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Batch settling curve registration via image data modeling.
Nicolas Derlon1, Christian Thürlimann1, David Dürrenmatt2
1Eawag, Department Process Engineering, Überlandstrasse 133, CH-8600 Dübendorf, Switzerland; Institute of Environmental Engineering, ETH Zürich, CH-8093 Zürich, Switzerland.
Water Research
|March 9, 2017
Summary
Automated image analysis enables accurate sludge blanket height and settling velocity measurement. This simplifies secondary settling tank design and operation, moving beyond conservative methods.
Area of Science:
- Environmental Engineering
- Water Treatment Technologies
- Wastewater Management
Background:
- Characterizing sludge settling properties is crucial for secondary settling tank design and operation.
- Current methods are often time-consuming and challenging, necessitating conservative design approaches.
- Reliable sludge settling data is essential for optimizing wastewater treatment processes.
Purpose of the Study:
- To develop and validate an automated method for sludge blanket height registration and zone settling velocity estimation.
- To demonstrate the feasibility of using off-the-shelf components and publicly released software for practical applications.
- To introduce a novel multivariate shape constrained spline model for accurate image analysis of sludge settling.
Main Methods:
- Batch settling experiments were conducted using an experimental setup with readily available components.
- Image analysis was employed to automatically track sludge blanket height over time.
- A multivariate shape constrained spline model was utilized for precise sludge blanket profile registration.
- Zone settling velocity was estimated from the registered sludge blanket height data.
Main Results:
- Automated sludge blanket height registration was successfully achieved through image analysis.
- Accurate estimation of zone settling velocity was demonstrated using the developed method.
- The experimental setup proved practical, requiring no moving parts and utilizing accessible software.
- The multivariate shape constrained spline model showed promise for reliable sludge blanket height profiling.
Conclusions:
- Automated image analysis offers a reliable and efficient alternative for characterizing sludge settling properties.
- The proposed method can improve the design and operation of secondary settling tanks, reducing the need for conservative approaches.
- The use of off-the-shelf components and publicly available software makes this technique widely applicable in practical settings.
