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<p>Sludge dewatering is a critical stage in sludge treatment, directly influencing the efficiency and environmental risks of subsequent disposal. This study systematically investigated the optimization of sludge dewatering by analyzing the matching relationship between the pore size of filter media materials and sludge particle size. The equivalent pore sizes of materials (e.g., nylon fabrics, geotextiles) were determined using nuclear magnetic resonance (NMR) and a machine learning-based image analysis method (IAM). Model experiments evaluated their dewatering performance. Results demonstrated that NMR, based on transverse relaxation time (T₂) inversion, achieved high accuracy for hydrophilic multilayer materials like geotextiles (errors &lt; 11%), while IAM showed ± 10% error for hydrophobic single-layer materials (e.g., nylon fabrics) but up to 53.91% error for geotextiles due to complex fiber interweaving. Optimal dewatering was achieved when the filter media pore size (O95) matched the sludge’s volume-mean particle size (D[4,3]). The 400-g/m<sup>2</sup> geotextile (pore size: 65 μm) exhibited the best alignment with the sludge’s D[4,3] (63.54 μm), yielding a relatively low cake moisture content and significantly reduced filtrate turbidity. This study provides a theoretical foundation for filter media selection and highlights the importance of pore size-particle size matching in enhancing dewatering efficiency, offering practical guidance for engineering applications.</p>

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Optimizing Sludge Dewatering Efficiency Through Pore Size-Particle Size Matching: A Multimodal NMR and Machine Learning Approach

  • 期刊论文

  • 发表日期：

    2025年8月26日

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  • Yongzheng Qi,
  • Chen Jin,
  • Zhongyang Xu,
  • Haoqing Xu,
  • Tao Wu,
  • Junhao Zhang

摘要

Sludge dewatering is a critical stage in sludge treatment, directly influencing the efficiency and environmental risks of subsequent disposal. This study systematically investigated the optimization of sludge dewatering by analyzing the matching relationship between the pore size of filter media materials and sludge particle size. The equivalent pore sizes of materials (e.g., nylon fabrics, geotextiles) were determined using nuclear magnetic resonance (NMR) and a machine learning-based image analysis method (IAM). Model experiments evaluated their dewatering performance. Results demonstrated that NMR, based on transverse relaxation time (T₂) inversion, achieved high accuracy for hydrophilic multilayer materials like geotextiles (errors < 11%), while IAM showed ± 10% error for hydrophobic single-layer materials (e.g., nylon fabrics) but up to 53.91% error for geotextiles due to complex fiber interweaving. Optimal dewatering was achieved when the filter media pore size (O95) matched the sludge’s volume-mean particle size (D[4,3]). The 400-g/m2 geotextile (pore size: 65 μm) exhibited the best alignment with the sludge’s D[4,3] (63.54 μm), yielding a relatively low cake moisture content and significantly reduced filtrate turbidity. This study provides a theoretical foundation for filter media selection and highlights the importance of pore size-particle size matching in enhancing dewatering efficiency, offering practical guidance for engineering applications.