Modeling And Simulation Of Full Load Condition Of CGC-IGCC

There are many ways to use biomass energy, in which the biomass gasification technology is considered to be the most advanced in the world. When the gasification furnace is used for biomass gasification, it is a process that has a lot of time lag and nonlinear characteristics, and the artificial neural network is especially suitable for the need to consider various factors and conditions, imprecise, fuzzy data processing. Therefore, it is very important to study the modeling and simulation technology of biomass gasification under full load working conditions to realize the automatic control of carbon and gas CO generation gasification furnace system.First of all, this thesis describes the main functions of the CGC-IGCC system: the straw and sawdust gasification under the condition of high temperature, we get coal, biomass gas and a small amount of tar, etc. Then introduced five main subsystems of the gasifier system. Analyzed in detail of the characteristics of the gasifier from two aspects of the overall structure and working principle, at the same time, debugged the hardware circuit of the measurement system in the laboratory, and carried out the installation test in the industrial field, to ensure the proper operation of the hardware circuit. Next, this paper analyzes the working conditions of CGC-IGCC system, expounds the characteristics and classification of the gasifier, and the factors that affect the working conditions. Study on full load condition of biomass gasification with the material of sawdust and straw: under ideal condition, when feeding amount is 1500 kg ? ?-1, the yield of biomass is about 1000m3, the yield of carbon is about 450 kg.In this thesis, with the analysis of the process of full load condition of CGC-IGCC system, deeply analyzed several existing gasification modeling methods in this thesis, studied the modeling method of biomass gasification process based on BP neural network. It is proved that it is feasible to establish the model of gasification process by using neural network. In this paper, using the Matlab neural network toolbox as the simulation platform, set up a gasification model with three layers BP neural network, using the gasification temperature and the size of the gasification material as the input parameter, the biomass gas and carbon as the output parameter, Using the collecting data of the CGC-IGCC system to do the simulation, Simulation results shows that:The temperature has great influence on the gasification result. The yield of biomass gas increases with the increase of temperature, and the yield of biomass gas is stable when the temperature comes to 820?; The yield of carbon decreased with the increase of temperature until the temperature was 820?; And the effect of particle size to gasification results is small. The result of modeling simulation shows that, modeling the full load condition of the CGC-IGCC system with BP neural network is feasible.