Optimizing Biogas Power Plants through Machine-Learning-Aided Rotor Configuration †
Abstract
:1. Introduction
2. Methodology
2.1. Computational Fluid Dynamics Single Case Configuration
2.2. Computational Fluid Dynamics Case Batch
- Rotor Design: Rotor types can vary widely from plant to plant. In this work, different rotor designs were supported by adding chosen rotor geometries to the simulation case.
- Rotor Speed Variation: Each subsequent batch of simulations explored different rotor speeds. This approach allowed us to analyze the interplay between rotor speed and placement, enhancing our understanding of their collective influence on the reactor’s mixing efficiency.
- Rotor Placement Strategy: The strategic placement of the rotor was systematically varied for each set of simulations, known as a batch. Within each batch, the rotor speed remained constant to isolate the effect of placement on fluid dynamics.
2.3. Evaluation Method
2.4. Shallow Learning
- Vessel Diameter and Height: These parameters define the physical constraints within which the rotors operate.
- Rotor Speed: This is a direct influencer of the agitation intensity within the vessel.
- Rotor Type: Different rotor designs can have significantly different impacts on mixing.
- Rotor Position (X, Y, Z): The spatial positioning of the rotor within the vessel which crucially affects the flow patterns and effective mixing.
- Rotor Angle (, , ): These angles define the orientation of the rotor, further refining the model’s understanding of how rotors interact with the fluid medium.
- First Hidden Layer: Consists of 128 neurons. This layer was designed to capture a broad spectrum of features and interactions from the input data. The ReLU activation function is used.
- Second Hidden Layer: Contains 64 neurons. It also uses the ReLU activation function.
2.5. Recommendation
3. Setup
3.1. Simulation Cases
3.2. Computation Post-Processing
4. Results
4.1. Training
4.2. Model Test
- Vessel Diameter: 10 m;
- Vessel Height: 3 m;
- Rotor Type: Landia POP [8];
- Rotor Angle: 0° on each axis.
- Rotor Speed: Either 7 or 15 rad s−1, which equals around 67 or 143 min−1, respectively.
- Rotor Positions: Possible positions within the cylindrical vessel with 1 m of granularity.
5. Discussion
5.1. Model Evaluation
5.2. Mixing Score Evaluation
5.3. Rotor Angle
6. Future Work
6.1. Neural Network
6.2. Training Data
6.3. Comparing to Other Approaches
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
NN | Neural network |
CFD | Computational Fluid Dynamics |
ReLU | Rectified Linear Unit |
AMI | Arbitrary Mesh Interfaces |
References
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X/m | Y/m | Z/m |
---|---|---|
−1 | −3 | 0 |
−1 | −1 | 0 |
−1 | 1 | 0 |
−1 | 3 | 0 |
1 | −1 | 0 |
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Heller, A.; Pomares, H.; Glösekötter, P. Optimizing Biogas Power Plants through Machine-Learning-Aided Rotor Configuration. Eng. Proc. 2024, 68, 46. https://doi.org/10.3390/engproc2024068046
Heller A, Pomares H, Glösekötter P. Optimizing Biogas Power Plants through Machine-Learning-Aided Rotor Configuration. Engineering Proceedings. 2024; 68(1):46. https://doi.org/10.3390/engproc2024068046
Chicago/Turabian StyleHeller, Andreas, Héctor Pomares, and Peter Glösekötter. 2024. "Optimizing Biogas Power Plants through Machine-Learning-Aided Rotor Configuration" Engineering Proceedings 68, no. 1: 46. https://doi.org/10.3390/engproc2024068046