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.     

基于故障树的生物质气化中毒事故BN-LOPA分析

为降低生物质气化中毒事故的概率,本文提出基于故障树的贝叶斯—保护层复合分析法(BN-LOPA),对生物质气化中毒事故进行风险分析。以辽宁省某气化站为例,通过贝叶斯网络计算出该气化站中毒事故发生概率为1.11×10^-4,基于贝叶斯网络推导出中毒事故基本事件的后验概率,得到导致事故发生的故障节点,并对其设置独立防护层,经推导计算,该气化站的气化中毒事故发生概率可降低至5.35×10^-6,风险等级从7级降至5级。结果表明:该气化站增加独立防护层后,符合安全生产的要求。