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Yiqiang Wu,Chunhua Yao,Yunchu Hu,Shoulu Yang,Yan Qing,Qinglin Wu 한국공업화학회 2014 Journal of Industrial and Engineering Chemistry Vol.20 No.5
Flame retardancy and thermal degradation of wood treated with magnesium chloride (MgCl2 6H2O) were investigated. Results showed that MgCl2 6H2O decreased flame intensity and heat release rate, and reduced smoke concentration and gas yield. From ambient temperature to 250 ℃, MgCl2 6H2O reduced wood combustibility by gas dilution mechanism. The chemical started to decompose at 350 8C and produced MgOHCl, in which -Cl and -Mg free radicals were generated and intervened the chain reactions of wood combustion. Hydrogen chloride gas generated promoted wood charring. MgCl2 6H2O gradually converted to MgOHCl and MgO compounds at higher temperatures, and MgO suppressed wood combustion by the wall effect mechanism.
Dongfang Zhang,Yunduan Cui,Yao Xiao,Shengxiang Fu,Suk Won Cha,Namwook Kim,Hongyan Mao,Chunhua Zheng 한국정밀공학회 2024 International Journal of Precision Engineering and Vol.11 No.1
With the rapid development of artificial intelligence, deep reinforcement learning (DRL)-based energy management strategies (EMSs) have become an important research direction for hybrid electric vehicles recently, which still face some problems such as fragile convergence characteristics, slower convergence speed, and unsatisfactory optimization effects. In this research, a novel DRL algorithm, i.e. an improved soft actor-critic (ISAC) algorithm is applied to the EMS of a fuel cell hybrid vehicle (FCHV), in which the priority experience replay (PER) and emphasizing recent experience (ERE) methods are adopted to improve the convergence performance of the algorithm and to enhance the FCHV fuel economy. In addition, the fuel cell durability is also considered in the proposed EMS based on a nonlinear fuel cell degradation model while considering the fuel economy. Results indicate that the FCHV fuel consumption of the proposed EMS is decreased by 7.87%, 2.79%, and 2.44% compared to that of the deep deterministic policy gradient (DDPG)-based, the twin delayed deep deterministic policy gradient (TD3)-based, and the SAC-based EMSs respectively while the fuel consumption gap to the dynamic programming-based EMS is narrowed to 2.37% by the proposed EMS. Moreover, the proposed EMS presents the best training performance considering both the convergence speed and stability, and the convergence speed of the proposed EMS is increased by an average of 47.89% compared to that of the other DRL-based EMSs. Furthermore, the fuel cell durability is improved by more than 95% using the proposed EMS compared to that of the EMS without considering the fuel cell degradation.