Biomass gasification using various gasification agents: Optimum feedstock selection, detailed numerical analyses and tri-objective grey wolf optimization

Biomass gasification using a range of feedstocks is simulated in such mediums as air, O-2, O-2-enriched air, steam, CO2, and a mixture of these agents. A parametric study is performed to investigate the effects of the main parameters on the performance criteria of the gasifier. Accordingly, gasification temperature and pressure, and moisture content of biomass are considered as the decision variables. Subsequently, a tri-objective grey wolf optimization as a robust and advanced technique is applied to the gasification system to determine the most suitable feedstock for high cold gas and exergy efficiencies and low CO2 emissions. Maximization of the cold gas and exergy efficiencies and minimizing CO2 emissions are the main purposes of tri-objective grey wolf optimization. Pareto optimal solutions are presented for each gasification agent considering a wide range of feedstocks. The final optimum solution points are defined for a wide range of feedstocks and gasification agents by employing the LINMAP and TOPSIS methods. The results show that for biomass gasification where O-2 is the gasification agent, a feedstock with the chemical formula of CH1.2O0.495 is preferred as in this condition the exergy and cold gas efficiencies are within the range of 79.1-80.7% and 84.0-87.5%, respectively. The results further show that CH1.5O0.71 and CH1.2O0.5 are suitable feedstocks for biomass gasification using steam and CO2, respectively. For the final optimum solution, the exergy efficiency of biomass gasification using a suitable feedstock in the medium of steam and CO2 is 93.7% and 94.7%, respectively. The corresponding values of cold gas efficiency are 94.8% and 75.8% for steam and CO2 agents, respectively. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The study found that by simulating biomass gasification process, it is possible to achieve high gasification efficiency with specific feedstocks under different gasifier conditions. Using the grey wolf optimization algorithm, the most suitable feedstock under different gasification agents was found to achieve optimal efficiency and minimal emissions.