All Outputs (3)

Microwave fluidized bed for biomass pyrolysis. Part II: Effect of process parameters (2017)
Journal Article
Adam, M., Beneroso, D., Katrib, J., Kingman, S., & Robinson, J. P. (in press). Microwave fluidized bed for biomass pyrolysis. Part II: Effect of process parameters. Biofuels, Bioproducts and Biorefining, https://doi.org/10.1002/bbb.1781

The microwave fluidized bed process developed in Part I (DOI: 10.1002/bbb.1780), in which the heating heterogeneity issues are overcome, has been applied to the pyrolysis of biomass. The degree of pyrolysis was established by studying the behavior of... Read More about Microwave fluidized bed for biomass pyrolysis. Part II: Effect of process parameters.

Microwave fluidized bed for biomass pyrolysis. Part I: Process design (2017)
Journal Article
Adam, M., Beneroso, D., Katrib, J., Kingman, S., & Robinson, J. P. (in press). Microwave fluidized bed for biomass pyrolysis. Part I: Process design. Biofuels, Bioproducts and Biorefining, https://doi.org/10.1002/bbb.1780

The production of bio-oils from microwave pyrolysis has received increasing attention from bioenergy researchers, but no studies reported to date have proposed reliable solutions to avoid thermal runaway. The motivation of this paper is to develop an... Read More about Microwave fluidized bed for biomass pyrolysis. Part I: Process design.

Deep recurrent neural networks for supernovae classification (2017)
Journal Article
Charnock, T., & Moss, A. (2017). Deep recurrent neural networks for supernovae classification. Astrophysical Journal, 837(2), https://doi.org/10.3847/2041-8213/aa603d

We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to t... Read More about Deep recurrent neural networks for supernovae classification.