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

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.