New criteria to select reasonable hyperparameters for kinetic parameter estimation in distributed activation energy model (DAEM) by using neural network

The utilization of biomass is being actively explored to reduce net emissions of CO2. Solid carbon such as biomass is a mixture of various components that are difficult to separate, and it is not easy to evaluate its reactivity. The distributed activation energy model (DAEM) is often used to evaluate the reactivity of solid feedstocks such as biomass and coal. In our previous study, we proposed DAEM-NN, a method to estimate the model parameters using a neural network. In this study, we propose a new criterion phi for selecting the optimal hyperparameters in DAEM-NN: by minimizing phi, we can select hyperparameters that maximize both the conversion prediction ac-curacy and the typical importance of each reaction in a parallel reaction system. To verify the usefulness of the proposed criterion, reaction data were generated by numerical simulation, and the number of hidden layer, and the lower limit of contribution were optimized to minimize phi. The hyperparameters that minimize phi reduced the set of kinetic parameters corresponding to less important responses from the DAEM-NN kinetic analysis results.