Publication | Peer reviewed papers | Potentiale, Bioenergiesysteme, Logistik
Correlations between tar content and permanent gases as well as reactor temperature in a lab-scale fluidized bed biomass gasifier applying different feedstock and operating conditions
Published 2021
Citation: von Berg L, Pongratz G, Pilatov A, Almuina-Villar H, Scharler R, Anca-Couce A. Correlations between tar content and permanent gases as well as reactor temperature in a lab-scale fluidized bed biomass gasifier applying different feedstock and operating conditions.Fuel.2021.305:121531
Abstract
The major problem of fluidized bed biomass gasification is the high tar contamination of the producer gas which is associated with the complex and time-consuming sampling and analysis of these tars. Therefore, correlations to predict the tar content are a helpful tool for the development and operation of biomass gasifiers. Correlations between tars and gas composition as well as reactor temperature derived for a steam-blown lab-scale bubbling fluidized bed gasifier are investigated in this study to assess their applicability. A comprehensive data set containing over 80 experimental points was obtained for various operation conditions, including variations in temperature from 700 to 800 °C, feedstock, amount of steam for fluidization, as well as the addition of oxygen. Linear correlations between tar and permanent gases show good accuracy for H2 and CH4 when using pure steam. However, experiments conducted with steam-oxygen mixtures show high deviations for the CH4-based correlation and smaller but still significant deviations for the H2-based correlation. No relation between tar and CO or CO2 was found. The correlation between tar and temperature shows highest accuracy, including good agreement with the steam-oxygen experiments. All tar correlations showed useful results over a broad operating range. However, significant deviations can be obtained when considering just one gas compound. Therefore, a combination of different correlations considering gas components and temperature seems to be the best method of tar prediction. This leads to a powerful tool for fast online tar monitoring for a broad range of operating conditions, once a calibration measurement was conducted.