Biomass energy is recognized as a clean and sustainable energy source and is leveraged as a key enabler for driving the low-carbon transition of the energy system and achieving sustainable development. The higher heating value of solid biomass fuels (HHV-SBF) is a key parameter in its catalytic conversion process, and HHV-SBF is of great significance for catalyst design and matching, as well as the selection of reaction process parameters. To address the limitations in accuracy and generalization capability of traditional prediction methods for estimating the HHV-SBF, a dataset is constructed in this study that correlates chemical elements, proximate analysis parameters, and biochemical components with the HHV-SBF. Key hyperparameters of the deep neural network (DNN) are optimized using the Bayesian optimization algorithm. A Bayesian optimization-based deep neural network (BO-DNN) model is developed for the intelligent prediction of the HHV-SBF. Results show that the coefficient of determination (R2) of the BO-DNN model reaches 92.6%. Compared to multiple mainstream deep learning algorithms, its performance is improved by approximately 11.61%, and the mean square error is significantly reduced. The BO-DNN model demonstrates excellent generalization capability and stability. The findings of this study provide a theoretical basis for the rapid and accurate prediction of the HHV-SBF.
Feng et al. (Thu,) studied this question.