Artificial neural network based modeling for the prediction of yield and surface area of activated carbon from biomass

Activated carbon (AC) is an adsorbent material with broad industrial applications. Understanding and predicting the yield and quality of AC produced from different feedstock is critical for biomass screening and process design. In this study, multi‐layer feedforward artificial neural network (ANN) m...

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Bibliographic Details
Published in:Biofuels, bioproducts and biorefining bioproducts and biorefining, 2019-07, Vol.13 (4), p.1015-1027
Main Authors: Liao, Mochen, Kelley, Stephen S, Yao, Yuan
Format: Article
Language:English
Subjects:
Activated carbon
Analysis
artificial neural network
Artificial neural networks
Biomass
Industrial applications
lignocellulosic biomass
Model accuracy
Neural networks
Organic chemistry
Predictions
Pyrolysis
Raw materials
Steam
steam activation
Surface area
Yield
Yields
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Summary:Activated carbon (AC) is an adsorbent material with broad industrial applications. Understanding and predicting the yield and quality of AC produced from different feedstock is critical for biomass screening and process design. In this study, multi‐layer feedforward artificial neural network (ANN) models were developed to predict the total yield and surface area of AC produced from various biomass feedstock using pyrolysis and steam activation. In total, 168 data samples identified from experiments in literature were used to train, validate, and test the ANN models. The trained ANN models showed high accuracy (R2 > 0.9) and demonstrated good alignment with the independent experimental data. The impacts of using datasets based on different biomass characterization methods (i.e., ultimate analysis and proximate analysis) were evaluated and compared. Finally, a contribution analysis was conducted to understand the impact of different process factors on AC yield and surface area. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd
ISSN:1932-104X
1932-1031
DOI:10.1002/bbb.1991