diagnosis of Permanent Magnet Synchronous Motor of Coal Mine Belt Conveyor Based on Digital Twin and ISSA-RF

Permanent magnet synchronous motors (PMSMs) have been gradually used as the driving equipment of coal mine belt conveyors. To ensure safety and stability, it is necessary to carry out real-time and accurate fault diagnosis of PMSM. Therefore, a fault diagnosis method for PMSM based on digital twin and ISSA-RF (Improved Sparrow Search Algorithm Optimized Random Forest) is proposed. Firstly, the multi-strategy hybrid ISSA is used to solve the problems of uneven population distribution, insufficient population diversity, low convergence speed, etc. In addition, the fault diagnosis model of ISSA-RF permanent magnet synchronous motor is constructed based on the optimization of the number of Random Forest decision trees and that of features of each node by ISSA. Secondly, considering the operation mechanism and physical properties of PMSM, the relevant digital twin model is constructed and the real-time mapping of physical entity and virtual model is realized through data interactive transmission. Finally, the simulation and experimental results show that the fault diagnosis accuracy of ISSA-RF, 98.2%, is higher than those of Random Forest (RF), Sparrow Search Algorithm Optimized Random Forest (SSA-RF), BP neural network (BP) and Support Vector Machine (SVM), which verifies the feasibility and ability of the proposed method to realize fault diagnosis and 3D visual monitoring of PMSM together with the digital twin model.