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.

Conventional solar power tower (SPT) systems often suffer from significant heat transfer exergy destruction due to large temperature differences between the heat source and the working fluid during the heat exchange process. To overcome this limitation, a high–low dual-tower configuration based on segmented thermal utilization is proposed. In this arrangement, the high-temperature tower is mainly responsible for the evaporation, superheating, and reheating processes, whereas the low-temperature tower primarily handles feedwater preheating. Such a configuration improves the temperature matching characteristics during the heat exchange process. A comprehensive model integrating the heliostat field, receiver, thermal energy storage system, and power block was developed and validated against Solar Two experimental data, showing good agreement. Comparative analyses were conducted under identical solar resource and operating conditions. The results indicate that the proposed system achieves a comparable power output while reducing total heat transfer exergy destruction by approximately 24%, with a significant reduction of over 80% in the preheating section. Sensitivity analysis further reveals that optimizing the high tower outlet temperature can effectively reduce irreversibility and slightly enhance power output, although constrained by the pinch temperature difference. Dynamic simulations based on typical meteorological year data demonstrate that the system maintains stable operation and improves cycle efficiency. From an economic perspective, the proposed system reduces the levelized cost of electricity (LCOE) by about 6.6% and shortens the dynamic payback period, indicating enhanced long-term competitiveness. Overall, the high and low dual-tower system effectively improves thermodynamic and economic performance, providing a promising approach for high-efficiency concentrating solar power (CSP) development.