Article
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Compressible Diagnosis of the Membrane Fouling Based on the Transfer Entropy
Version 1
: Received: 8 August 2024 / Approved: 9 August 2024 / Online: 9 August 2024 (12:19:35 CEST)
How to cite: Wu, X.; Hou, D.; Yang, H.; Han, H. Compressible Diagnosis of the Membrane Fouling Based on the Transfer Entropy. Preprints 2024, 2024080707. https://doi.org/10.20944/preprints202408.0707.v1 Wu, X.; Hou, D.; Yang, H.; Han, H. Compressible Diagnosis of the Membrane Fouling Based on the Transfer Entropy. Preprints 2024, 2024080707. https://doi.org/10.20944/preprints202408.0707.v1
Abstract
Membrane fouling caused by many direct and indirect triggering factors has become an obstacle to the application of membrane bioreactor (MBR). The nonlinear relationship between those factors is subject to complex causality or affiliation, which is difficult to clarify for the diagnosis of membrane fouling. To solve this problem, this paper proposes a compressible diagnosis model (CDM) based on transfer entropy to facilitate the fault diagnosis of the root cause for membrane fouling. Firstly, a framework of CDM between membrane fouling and causal variables is built based on a feature extraction algorithm and mechanism analysis. The framework can identify fault transfer scenarios following the changes in operating conditions. Secondly, the fault transfer topology of CDM based on transfer entropy is constructed to describe the causal relationship between variables dynamically. Thirdly, an information compressible strategy is designed to simplify the fault transfer topology. This strategy can eliminate the repetitious affiliation relationship, which contributes to the root causal variables speedily and accurately. Finally, the effectiveness of the proposed CDM is verified by the measured data from an actual MBR.
Keywords
Membrane fouling; diagnosis; causal relationship; root causal variables; transfer entropy
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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