Experimental-data-based, easy-to-use product gas composition prediction of a commercial open-top gasifier based on commercially used properties of softwood chips

Angelika Zachl, Markus Buchmayr, Johann Gruber, Andrés Anca-Couce, Robert Scharler and Christoph Hochenauer

Renewable Energy, 2024, vol. 226, issue C

Abstract: The high fuel requirements for downdraft gasifiers prevent the technology's breakthrough. To identify the limitations for the woodchips fines content (FC), bark content (BC) and water content (WC), this work developed novel and easy-to-use functions to predict the gas composition based on these fuel properties. While previous prediction tools require the elemental fuel composition, the presented tool uses only typical commercial fuel properties. Multivariate polynomial regression is performed using 24 different operating points of an 85-kW open-top gasifier. The evaluations revealed three novel findings: (1) The polynomial functions of first order were the most suitable to describe the effects of the fuel properties on the gas composition. (2) According to the developed approach, the optimum fuel leading to the highest product gas lower heating value and a total hydrocarbon content below 8000 ppm contains 0 m% FC, 6.1 m% BC and 7.2 m% WC. (3) The achieved average deviation of the predicted gas composition compared to the measured one was in the same range as the typical variations between experiments. Therefore, a satisfying accuracy was achieved. The developed functions can be applied in academia and industry to identify suitable woodchips and to predict the gas quality when buying woodchips.

Keywords: Biomass downdraft gasification; Gas quality; Prediction; Fuel properties; Multivariate polynomial regression (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:226:y:2024:i:c:s0960148124004725

DOI: 10.1016/j.renene.2024.120407

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