Authors

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.

Abstract in Chinese

以椰子椰壳粉和玉米秸秆粉料为原料制取成形颗粒，利用四套单轴压缩成形装置，探索成形模具内孔直径、压缩深度、成形颗粒保形深度对颗粒密度的影响规律，在压缩力平稳阶段对成形颗粒进行取样，得到的椰子椰壳粉原料的最大颗粒密度为1.53 g/cm3 (1530 kg/m3)，玉米秸秆粉料颗粒的最大颗粒密度为1.23 g/cm3 (1230 kg/m3)，同时在试验过程中两种生物质原料都出现了未成形的情况，表明了处理所研究压缩参数对颗粒质量影响显著。在已获得的颗粒成形试验数据基础上，考虑到颗粒密度与研究参数间的非线性特点，利用自适应模糊神经网络算法对玉米秸秆和椰子椰壳粉颗粒密度进行预测，结果显示该方法对预测生物质颗粒密度是有效的。