What is it about?
The paper introduces AI methods, i.e. genetic algorithms and artificial neural networks approach for modelling of one of the most popular and promising technology of hydrogen production via CaO sorption from sawdust biomass in flliudized bed (FB) and circulating fluidized bed (CFB) gasifiers.
Featured Image
Why is it important?
To the best our knowledge, there are no reports on model research of hydrogen concentrations in syngas by the bio-inspired artificial intelligence methods. The use of both FB and VFB units for H2 generation from biomass is also interesting and timely subject.
Perspectives
The developed model can be easily employed by scientists and engineers for optimizations purposes of H2 production processes in FB and CFB gasifiers.
Jaroslaw Krzywanski
Jan Dlugosz University in Czestochowa
Read the Original
This page is a summary of: Genetic algorithms and neural networks in optimization of sorbent enhanced H 2 production in FB and CFB gasifiers, Energy Conversion and Management, September 2018, Elsevier,
DOI: 10.1016/j.enconman.2018.06.098.
You can read the full text:
Resources
Contributors
The following have contributed to this page
