机器学习驱动的生物质热解模型建立及挥发分化学链重整制氢工艺优化

To address the challenges of low gasification efficiency and poor hydrogen selectivity in the production of green hydrogen from biomass gasification, a decoupling process involving pyrolysis followed by chemical looping reforming of volatiles for hydrogen production is proposed. In the theoretical analysis of the above process, it was found that the complex relationship between the yield and composition of pyrolysis volatiles and the properties of biomass and pyrolysis operating conditions is difficult to be accurately associated with the traditional modeling method, which restricts the precise analysis and regulation of the above process. Therefore, this paper establishes a neural network model for the product distribution of the biomass fast pyrolysis process using machine learning methods and determines the optimal pyrolysis conditions using the particle swarm optimization algorithm. The goal is to maximize the hydrogen atom ratio and heating value of the pyrolysis volatiles while minimizing the oxygen atom ratio. Subsequently, the process of hydrogen production from chemical looping reforming of volatiles was analyzed and optimized through process simulation. The results show that the established neural network model can accurately predict the yield of the three-phase pyrolysis products, the detailed composition of the pyrolysis gas, the elemental distribution of the pyrolysis oil, and the higher heating value, etc. The average coefficient of determination of the predictions is 0.821, and the average root mean square error is 2.00, in the test set of the above output parameters. After optimization, the pyrolysis volatiles yield for herbaceous biomass (wheat straw, corn stover) and woody biomass (ficus, pine wood) ranged from 64.49% to 78.62%, with a hydrogen atom ratio between 3.77% and 4.39%. Under optimal conditions at a reforming temperature of 700 ℃ and a steam-to-biomass mass ratio of 0.71 to 0.88, wheat straw showed the highest hydrogen yield and CO₂ negative emission capability, with values of 0.60 m³/kg and -1.74 kg /m³, respectively. Using chemical looping reforming of biomass volatiles for hydrogen production, the hydrogen yield from the four types of biomass increased by 61%, 35%, 16%, and 34% respectively compared to conventional gasification. The research results provide effective foundational support for the production of green hydrogen from biomass.