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

• Main Authors: 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 P., Culaba, Alvin B.
• Format: text
• Published: Animo Repository 2019
• Subjects: Pyrolysis; Microbial mats; Thermogravimetry; Neural networks (Computer science); Electrical and Computer Engineering; Electrical and Electronics
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1922
• Institution: De La Salle University

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. © 2018 IEEE.