Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method.

Jia, Tianlong; Peng, Zhaoxu; Yu, Jing; Piaggio, Antonella L; Zhang, Shuo; de Kreuk, Merle K

Jia, Tianlong; Peng, Zhaoxu; Yu, Jing; Piaggio, Antonella L; Zhang, Shuo; de Kreuk, Merle K.

Afiliación

Jia T; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, Stevinweg 1, 2628 CN Delft, the Netherlands.
Peng Z; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, Stevinweg 1, 2628 CN Delft, the Netherlands; Zhengzhou University, School of Water Conservancy and Transportation, Kexue Road 100, Zhengzhou 450001, China. Electronic address: pzx@zzu.edu.cn
Yu J; Erasmus University Medical Center, Department of Radiology and Nuclear Medicine, Dr Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
Piaggio AL; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, Stevinweg 1, 2628 CN Delft, the Netherlands.
Zhang S; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, Stevinweg 1, 2628 CN Delft, the Netherlands.
de Kreuk MK; Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, Stevinweg 1, 2628 CN Delft, the Netherlands.

Sci Total Environ ; 951: 175813, 2024 Nov 15.

Article en En | MEDLINE | ID: mdl-39191331

ABSTRACT

ABSTRACT

Investigating the interaction between influent particles and biomass is basic and important for the biological wastewater treatment. The micro-level methods allow for this, such as the microscope image analysis method with the conventional ImageJ processing software. However, these methods are cost and time-consuming, and require a large amount of work on manual parameter tuning. To deal with this problem, we proposed a deep learning (DL) method to automatically detect and quantify microparticles free from biomass and entrapped in biomass from microscope images. Firstly, we introduced a "TU Delft-Interaction between Particles and Biomass" dataset containing labeled microscope images. Then, we built DL models using this dataset with seven state-of-the-art model architectures for a instance segmentation task, such as Mask R-CNN, Cascade Mask R-CNN, Yolact and YOLOv8. The results show that the Cascade Mask R-CNN with ResNet50 backbone achieves promising detection accuracy, with a mAP50box and mAP50mask of 90.6 % on the test set. Then, we benchmarked our results against the conventional ImageJ processing method. The results show that the DL method significantly outperforms the ImageJ processing method in terms of detection accuracy and processing cost. The DL method shows a 13.8 % improvement in micro-average precision, and a 21.7 % improvement in micro-average recall, compared to the ImageJ method. Moreover, the DL method can process 70 images within 1 min, while the ImageJ method costs at least 6 h. The promising performance of our method allows it to offer a potential alternative to examine the interaction between microparticles and biomass in biological wastewater treatment process in an affordable manner. This approach offers more useful insights into the treatment process, enabling further reveal the microparticles transfer in biological treatment systems.

Asunto(s)

Biomasa; Aprendizaje Profundo; Eliminación de Residuos Líquidos; Aguas Residuales; Eliminación de Residuos Líquidos/métodos

Palabras clave

Artificial intelligence; Computer vision; Instance segmentation; Mask R-CNN; Microplastics; Yolact

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Eliminación de Residuos Líquidos / Biomasa / Aguas Residuales / Aprendizaje Profundo Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Países Bajos

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