Research On The Characteristics Of Biomass Based On The TGA Experiment And The BP Neural Network

Biomass energy is a renewable and clean energy which has taken great attentions of wordwide researchers for its high efficient conversion and cleanness. In this paper, the influence of different cellulose content, different heating rate, different sample size was researched by the means of thermogravimetry (TG). The catalysis effects of alkaline earth metallic on pyrolysis characteristics of pretreated biomass were analyzed with different calalysts and different mixing ratio mixtures.It is observed that the higher the cellulose content, the faster the pyrolysis rate. In contrast, the higher lignin content, the slower. The behaviour of deashing can hinder biomass pyrolysis, especially the hydrochloric deashing which reduces the pyrolysis rate at low temperatures, but improves it at high temperatures. The DTG curve of biomass deashed by distilled water occurs shoulder shape peak at about300℃which means this deashed manner can change the structure of biomass three component. Alkaline earth metallic compounds addition such as K2CO3and dolomite can promote biomass pyrolysis, and their mixture shows better catalytic activity with the largest pyrolysis rate and lowest char production when the mixing ratio is7:3.The most probable mechanism function was obtained through Malek method. It is analysed that biomass pyrolytic process should be divided into two stages and establish dynamic model respectively. The dynamic parameters such as activation energies and pre-exponential factor will finally be obtained more reasonably. BP neural network which established with momentum added will make an efficient prediction on activation energies. It shows that the diversion of forecasting values from tested values is no more than1.23kJ?mol-1with a relative error within±4.35%. It proves that the BP neural network has a better forecasting ability.