Simulation and Computational Study of CFD on Tube MBR Membrane Assembly

Abstract

When using tubular MBR to treat sewage, the water production is an important parameter to measure the efficiency of the tubular MBR system. The problem to be solved in this paper is to calculate the water yield of the tubular MBR system, so as to evaluate the sewage treatment efficiency of the MBR system. This research uses the CFD simulation software ANSYS 16.0 to study the water yield of the tubular MBR system. The MBR model of a single membrane filament tube was established using the ICEM CFD preprocessor in ANSYS 16.0, and the structured grid was divided to obtain a grid file. Then, the fluid solver was used to solve the mesh file and through the flow monitoring window to obtain the water output of the tubular MBR system. Finally, the CFD postprocessor in ANSYS 16.0 was used to visualize the calculation results and compare them with the waste-water treatment results of some actual MBR systems. The results show that the water yield calculated by the fluent solver is basically the same as that of the actual MBR system. This research realizes the purpose of calculating the water yield of the tubular MBR system with CFD technology, solves the problem of evaluating the working efficiency of the tubular MBR system with water consumption, and realizes the MBR before deployment The evaluation of the working efficiency of the system has certain reference value for the planning, design, and deployment of MBR.

Improved Double‐Layer Structure Multilabel Classification Model via Optimal Sequence and Attention Mechanism

“…Te multilabel classifcation includes two common methods: problem transformation process and algorithm adaptation process. Te former changes a multilabel problem into one or several single-label issues [11] and uses basic classifcation algorithms, such as Naive Bayesian, supporting vector machine [14], k-nearest neighbor algorithm, and so on to solve them. Te latter transforms the existing algorithms so that they can solve the multiclassifcation problem, e.g., ML-RBF method [15,16], ML-kNN approach [17,18], rank-SVM classifcation [9], and associated classifcation algorithm [19,20].…”

“…However, BR is often overlooked because it cannot efectively use the correlation between labels. Te MBR based on BR was proposed [11], which was constructed as a two-layer model. Layer one in MBR is taken as the input of layer two as a sample attribute to consider label correlation.…”

“…Tere are many existing problem transformation methods, such as the BR method [9], the CC theory [10], the MBR model [11], and the DLMC-OS algorithm [12]. However, these methods usually ignore the correlation among labels, the randomness of label sequences, and the redundant interactive label information, which reduces the accuracy of classifcation.…”