Test and performance optimization of nozzle inclination angle and swirl combustor in a low-tar biomass gasifier:a biomass power generation system perspective

A nozzle inclination angle and swirl combustor inside the low-tar biomass(LTB)gasifier reactor were tested and optimized to evaluate these effects on tar reduction to design tar-free producer gas.The tar reduction process is mainly based on the concept of a swirling flow created by the nozzle inclination angle,with the inclination angle of 55◦to the radial line,allowing good mixing between pyrolysis gases and gasifying agents.A separate swirl combustor has created large internal annular and reverses flow zones with the help of swirl flow,resulting in homogenized temperature inside the combustor and providing longer residence time;both have a positive effect on the combustion of mixed gasifying air-pyrolysis gases by the thermal cracking in the partial oxidation zone.Recircling ratio(RR)and combustion degree of volatiles are the two optimization parameters for evaluating the performance of NIA and swirl combustor.The result observed that outstanding tar reduction occurred in this novel system.About 86.5 and 12.8%of tar compounds are broken down in the partial oxidation zone and pyrolysis zone using the novel swirl combustor and NIA,respectively;gas outlet has observed producer gas having tar concentration of less than 1%.The optimization results reveal that a lower recycling ratio(recycle gas/gasifying air)and a higher combustion degree of volatiles perform better in biomass gasification.Finally,this system generated producer gas with the tar concentration at an extremely low level of 7.4 mg/Nm^(3)for a biomass moisture content of 9%and appeared the lower heating value of 4.6–5.1 MJ/Nm^(3).This lower tar concentration might be directly coupled with an internal combustion engine or a gas turbine for power generation.

Understanding and optimizing the gasification of biomass waste with machine learning

Gasification is a sustainable approach for biomass waste treatment with simultaneous combustible H2-syngas production.However,this thermochemical process was quite complicated with multi-phase products generated.The product distribution and composition also highly depend on the feedstock information and gasification condition.At present,it is still challenging to fully understand and optimize this process.In this context,four datadriven machine learning(ML)methods were applied to model the biomass waste gasification process for product prediction and process interpretation and optimization.The results indicated that the Gradient Boosting Regression(GBR)model showed good performance for predicting three-phase products and syngas compositions with test R^(2)of 0.82–0.96.The GBR model-based interpretation suggested that both feed and gasification condition(including the contents of feedstock ash,carbon,nitrogen,oxygen,and gasification temperature)were important factors influencing the distribution of char,tar,and syngas.Furthermore,it was found that a feedstock with higher carbon(>48%),lower nitrogen(<0.5%),and ash(1%–5%)contents under a temperature over 800℃could achieve a higher yield of H_(2)-rich syngas.It was shown that the optimal conditions suggested by the model could achieve an output containing 60%–62%syngas and achieve an H_(2)yield of 44.34 mol/kg.These valuable insights provided from the model-based interpretation could aid the understanding and optimization of biomass gasification to guide the production of H_(2)-rich syngas.