Palla S, Surya DV, Pritam K, Puppala H, Basak T, Palla VCS. A critical review on the influence of operating parameters and feedstock characteristics on microwave pyrolysis of biomass.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024;
31:57570-57593. [PMID:
38888826 DOI:
10.1007/s11356-024-33607-0]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 05/04/2024] [Indexed: 06/20/2024]
Abstract
Biomass pyrolysis is the most effective process to convert abundant organic matter into value-added products that could be an alternative to depleting fossil fuels. A comprehensive understanding of the biomass pyrolysis is essential in designing the experiments. However, pyrolysis is a complex process dependent on multiple feedstock characteristics, such as biomass consisting of volatile matter, moisture content, fixed carbon, and ash content, all of which can influence yield formation. On top of that, product composition can also be affected by the particle size, shape, susceptors used, and pre-treatment conditions of the feedstock. Compared to conventional pyrolysis, microwave-assisted pyrolysis (MAP) is a novel thermochemical process that improves internal heat transfer. MAP experiments complicate the operation due to additional governing factors (i.e. operating parameters) such as heating rate, temperature, and microwave power. In most instances, a single parameter or the interaction of parameters, i.e. the influence of other parameter integration, plays a crucial role in pyrolysis. Although various studies on a few operating parameters or feedstock characteristics have been discussed in the literature, a comprehensive review still needs to be provided. Consequently, this review paper deconstructed biomass and its sources, including microwave-assisted pyrolysis, and discussed the impact of operating parameters and biomass properties on pyrolysis products. This paper addresses the challenge of handling multivariate problems in MAP and delivers solutions by application of the machine learning technique to minimise experimental effort. Techno-economic analysis of the biomass pyrolysis process and suggestions for future research are also discussed.
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