利用深度卷积神经网络与迁移学习从显微图像评估活性污泥沉降特性
Evaluation of Activated Sludge Settling Characteristics from Microscopy Images with Deep Convolutional Neural Networks and Transfer Learning
AI总结：
本研究提出基于深度卷积神经网络和迁移学习的计算机视觉方法，通过显微图像中絮体与丝状菌形态评估活性污泥沉降特性，实现客观、一致且可实时应用的预测。
摘要：
微生物群落在生物污水处理过程中起着关键作用。例如，活性污泥沉降特性会受到微生物群落组成的影响，并随污水处理厂（WWTPs）运行条件和进水特性的变化而改变。及时评估和预测导致沉降问题的微生物组成变化，如丝状菌膨胀（FB），可以预防运行挑战、处理效率下降以及不利的环境影响。本研究提出了一种基于计算机视觉的创新方法，根据显微图像中絮体和丝状菌的形态特性来评估活性污泥沉降特性。该方法通过实现深度卷积神经网络（CNN）模型的迁移学习，旨在克服现有定量图像分析技术的局限性。离线显微图像数据集历时两年收集，每周在比利时一个全尺寸工业污水处理厂采样。采用多种数据增强技术来增强CNN模型的泛化能力。测试了多种CNN架构，包括Inception v3、ResNet18、ResNet152、ConvNeXt-nano和ConvNeXt-S，以评估它们在预测污泥沉降特性方面的性能。污泥体积指数被用作最终预测变量，但该方法可以很容易地调整为预测任何其他选定的沉降指标。结果表明，所提出的基于CNN的方法提供了劳动强度更低、客观且一致的评估，同时迁移学习显著缩短了训练阶段，从而形成了一个可用于实时应用的泛化系统。
英文摘要：
Microbial communities play a key role in biological wastewater treatment processes. Activated sludge settling characteristics, for example, are affected by microbial community composition, varying by changes in operating conditions and influent characteristics of wastewater treatment plants (WWTPs). Timely assessment and prediction of changes in microbial composition leading to settling problems, such as filamentous bulking (FB), can prevent operational challenges, reductions in treatment efficiency, and adverse environmental impacts. This study presents an innovative computer vision-based approach to assess activated sludge-settling characteristics based on the morphological properties of flocs and filaments in microscopy images. Implementing the transfer learning of deep convolutional neural network (CNN) models, this approach aims to overcome the limitations of existing quantitative image analysis techniques. The offline microscopy image dataset was collected over two years, with weekly sampling at a full-scale industrial WWTP in Belgium. Multiple data augmentation techniques were employed to enhance the generalizability of the CNN models. Various CNN architectures, including Inception v3, ResNet18, ResNet152, ConvNeXt-nano, and ConvNeXt-S, were tested to evaluate their performance in predicting sludge settling characteristics. The sludge volume index was used as the final prediction variable, but the method can easily be adjusted to predict any other settling metric of choice. The results showed that the suggested CNN-based approach provides less labour-intensive, objective, and consistent assessments, while transfer learning notably minimises the training phase, resulting in a generalizable system that can be employed in real-time applications.
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