Prediction and optimization of syngas production from steam gasification: Numerical study of operating conditions and biomass composition

Prior information regarding the effects of the operating conditions and biomass composition on the syngas production is critical to predict and optimize the syngas production. Therefore, this paper proposes an effective method to predict and optimize the syngas production by combining a process model and data analysis techniques. Three agricultural waste types from Northeast China and 56 biomasses with different compositions were used as the gasification feedstock to investigate the effects of the operating conditions and biomass composition on the syngas production. A validated Aspen Plus process model was implemented to realize the biomass gasification process and techno-economic analysis. The sensitivity analysis results indicated that a lower temperature (approximately 600 ?) and lower steam-to-biomass ratio (S/B) (approximately 0.1) were the optimal operating conditions to achieve a higher lower heating value (LHV) of syngas. The introduction of steam changed the relationship between the temperature and LHV of syngas from inverse to direct. Furthermore, the performance comparison of the three agricultural waste based gasification processes indicated that a higher temperature and higher S/B could weaken the effect of the C, H, and O contents on the syngas composition. More importantly, the partial correlation analysis of the 56 sets of simulation results highlighted that a higher content of C and H, C/O and C/H helped enhance the LHV of syngas. The C/O value and C content exhibited the most significant correlations with the LHV of syngas, with correlation coefficients of 0.945 and 0.840, respectively. Correspondingly, these parameters exhibited a reasonable linear response with the LHV of syngas, with R2 values of 0.864 and 0.533, respectively. Finally, the techno-economic analysis indicated that SS is the optimal feedstock for syngas production.

Automated summary

This study proposes an effective method for predicting and optimizing syngas production by combining a process model and data analysis techniques. The effects of operating conditions and biomass composition on syngas production were investigated using three types of agricultural waste and 56 biomasses. Results showed that lower temperature and steam-to-biomass ratio were optimal for achieving higher heating value of syngas, and the introduction of steam altered the relationship between temperature and syngas heating value.