Robust Simulation Platform of Biomass Gasification Process with Carbon Capture for Energy Vector Polygeneration

Autor: Josephine Hannah Macdonald, Khairunnisa Mohd Paad, Nursyuhada Kamaruzaman, Shinya Yamanaka, Ali Abbas, Norhuda Abdul Manaf
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Chemical Engineering Transactions, Vol 113 (2024)
Druh dokumentu: article
ISSN: 2283-9216
Popis: Recent studies underscore the need for advanced technologies to limit global warming to below 2 °C and prevent irreversible climate change. This urgency is reflected in initiatives by the United Nations Framework Convention on Climate Change (UNFCCC) and the International Energy Agency (IEA). Carbon-negative solutions like the biomass gasification-carbon capture and storage (BECCS) hybrid systems are crucial, as BECCS is currently the only large-scale technology capable of removing CO2 from the atmosphere. BECCS integrates sustainable biomass conversion via gasification combined heat and power (CHP) to generate electricity and heat, with post-combustion carbon capture (PCC) being one of the most mature CCS technologies available. In this work, a robust simulation platform of biomass gasification with PCC technology is developed using Aspen Plus software. The BECCS system is operated using palm kernel shells as a feedstock, while monoethanolamine with a concentration of 30 wt.% is used for the PCC plant. The performance evaluation of the BECCS system is conducted via sensitivity analysis. The simulation analysis shows that an increase in gasification temperature produces higher quality syngas with the optimal gasification temperature being 850 (C. Meanwhile, the optimal reboiler temperature obtained is 120.6 (C, indicating the optimal temperature for CO2 desorption in the stripper. This study achieved a carbon removal rate of 99.94 %, and the highest power generated was observed to be 18 kW. The output from this robust simulation platform enables the minimization of overall emissions to below zero, offsetting emissions in other sectors where reductions are more challenging to achieve.
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