Application of artificial neural networks in prediction of pyrolysis behavior for algal mat (LABLAB) biomass

Andres Philip Mayol, Jose Martin Z. Maningo, Audrey Gayle Alexis Y. Chua-Unsu, Charles B. Felix, Patricia I. Rico, Gundelina S. Chua, Eduardo V. Manalili, Dalisay Dg Fernandez, Joel L Cuello, Argel A. Bandala, Aristotle T. Ubando, Cynthia F. Madrazo, Elmer Dadios, Alvin B. Culaba

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publication2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538677674
DOIs
StatePublished - Mar 12 2019
Event10th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018 - Baguio City, Philippines
Duration: Nov 29 2018Dec 2 2018

Publication series

Name2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018

Conference

Conference10th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018
CountryPhilippines
CityBaguio City
Period11/29/1812/2/18

Fingerprint

neural network
Biomass
Pyrolysis
heat pump
Neural networks
Kinetics
Heating rate
energy
Thermogravimetric analysis
Chemical analysis

Keywords

  • Artificial Neural Network
  • Kinetic modeling
  • Pyrolysis
  • Thermogravimetric analysis

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Human-Computer Interaction
  • Artificial Intelligence
  • Communication
  • Hardware and Architecture

Cite this

Mayol, A. P., Maningo, J. M. Z., Chua-Unsu, A. G. A. Y., Felix, C. B., Rico, P. I., Chua, G. S., ... Culaba, A. B. (2019). Application of artificial neural networks in prediction of pyrolysis behavior for algal mat (LABLAB) biomass. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018 [8666376] (2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HNICEM.2018.8666376

Application of artificial neural networks in prediction of pyrolysis behavior for algal mat (LABLAB) biomass. / Mayol, Andres Philip; Maningo, Jose Martin Z.; Chua-Unsu, Audrey Gayle Alexis Y.; Felix, Charles B.; Rico, Patricia I.; Chua, Gundelina S.; Manalili, Eduardo V.; Fernandez, Dalisay Dg; Cuello, Joel L; Bandala, Argel A.; Ubando, Aristotle T.; Madrazo, Cynthia F.; Dadios, Elmer; Culaba, Alvin B.

2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018. Institute of Electrical and Electronics Engineers Inc., 2019. 8666376 (2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mayol, AP, Maningo, JMZ, Chua-Unsu, AGAY, Felix, CB, Rico, PI, Chua, GS, Manalili, EV, Fernandez, DD, Cuello, JL, Bandala, AA, Ubando, AT, Madrazo, CF, Dadios, E & Culaba, AB 2019, Application of artificial neural networks in prediction of pyrolysis behavior for algal mat (LABLAB) biomass. in 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018., 8666376, 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018, Institute of Electrical and Electronics Engineers Inc., 10th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018, Baguio City, Philippines, 11/29/18. https://doi.org/10.1109/HNICEM.2018.8666376
Mayol AP, Maningo JMZ, Chua-Unsu AGAY, Felix CB, Rico PI, Chua GS et al. Application of artificial neural networks in prediction of pyrolysis behavior for algal mat (LABLAB) biomass. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. 8666376. (2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018). https://doi.org/10.1109/HNICEM.2018.8666376
Mayol, Andres Philip ; Maningo, Jose Martin Z. ; Chua-Unsu, Audrey Gayle Alexis Y. ; Felix, Charles B. ; Rico, Patricia I. ; Chua, Gundelina S. ; Manalili, Eduardo V. ; Fernandez, Dalisay Dg ; Cuello, Joel L ; Bandala, Argel A. ; Ubando, Aristotle T. ; Madrazo, Cynthia F. ; Dadios, Elmer ; Culaba, Alvin B. / Application of artificial neural networks in prediction of pyrolysis behavior for algal mat (LABLAB) biomass. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018. Institute of Electrical and Electronics Engineers Inc., 2019. (2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018).
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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.

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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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