Parameter Optimization Configuration Of Tar Removal In Biomass Gasification Process

The existence of tar effected the whole biomass gasification process. Therefore, how to minimize the tar content or raise the tar cracking rate was a very important actual issue.Based on the two different Kernel functions,Gaussian Radial Basis Kernel function and Linear Kernel function, LS-SVM model of Rice straw pyrogenation and gasification tar removal process by catalytic cracking was built.Through the validation, the results showed that the model based on the Linear Kernel function had better generalization ability and fitting effect than the model based on the Gaussian Radial Basis Kernel function. On the basis of the two models, using GA and PSO to find the conditions what the catalytic cracking temptature (T ) and the gas residence time (t ) should meet to,while the tar catalytic cracking rate was maximum. Through the optimized calculation, the results showed that the optimizing effection of PSO was better than GA, and obtaining satisfactory results.Thus proving the optimization method proposed in this thesis was feasible and valid in tar removal process of the biomass gasification.