AUTHOR=Altriki Ahmed , Ali Imtiaz , Razzak Shaikh Abdur , Ahmad Irshad , Farooq Wasif 

TITLE=Assessment of CO2 biofixation and bioenergy potential of microalga Gonium pectorale through its biomass pyrolysis, and elucidation of pyrolysis reaction via kinetics modeling and artificial neural network

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2022.925391

DOI=10.3389/fbioe.2022.925391

ISSN=2296-4185

ABSTRACT=This study investigates CO2 biofixation and pyrolytic kinetics of microalgae Gonium pectorale using model-fitting and model-free methods. Microalgae was grown in two different media. The highest rate of CO2 fixation (0.130 g/L/day) was observed at a CO2 concentration of 2%. The pyrokinetics of the biomass was performed using a thermogravimetric analyzer (TGA). Thermogravimetric (TG) and differential thermogravimetric (DTG) curves at 5, 10 and 20 °C/min indicated the presence of multiple peaks in the active pyrolysis zones. The predicted activation energy values in the models' ranges are Friedman (61.7–287 kJ/mol), FWO (40.6–262 kJ/mol), KAS (35 – 262 kJ/mol) and Popescu (66.4 – 255kJ/mol) which showed good agreement with the experimental values (R2>0.96). Moreover, it was found that the most probable reaction mechanism for Gonium pectorale pyrolysis was a third-order function. Furthermore, the multilayer perceptron-based artificial neural network (MLP-ANN) regression model of the 3-7-1 architecture demonstrated excellent agreement with the experimental values of the thermal decomposition of the Gonium pectoral. Therefore, the study suggests that the MLP-ANN regression model could be utilized to predict thermogravimetric parameters.