Prospective of response surface methodology as an optimization tool for biomass gasification process.

Asaad, Sara Maen; Inayat, Abrar; Rocha-Meneses, Lisandra; Jamil, Farrukh; Ghenai, Chaouki; Shanableh, Abdallah

Abstract

The worldwide population growth and the technological advancements reported in the past few years have led to an increase in the production and consumption of energy. This has increased greenhouse gas (GHG) emissions, the primary driver of climate change. As a result, great attention has been paid to sustainable and green energy sources that can replace or reduce reliance on non-sustainable energy sources. Among the different types of renewable energy sources currently available, bioenergy has been reported as an attractive resource mainly due to its low cost and great availability. Bioenergy can be produced from different biomass sources and converted into biofuels or value-added products through thermochemical, biochemical, and chemical processes. Gasification is a thermochemical process commonly used for bioenergy production, and it is particularly attractive mainly due to its high efficiency. However, its performance is influenced by parameters such as type of feedstock, size of biomass particle, feed rate, type of reactor, temperature, pressure, equivalence ratio, steam to biomass ratio, gasification agent, catalyst, and residence time. In this paper, the influence of different performance parameters in the gasification process is analyzed, and optimization and modelling techniques are proposed as a strategy for product yield enhancement.

Citation

ASAAD, S.M., INAYAT, A., ROCHA-MENESES, L., JAMIL, F., GHENAI, C, and SHANABLEH, A. 2023. Prospective of response surface methodology as an optimization tool for biomass gasification process. Energies [online], 16(1), article 40. Available from: https://doi.org/10.3390/en16010040

• Journal Article Type: Review
• Acceptance Date: Dec 16, 2022
• Online Publication Date: Dec 21, 2022
• Publication Date: Jan 1, 2023
• Deposit Date: Aug 28, 2023
• Publicly Available Date: Oct 2, 2023
• Journal: Energies
• Electronic ISSN: 1996-1073
• Publisher: MDPI
• Volume: 16
• Issue: 1
• Article Number: 40
• DOI: https://doi.org/10.3390/en16010040
• Keywords: Modeling; Optimization techniques; RSM; Syngas production; Zero-waste
• Public URL: https://rgu-repository.worktribe.com/output/2009981