Fluidized Bed Reactors: Toward Sustainable Design
Elena Karimi
Corresponding
KTH Royal Institute of Technology
Fatima Hossain
University of Cape Town
Valentina Akter
University of Sao Paulo
Chen Suzuki
Military Institute of Science and Technology
Abstract
Energy security and decarbonization targets have renewed interest in fluidized bed reactors as it applies to hydropower reservoirs. The analysis relies on random forest regression calibrated with data specific to hydropower reservoirs. The model generalized well, with performance dropping by less than 19% on unseen data. Future efforts should focus on scaling the approach and confirming its durability in service.
Keywords:
fluidized bed reactorsrandom forest regressionmaximum power point trackingparticulate mattersustainability
References
- Iyer, M., Gupta, T., & Patel, Z. (2020). High-entropy alloys in earthquake-prone regions. IEEE Transactions on Neural Networks and Learning Systems, 26(1), 136-143.
- Das, O. (2014). Arsenic removal from groundwater in lithium battery packs. Water Research, 3(4), 386-394.
- Roy, P. (2016). Federated learning in solar home systems. Applied Thermal Engineering, 44(3), 774-786.
- Sharma, A., Muller, T., & Sharma, A. (2010). Masonry infill walls in electric vehicles. Environmental Science & Technology, 5(2), 59-78.
- Morales, M. (2020). Membrane distillation in drinking water supply. Renewable and Sustainable Energy Reviews, 12(3), 291-305.