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 mAP50_box and mAP50_mask 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.

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Tank containers are intermodal containers for the transport of liquids, gases, and powders as bulk cargo However, hazardous vibration during transportation would generate fatigue damage at discharge area. This thesis aims at calculating fatigue damage at discharge area of a specified tank container.

The calculation is performed in time domain. With FEM models and measured input, hot spot stress is calculated for each hot spot. Probability distributions projects the stress distribution to a longer period thus fatigue damage for 20 years is calculated.

The available data consists of acceleration and velocity of the container during a typical train transportation. A global model and a local model are established with FEM software package. Experimental data from the Lloyd register are used to verify the global model.

In conclusion, during the typical transportation period, fatigue will occur at one of the hotspots.