A hybrid kinetic and optimization approach for biomass pyrolysis: The hybrid scheme of the isoconversional methods, DAEM, and a parallel-reaction mechanism

2020 
Abstract Biomass is a renewable source and is considered as an alternative to fossil fuels. Bioenergy can be released from biomass combustion while various bioproducts can be produced through gasification and pyrolysis. Among these three technologies, pyrolysis is more attractive, because multiple types of bioproducts including biogas, bio-oil, and biochar can be generated during the process. On the other hand, due to the convolution of biomass pyrolysis, the design of pyrolysis reactors is very challenging. In this work, a new hybrid kinetic and optimization approach was proposed to facilitate the design and process optimization of pyrolysis reactors. In the hybrid approach, the isoconversional methods were integrated with a parallel-reaction mechanism to provide more detailed description on thermal decomposition of biomass. Duckweed samples were analyzed in a TGA analyzer at three heating rates (10, 20, and 30 °C/min). The data were studied and compared with our proposed hybrid model. In the proposed approach, the isoconversional methods including the Friedman, FWO, and KAS were first applied to gain the values of kinetic parameters. A kinetic model using a parallel reaction mechanism was then developed to predict the thermal decomposition of duckweed. Furthermore, a nonlinear dynamic optimization model was established and coupled with the kinetic model to gain the optimal kinetic parameters for each parallel reaction and achieve good model-fitting. The same hybrid scheme was also employed and coupled with a three pseudo-components DAEM (distributed activation energy model) to predict duckweed pyrolysis. The model calculations of the hybrid models were all well-matched with experimental data that exhibited great potentials of our proposed hybrid scheme for providing detailed description of biomass pyrolysis.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    51
    References
    15
    Citations
    NaN
    KQI
    []