Optimizing Biogas Power Plants through Machine-Learning-Aided Rotor Configuration
Author:
Affiliation:
1. Department of Computer Architecture and Computer Technology, Universidad de Granada, 18014 Granada, Spain
2. Department of Electrical Engineering and Computer Science, FH Münster, 48565 Steinfurt, Germany
Publisher
MDPI
Link
https://www.mdpi.com/2673-4591/68/1/46/pdf
Reference10 articles.
1. Annas, S. (2021). Charakterisierung von Rühr- und Mischprozessen in Nicht-Newtonschen Fluiden am Beispiel von Biogasanlagen mit Paddelrührwerk Berichte des Fachgebiets für Strömungsmechanik, Shaker Verlag.
2. Heller, A., Glösekötter, P., Buntkiel, L., Reinecke, S., and Annas, S. (2023). Sim-to-Real Transfer in Deep Learning for Agitation Evaluation of Biogas Power Plants. Eng. Proc., 39.
3. Numeric Simulation-Based Analysis of the Mixing Process in Anaerobic Digesters of Biogas Plants;Conti;Bioenergy X-Factor,2022
4. Šafarič, L., Yekta, S.S., Ejlertsson, J., Safari, M., Najafabadi, H.N., Karlsson, A., Ometto, F., Svensson, B.H., and Björn, A. (2019). A Comparative Study of Biogas Reactor Fluid Rheology—Implications for Mixing Profile and Power Demand. Processes, 7.
5. Improving the Mixing Performances of Rice Straw Anaerobic Digestion for Higher Biogas Production by Computational Fluid Dynamics (CFD) Simulation;Shen;Appl. Biochem. Biotechnol.,2013
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