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The metal-support interactions and their interfaces have showed great influence on catalytic activity and stability. Herein, different support of M2O3- (M = La , Al-) supported Ni catalysts was prepared by a citric acid-assisted sol-gel method. The physical structure and chemical properties of the As-prepared catalysts were systematically characterized by various technologies. The catalytic performance was evaluated for hydrogen or syngas production by ethanol steam reforming and methane dry (CO2) reforming, respectively. The results showed that compared with Al2O3 support, the nickel supported on La2O3 possessed a smaller particle size of nickel even after high-temperature reduction. In addition, the La2O3-supported nickel catalyst had stronger metal-support interaction and higher nickel electron density as a result of higher ethanol conversion activity and stability. The ethanol conversion was maintained at 87.8% after a 3000-minute test, and the hydrogen production was as high as 6500 μmol/min. Moreover, the Ni-La2O3 catalyst also showed good activity for methane dry reforming. The initial conversion of methane and carbon dioxide was close to 90%, and the ratio of H2/CO reached 0.94. The better catalytic performance of the Ni-La2O3 catalyst was ascribed to smaller particle size of Ni, rich metal-support interfaces, more nickel electron densities, abundant strong basic sites, and strong metal-support interactions. [ABSTRACT FROM AUTHOR]

The rapid urbanization and increasing use of distributed renewable energy resources have imposed a significant burden on power networks. Smart buildings equipped with artificial intelligence technology can play a pivotal role in energy management, ultimately enhancing energy efficiency and voltage quality. However, ensuring voltage stability within large-scale smart building systems presents challenges due to the coexistence of diverse energy sources and the fluctuating nature of renewable energy. This paper proposes a safe multi-energy management framework achieved by online decentralized execution and centralized training for large scale smart buildings in distribution networks. The energy management problem is formulated as a safety-augmented Markov decision process, presenting intractability for dynamic programming due to its extensive continuous state space. To solve this issue and improve the convergence speed and training process stability, a safety-augmented constrained multi-agent reinforcement learning algorithm based on reward extrapolation is proposed. In this algorithm, hazard values are introduced to enhance non-safe multi-agent reinforcement learning algorithms and meet safety constraints. A novel reward network is designed by imitating expert underlying intentions to ensure the rationality of the reward function for multi-objective tasks. Additionally, the loss function for estimating the Q-network is redesigned during training process to guarantee effective convergence. Theoretical analysis is conducted to provide the convergence guarantee. Numerical case studies based on actual data are performed to validate the effectiveness and scalability of our approach, showing that smart buildings can achieve superior energy management performance while ensuring voltage safety for distribution networks. The source code of the proposed algorithm is available at https://github.com/SYiyun/CMARL-EX. • A novel framework for online energy management in large-scale smart building systems is proposed. • The proposed method ensures voltage safety in online multi-agent reinforcement learning. • Redesigning the loss function's target of the estimation Q-network ensures enhanced task performance. • Imitating expert demonstrations improves stability and accelerates learning in reinforcement learning-based smart buildings. [ABSTRACT FROM AUTHOR]