Kinetic, thermodynamic and artificial neural network prediction studies on co-pyrolysis of the agricultural waste and algae

Qian Wang, Rui Wang, Zixuan Li, Yanhua Zhao, Qiankun Cao, Feifei Han and Yuze Gao

Renewable Energy, 2024, vol. 233, issue C

Abstract: The thermal decomposition behaviors of Maize straw (MS), algae (AL), and their blends were studied in a thermogravimetric analyzer to evaluate the bio-energy potential in this research. The blends neutralized violent reactions of each other at the second pyrolysis stage, and S and CPI respectively achieved the lowest points at 40%MS-60%AL and 60%MS-40%AL. The average Eα calculated by OFW, KAS, Starink and Friedman methods changed from 6.94 to 68.03 kJ/mol, 14.61–85.76 kJ/mol, 14.31–85.12 kJ/mol, respectively. When α was in range of 0.2–0.6, the Coats-Redfern method was calculated within 19.39 kJ/mol. KAS and Starink methods seemed more reliable. Reaction mechanism analyzed by Criado method show that AL was significantly different from that of MS. In addition, thermal degradation input by biomass types, heating rate and temperature were well predicted by artificial neural network (ANN). ANN 19 modal (3-5-15-1) was obtained the best (MSE = 0.0978 and R2 = 0.9999). The modal was utilized to predict the thermogravimetric curve without experiment, DTGmax of 70%MS-30%AL was the highest. While overfitting occurred in the high-temperature ranges (>700 °C). This study proves the potential of ANN modeling for producing derived energy from co-pyrolysis of biomass and other residues.

Keywords: Biomass; Pyrolysis; Kinetics; Criado; Neural network (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:233:y:2024:i:c:s0960148124012102

DOI: 10.1016/j.renene.2024.121142

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