An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.     

沿海设施环境适应性试验和评价方法研究进展

概述了沿海设施所服役的腐蚀环境分类，主要分为海洋大气、浪花飞溅区及海水浸泡区，并对其暴露于大气和不同海洋环境位置的环境适应性试验评估方法进行了系统的总结分析。海洋大气环境模拟采用干湿交替加速腐蚀法，或建立户外大气暴露试验台开展试验；海水飞溅区试验通过实验室建海水冲刷试验装置进行模拟试验，或在实际环境中建立腐蚀试验中心开展试验；海水浸泡环境模拟通过海洋工程深水试验池开展试验。本研究对于开展海洋环境钢结构设施的防护性能考核评估具有重要意义，对沿海设施的环境适应评估方法发展具有指导意义。