Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach
Biomass; Coal; Complex networks; Errors; Forecasting; Gasification; Hydrogen production; Learning algorithms; Mean square error; Neural networks; Regression analysis; Sensitivity analysis; Support vector machines; Co-gasification; Gaussian process regression; Hydrogen-rich syngas; Machine learning a...
Saved in:
Main Authors: | Bahadar A., Kanthasamy R., Sait H.H., Zwawi M., Algarni M., Ayodele B.V., Cheng C.K., Wei L.J. |
---|---|
Other Authors: | 35182494100 |
Format: | Article |
Published: |
Elsevier Ltd
2023
|
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach
by: Bahadar, A., et al.
Published: (2022) -
Hydrogen-Rich Syngas and Biochar Production by Non-Catalytic Valorization of Date Palm Seeds
by: Sait H.H., et al.
Published: (2023) -
Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms
by: Ayodele B.V., et al.
Published: (2023) -
Hydrogen and syngas generation from gasification of coal in an integrated fuel processor
by: Chowdhury, S., et al.
Published: (2014) -
Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed co-gasification of biomass wastes from oil palm
by: Ayodele, B.V., et al.
Published: (2022)