A monitoring technique for bioreactors based on machine vision

ZHANG Liang; YANG Bowen; LIU Yuheng; ZHANG Yu; GAO Yu; LI Jiacheng; LI Junchen; LIN Sijie

doi:10.13205/j.hjgc.202503001

Volume 43 Issue 3

Mar. 2025

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2025 > 43(3): 1-10

ZHANG Liang, YANG Bowen, LIU Yuheng, ZHANG Yu, GAO Yu, LI Jiacheng, LI Junchen, LIN Sijie. A monitoring technique for bioreactors based on machine vision[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(3): 1-10. doi: 10.13205/j.hjgc.202503001

Citation:

ZHANG Liang, YANG Bowen, LIU Yuheng, ZHANG Yu, GAO Yu, LI Jiacheng, LI Junchen, LIN Sijie. A monitoring technique for bioreactors based on machine vision[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(3): 1-10. doi: 10.13205/j.hjgc.202503001

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A monitoring technique for bioreactors based on machine vision

doi: 10.13205/j.hjgc.202503001

1. National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, College of Environmental Science and Engineering, Beijing University of Technology, Beijing 100124, China;
2. School of Environment Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China;
3. Engineering Innovation Center of SUSTech (Beijing), Southern University of Science and Technology, Beijing 100083, China

Received Date: 2024-11-29
Accepted Date: 2025-01-03
Rev Recd Date: 2024-12-10

Available Online: 2025-06-07

Publish Date: 2025-03-01

Abstract

Abstract

In the operation and management of wastewater treatment plants， the online monitoring of bioreactors plays a critical role in the stable operation of the entire process. Currently， the monitoring of bioreactors mainly relies on flow meters and sensors to obtain relevant data， supplemented by manual inspection and comprehensive judgment. However， this traditional monitoring mode has certain limitations. Manual inspection consumes a lot of manpower， materials and time， and due to the intermittent nature of manual operation， it is difficult to ensure the continuity of monitoring. Furthermore， manual judgment may be influenced by subjective factors， thus affecting the accuracy of monitoring to a certain extent. Therefore， this study proposed a novel monitoring program based on machine vision， and set the aeration volume prediction as the core objective to examine the feasibility of this technical program in practical application. In the specific experimental process， a lab-scale bioreactor biochemical tank was selected as the prediction object， with an aeration volume in the range of 1 L/min to 5 L/min. Firstly， the camera was used to collect the aeration images of the bioreactor， which covered various state information of the tank affected by different aeration volumes. Subsequently， a special database was constructed to systematically organize and store the collected image data for subsequent analysis and processing. Then， the advanced convolutional neural network technology was used to extract features from the image data， and the key feature information closely related to the aeration volume was mined out. Finally， a corresponding model was established based on these extracted features to form a complete monitoring framework， realizing the accurate perception of aeration volume in the wastewater treatment process. The analysis of the model revealed that the prediction accuracy of the test set was as high as 99%， which fully demonstrated the high stability of the model and fully met the actual needs of automatic monitoring of wastewater treatment plants. In order to further expand the application scope of this technical program， this study also focused on the feasibility of migrating the machine vision technology from the lab-scale bioreactor to the pilot-scale bioreactor， which strongly proved that the method had good feasibility in both， and showed the good application potential of the machine vision technology in the field of wastewater treatment. In the whole research process， the using of hardware （camera） and software （machine learning model）， not only efficiently completed the task of online monitoring of the aeration volume， but also sent out alarm signals in time when abnormalities were detected. The realization of such a program could partially replace the traditional manual inspection work， greatly reducing the manual workload， while improving the efficiency and accuracy of monitoring. Thus， some valuable and feasible ideas for the development of wastewater treatment plants in intelligent operation were put forward in the hope of promoting the technological upgrading and innovation of the entire wastewater treatment industry.
- bioreactor monitoring,
- aeration volume,
- image data,
- machine vision,
- visualization

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References(33)

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