Subjects: Energy Science >> Technology of Energy Storage submitted time 2024-03-31
Abstract： This work experimentally explores the influence of the sodium chloride pollution on the PEMFC performance in the marine salt spray environment by analyzing the concentration diffusion characteristics of the sodium chloride in the PEMFC membrane electrodes. Firstly, a set of experiments were carried out to determine the distribution of the sodium chloride components in the membrane electrodes, where five different salt spray environments (i.e., 100 mg/L, 200 mg/L, 300 mg/L, 400 mg/L, and 500 mg/L of the salt component, respectively) were used/employed to analyze the concentration diffusion characteristics of the sodium chloride. Then, the obtained samples were microscopically characterized and elementally analyzed by the field emission scanning electron microscopy (FESEM) and the energy spectrometry. Subsequently, a least squares-based model was proposed to predict the diffusion rate of the contaminating ions in the membrane electrodes. Lastly, the pollution of the sodium chloride was evaluated/assessed to reveal the performance degradation of the PEMFCs. The experimental results demonstrated that (1) the sodium chloride fraction existed as crystals or ions in the membrane electrodes in the marine salt spray environment; (2) the sodium chloride poisoning was founded in the proton exchange membrane in the form of sodium ions; (3) and the sodium-to-chloride ratio was proportional to the contamination time and the salt spray in the proton exchange membrane.
Subjects: Mechanical Engineering >> Mechanical Design submitted time 2024-04-01
Abstract： Overstaffing production in underground coal mining is not convenient for daily management, and incomplete information of coal miners hinders the rescue process of firefighters during mine accidents. To address this safety sustainability issue, a novel face recognition method based on an improved multiscale neural network is proposed in this paper. A new depthwise seperable (DS)-inception block is designed and a joint supervised loss function based on center loss theory is developed to constructe a new multiscale model. The miniers can be recognized in the harsh underground environment during the life rescue. Experimental results show that the accuracy, recall and F1-score indexes of the proposed method for the miner face recognition in the underground mining environment are 97.26%, 94.17% and 95.42%, respectively. Transfer model with joint supervised loss can effectively improve the recognition accuracy by about 0.5~1.5%. In addition, the average recognition accuracy of the proposed face recognition method achieves to 91.34% and the miss detection rate is less than 5% in the dugout tunnel of coal mine.
Hits
YES
Hits
Downloads
Comment