TY - GEN
T1 - Application of artificial neural networks in prediction of pyrolysis behavior for algal mat (LABLAB) biomass
AU - Mayol, Andres Philip
AU - Maningo, Jose Martin Z.
AU - Chua-Unsu, Audrey Gayle Alexis Y.
AU - Felix, Charles B.
AU - Rico, Patricia I.
AU - Chua, Gundelina S.
AU - Manalili, Eduardo V.
AU - Fernandez, Dalisay Dg
AU - Cuello, Joel L.
AU - Bandala, Argel A.
AU - Ubando, Aristotle T.
AU - Madrazo, Cynthia F.
AU - Dadios, Elmer
AU - Culaba, Alvin B.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Pyrolysis kinetics is one way to produce bio-oil and biochar from a biomass product. It is a method to harvest clean energy from a biomass product. Moreover, kinetics and thermal composition of the biomass product is essential for pyrolysis design and optimization. However, industrial pyrolysis process is up to 200°C/min and lab scale pyrolysis temperature is up to 100°C/min. In this study, data from thermogravimetric analysis (TGA) has been utilized and gathered to provide data on algal pyrolysis kinetics. To predict the pyrolysis kinetics at a heating rate of 200°C/min, artificial neural networks (ANN) has been utilized. Results show that ANN predicted the outcome of pyrolysis kinetics which had a correlation with heating rates (10°C, 25°C, and 50°C) of the sample. This is quantified by the correlation coefficient during training which is 0.9972. The average fit quality of the derived model with respect to the experimental data is 98.51%. This work can be improved by considering other hyperparameters for the neural network. This work can also be extended to other compounds besides lablab biomass.
AB - Pyrolysis kinetics is one way to produce bio-oil and biochar from a biomass product. It is a method to harvest clean energy from a biomass product. Moreover, kinetics and thermal composition of the biomass product is essential for pyrolysis design and optimization. However, industrial pyrolysis process is up to 200°C/min and lab scale pyrolysis temperature is up to 100°C/min. In this study, data from thermogravimetric analysis (TGA) has been utilized and gathered to provide data on algal pyrolysis kinetics. To predict the pyrolysis kinetics at a heating rate of 200°C/min, artificial neural networks (ANN) has been utilized. Results show that ANN predicted the outcome of pyrolysis kinetics which had a correlation with heating rates (10°C, 25°C, and 50°C) of the sample. This is quantified by the correlation coefficient during training which is 0.9972. The average fit quality of the derived model with respect to the experimental data is 98.51%. This work can be improved by considering other hyperparameters for the neural network. This work can also be extended to other compounds besides lablab biomass.
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U2 - 10.1109/HNICEM.2018.8666376
DO - 10.1109/HNICEM.2018.8666376
M3 - Conference contribution
AN - SCOPUS:85064124700
T3 - 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018
BT - 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018
Y2 - 29 November 2018 through 2 December 2018
ER -