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- W4378839384 abstract "Numerical simulation of bench-scale pyrolysis behaviors is an important step in understanding the pyrolysis progress and modeling fire growth. However, it is usually difficult to measure accurate value of involved kinetic parameters even with state-of-the-art instruments due to the complicated kinetics associated with a large number of tandem reactions. In this paper, a chemistry informed neural network (CINN) framework is reported aiming at optimizing kinetic parameters for bench-scale pyrolysis with a high computational efficiency, where a temperature-dependent Arrhenius equation is formulated as a neuron in the CINN. With a transparent poly (methyl methacrylate) (PMMA) slabs as an example, the gradients of CINN parameters are analytically derived so that it does not need to repeatedly solve governing equations for the evaluation of gradients based on finite difference methods. Results show that the gradient-based training algorithm successfully reduces the prediction error of mass loss rate after several tens of epochs regardless of the type of incident heat fluxes. With the help of gradient information, the proposed CINN framework greatly reduces the computational burden of inverse modeling, which can be widely applied in the optimization of kinetic parameters for bench-scale pyrolysis." @default.
- W4378839384 created "2023-06-01" @default.
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- W4378839384 date "2023-06-01" @default.
- W4378839384 modified "2023-10-17" @default.
- W4378839384 title "Kinetic parameter optimization for the bench-scale pyrolysis via chemistry informed neural network" @default.
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- W4378839384 doi "https://doi.org/10.1016/j.jaap.2023.106030" @default.
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