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Artificial neural network based modeling for the prediction of yield and surface area of activated carbon from biomass
Author(s) -
Liao Mochen,
Kelley Stephen S,
Yao Yuan
Publication year - 2019
Publication title -
biofuels, bioproducts and biorefining
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.931
H-Index - 83
eISSN - 1932-1031
pISSN - 1932-104X
DOI - 10.1002/bbb.1991
Subject(s) - artificial neural network , biomass (ecology) , raw material , yield (engineering) , activated carbon , pyrolysis , process engineering , environmental science , pulp and paper industry , adsorption , biological system , computer science , machine learning , engineering , materials science , chemistry , waste management , composite material , agronomy , organic chemistry , biology
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 ( R 2 > 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