Biomasss Solid Organic Waste Gasification Technology And Modeling Analysis

With the advancement of science and technology,the problem of energy shortage and environmental pollution has become increasingly serious.Therefore,all countries in the world are seeking low-pollution,renewable,and efficient energy sources.As the most extensive renewable energy source on the planet,biomass has many advantages besides its large output,easy storage,and cleanliness.Through the conversion of biomass gasification technology,biomass can be effectively converted into high-grade energy to replace fossil fuels such as hydrogen and carbon monoxide,which has tremendous potential for the development of biomass energy.Gasification furnace with self-heating water gas replenishment,optimized the system design was innovatively designed in this paper,and the design of the gasifier was optimized through Fluent simulation.Completed gasification process simulation and experimental research was carried out based on Aspen Plus software,which provide a strong theoretical basis for the preparation of high-quality biomass gasification gas mixture biomass gasification and reference for engineering applications.The specific research content and results are as follows:1)The design of the gasifier was first optimized combined the results of' Fluent simulations.The results show that the gas velocity has god uniformity with the single-side exhaust and both-side intake method.The simulation results were verified by experiments.The results show that the combustion of the gasification process was stable in this scheme,indicating high accuracy of Fluent gasification simulation process.2)Gasification process simulation was carried out based on Aspen Plus software to explore the parameter design of gasification experiments.The simulation results show that the gasification temperature,steam flow rate,aeration rate,biochar dosage,and combustion chamber pressure had an effect on the yield of the components of the gas mixture produced by the biomass gasification.Among them,the gasification temperature had the most significant effect on gasification,The temperature is about 700 ?,which was most conducive to the formation of H2.Taking into account the energy consumption,manufacturing costs,and safety of the biomass gasification system,the preferred gasification temperature for the quality of the gas mixture should be controlled at about 700 ?.Under the condition of low aeration rate,small particle size and vapor,the gasification temperature would be reduced to a certain extent.Therefore,the longan shell which has higher gasification temperature was selected as the biomass for subsequent gasification experiments.3)The experimental scheme was then determined on the basis of simulation and the experiment was completed.Based on the simulation,the experimental scheme was designed.The effects of biochar type,steam flow rate,aeration rate,and biochar addition on the gasification process were studied.The experiment found that when the flow rate of the steam was 0.8 L/h,the aeration rate was 18 L/min,and the amount of biochar added was 4%,the gasification effect was obtained with a H2 output of 41.78 g/kg,a conversion rate of 91.80 wt%,a calorific value of 4.71 MJ/m3 and a gasification energy consumption of 2.50 kW·h/kg.Test parameters of each group of was introduced into the Aspen Plus model,and the simulated values of the two gases of H2 and CO were basically consistent with the change trend of the experimental values,indicating higher reliability of the gasification model.The gasification parameters were optimized using the optimization module in Aspen Plus.After optimization,the yield of H2 was 42.44 g/kg,and the gasification energy consumption was 2.50 kW · h/kg.4)Finally,a BP neural network prediction model was established.The input layer of this model includes 7 inputs,namely TC1,TC2,TC3,aeration rate,steam flow rate,reaction time,and biochar addition amount.The results show that the tansig function with a single hidden layer has a higher prediction accuracy for H2(R2=0.8914).Through modeling and simulation results,it can be known that the BP neural network model adopted by the heating biomass gasification system was feasible for simulation and prediction,and the model can provide guidance for the gasification process of the self-heating biomass gasification system.