Process simulation of chemical looping gasification of biomass using Fe-based oxygen carrier: Effect of coupled parameters

Abstract

Chemical looping gasification (CLG) of biomass is an efficient way to obtain high-quality syngas without the need of air separation. It offers a carbon-negative and clean way for the utilization of biomass while syngas is used combining with carbon capture. A simulation model of CLG process with continuous feedstock of pine sawdust is constructed and investigated in this work. The iron-based oxygen carrier (OC) is adopted to offer lattice oxygen. This model is validated using the experimental results from literature to demonstrate its feasibility for CLG of biomass. Then, the operating parameters of gasification temperature, amounts of steam and OC are changed to investigated their effects on the gasification performances. Results demonstrated that increasing the temperature, OC and steam flow is beneficial to intensify the carbon conversion efficiency. When the gasification temperature is 880 °C, carbon conversion efficiency and gasification efficiency reach 92.76% and 81.87%. The carbon conversion efficiency rises from 76.81% to 82.18% when OC mass flow is changed from 0 to 1.5 kg/h. Coupled effects of the amounts of reaction temperature, OC mass flow and steam molar flow are also investigated while two of them are changed simultaneously. The minimum and maximum values of gasification efficiencies are 62.13% and 77.73% when the amounts of steam and OC are (0 mol/h, 1.0 kg/h) and (1.0 mol/h, 0 kg/h), respectively. When both of them increases to 1.0 kg/h or 1.0 mol/h, the carbon conversion efficiency reaches the maximum value of 88.57%. The coupled effects of temperature and OC or steam amount demonstrate that the carbon conversion efficiency is mainly determined by temperature at lower-temperature range. Though both of OC and steam can offer oxygen in CLG, due to the different oxidation ability, their effects on gasification efficiency are opposite. Investigation on coupled effects of multiple parameters offers thorough information for the system performance evaluation, which can be used for further system optimization.