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=10 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 microalga G. pectorale using model-fitting and model-free methods. Microalga 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 by a thermogravimetric analyzer (TGA). Thermogravimetric (TG) and derivative thermogravimetric (DTG) curves at 5, 10 and 20°C/min indicated the presence of multiple peaks in the active pyrolysis zones. The activation energy was calculated by different model-free methods such as Friedman, Flynn-Wall-Ozawa (FWO), Kissinger-Akahira-Sunose (KAS), and Popescu. The obtained activation energy which are 61.7–287 kJ/mol using Friedman, 40.6–262 kJ/mol using FWO, 35–262 kJ/mol using KAS, and 66.4–255 kJ/mol using Popescu showed good agreement with the experimental values with higher than 0.96 determination coefficient (R2). Moreover, it was found that the most probable reaction mechanism for G. pectorale pyrolysis was a third-order function. Furthermore, the multilayer perceptron-based artificial neural network (MLP-ANN) regression model of the 4-10-1 architecture demonstrated excellent agreement with the experimental values of the thermal decomposition of the G. pectoral. Therefore, the study suggests that the MLP-ANN regression model could be utilized to predict thermogravimetric parameters.