Membrane Fouling Diagnosis of Membrane Components Based on MOJS-ADBN

Area of Science:

• Environmental Engineering
• Water Treatment Technologies
• Artificial Intelligence in Engineering

Background:

• Membrane fouling in water treatment presents significant challenges due to strong nonlinearity and time-varying characteristics.
• Accurate diagnosis of membrane component fouling is crucial for maintaining efficient water treatment processes.

Purpose of the Study:

• To propose a novel membrane fouling diagnosis method using a multi-objective jellyfish search adaptive deep belief network (MOJS-ADBN).
• To enhance the convergence speed and feature extraction capabilities for improved diagnostic accuracy in membrane water treatment.

Main Methods:

• Introduced adaptive learning rates into the unsupervised pre-training phase of Deep Belief Networks (DBN) to accelerate convergence.
• Employed the Multi-Objective Jellyfish Search (MOJS) algorithm for weight fine-tuning, replacing traditional gradient-based methods to improve feature extraction.
• Proved the convergence of the MOJS-ADBN learning process using Lyapunov functions.

Main Results:

• The MOJS-ADBN demonstrated a fast convergence speed in diagnosing membrane fouling.
• The proposed method achieved high diagnostic accuracy for membrane components.
• Experimental validation confirmed the effectiveness of MOJS-ADBN in membrane packaging diagnosis.

Conclusions:

• MOJS-ADBN offers a robust and accurate solution for membrane fouling diagnosis in real-time water treatment operations.
• The method provides a valuable theoretical foundation for addressing fouling issues in membrane systems.
• This approach contributes to the optimization and reliability of membrane-based water purification.