Multi-objective genetic algorithm approach for enhanced cumulative hydrogen and methane-rich syngas emission through co-pyrolysis of de-oiled microalgae and coal blending

Shweta Rawat, Lokesh Wagadre and Sanjay Kumar

Renewable Energy, 2024, vol. 225, issue C

Abstract: Towards establishing low-carbon bio-economy, the energy-rich syngas is considered a global energy carrier. Targeting hydrogen as a promising advanced fuel, the significance of methane also increases due to its direct conversion capability into hydrogen. The current study aims to use a co-pyrolysis-based valorization of de-oiled microalgae and low-rank coal blend to generate H2 and CH4 rich syngas. Pyrolysis kinetic models, Kissinger-Akahira-Sunose (KAS) and Starink (STK) are used to evaluate apparent activation energy (Ea). The gradual addition of microalgae (0⎼100%) in coal reduces Ea from 189.11⎼55.87 kJ/mol and 180.16⎼54.61 kJ/mol by KAS and STK method, respectively. The maximum hymethane carrying ratio is observed 2.51 and 3.51 at optimized conditions of response surface methodology (RSM) and artificial neural network-based multi-objective genetic algorithm (ANN-MOGA), respectively. Maximum H2 (54.5 %) in the syngas is observed at mid pyrolysis stage (451 °C) using ANN-MOGA optimized conditions (blending ratio - 42.25 % and heating rate-13.8 °C/min). This study highlights the advantage of ANN-MOGA optimization over statistical based optimization. Hence, incorporating of the evolutionary algorithm as integrated ANN-MOGA optimization could be an efficient way for hymethane rich syngas emission in co-pyrolysis approach to gain carbon-neutral energy with zero waste discharge.

Keywords: De-oiled microalgae; Co-pyrolysis; Syngas; Artificial neural network; Multi-objective genetic algorithm; Hymethane carrying ratio (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:225:y:2024:i:c:s096014812400329x

DOI: 10.1016/j.renene.2024.120264

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