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.     

废混油钻井液资源化利用技术研究

废钻井液是钻井作业中主要污染源之一,其中含有重金属、碱、盐、油、有机物等多种污染物质,特别是废混油钻井液,油含量更高,是较难解决的问题之一,也是制约油田企业环境质量提高和可持续发展的一大难题。以废钻井液处理为代表的资源化利用技术可有效治理井场废物,将处理后的废钻井液直接用来做井场围堰和平整地面的材料,既可有效地利用废物,消除污染源,又可缓解因污染引起的工农矛盾,减少由钻井开发引发的污染赔款,实现社会效益、经济效益和环境效益的统一。