基于自适应模糊神经网络算法（ANFIS）的生物质原料颗粒密度预测

Abstract

Coconut coir dust and corn stover powder were taken as raw biomass materials for pellet production, using four uni-axial compression set-ups, to explore the influence of the diameter of the inner hole diameter of the cylinder, the depth in compression , and the depth remained in compaction on the pellet density. Sample of pellets produced at the force steady phase, the maximum pellet density of the coconut coir dust material is 1.53 g/cm3 (1530 kg/m3), and 1.23 g/cm3 (1230 kg/m3) of the corn stalk powder pellets are obtained, At the same time, in the process of the test, Failure to compress the two biomass raw materials into pellets also occurred, indicating that the compression parameters studied in the experiment had a significant impact on the pellet quality. On the basis of the obtained pelleting test data, taking into account the nonlinear characteristics between pellet density and processing parameters involved, the adaptive neuro-fuzzy influence system(ANFIS) method was used to predict the pellet density of coconut coir dust and corn stover powder. The results show that the method is effective for predicting the density of biomass particles.