Bayesian sample size determination for inspection of general corrosion of process components

A Bayesian approach-based method is proposed for calculating the minimum size of a sample to assess, with a specified precision, the integrity of process components suffering from general corrosion. The proposed method ensures that the error in the posterior estimate of the mean does not exceed a pre-defined acceptable margin of error at a specified confidence level. An analytical formula to estimate the sample size is introduced. The sample size obtained using the proposed method is smaller than a sample size obtained using the classical method with same confidence level. This reduces sampling inspection cost without affecting the precision of the estimate.     

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.