The Influence Of Biomass Addition On Low Rank Coal Pyrolysis Behavior And Its BP Neural Network Model

The traditional method of coal directly utilization is not only failed to make efficientlyuse of the chemical energy in coal but also caused serious pollution to the ecological environment. The low rank coal is abundant in coal resources, as its higher ratio of H/C, volatile and easily liquefied, can be easy to transform to get more yield of tar and gas product under low temperature pyrolysis. But the mechanism of low temperature pyrolysis process of coal is relatively complex, the situation which the uncertainty of products distribution and high heavy components content in product is also existed. The biomass is a kind of material that can renewable, wide distribution and variety, rich in hydrogen and has the huge potential of utility as a hydrogen donor. In this paper, the method that mixing biomass and coal was used to explore the influence of biomass amounts on products distribution and components of pyrolytic tar of low temperature pyrolysis, and combined with electron ESR spectrum to study the influence mechanism of pyrolysis process from free radical change trend of products also. Finally, the prediction model of co-pyrolysis products was established by BP neural network toolbox.The mono-pyrolysis of biomass and coal analyzed by thermal gravimetric combined with co-pyrolysis of them in tube furnace showed there is no overlapping temperature region in main stage of co-pyrolysis, which provided the possibility of hydrogen transfer from biomass to coal during co-pyrolysis. It is conclude that there is a certain promoting reaction between coal and biomass at the low temperature pyrolysis, which effect the distribution of pyrolysis products, free radical concentrations, quality and component of pyrolytic tars. The content of phenolic compound in two coal tars increased7.31%and18.36%respectively with increased with biomass mixing, as high additional value products are obtained in certain concentration. When the blending of rice husk up to25%, the free radical concentration of two pyrolytic tars decreased72.30%and66.12%compared with raw coals, respectively, that is indicated that biomass reacts with free radicals in pyrolytic tar and a certain relationship between qualities of pyrolytic tar with its free radical concentrations. Due to the chain reactions of free radicals occurred between the free radicals of tar with oxygen in air, the free radical concentrations of tar increased over time when the pyrolytic tars preserved in air, as the increased maximum is317.09%and249.22%respectively, result in deterioration of tar qualities. Finally, the multi-factors (mass ratio, approximate analysis of samples, hydrocarbon ratio) BP neural network model to predict the three kinds of product yield (tar, water, char) of co-pyrolysis was established, the model was showed good predictability, and the relative mean errors about3.5%.