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The potential risk caused by the leakage of toxic and hazardous substances during air pollution accidents poses a great threat to local environmental safety, human heath, and even property security. Uncertainties and causalities are important components that should not be ignored in risk assessment. Compared with traditional non-probabilistic methods, Bayesian networks (BNs) are well suited to solving problems with high complexity and uncertainties. To utilize these advantages, a BN-based approach, coupled with diffusion simulation of risk substances, was proposed to estimate the risk of air pollution accidents in a probabilistic way at the local scale. This method was applied to analyze the risk levels of 24 risk sources and 24 risk subregions in Daya Bay, a national economic and technological development zone with a large petrochemical industry in Huizhou, Guangdong province. The results indicated that the Daya Bay area suffered a relatively low risk of accidental air pollution. Only five risk sources (20.8%) and two risk subregions (8.3%) were found to have high danger or risk. Ammonia, styrene, and sulfur dioxide were the most threatening substances in order. Acute exposure was the most sensitive factor, and made the highest contribution to risk level. This research provides an effective approach that can be used to characterize the air pollution accident risk quantitatively, probabilistically, and graphically. Probabilistic risk assessment can provide substantial support to local risk prevention, control, and emergency response for accidental air pollution, as well as to future risk management and policy decisions. [Display omitted] • A BN-based formal probabilistic approach is proposed for accidental air pollution risk analysis at the local scale. • Uncertainties and causalities in risk assessment are well expressed. • Dispersion models are introduced to enhance quantitative exposure estimation. • Daya Bay suffers a relatively low risk of accidental air pollution, acute exposure is the key driver of the risk level. • The results provide more detailed support to local accidental air pollution risk management. [ABSTRACT FROM AUTHOR]

This paper considers the modeling of the threshold autoregressive (TAR) process, which is driven by a noise process that follows a Student's t -dis- tribution. The analysis is done in the presence of missing data in both the threshold process f Z t g and the interest process f X t g . We develop a three-stage procedure based on the Gibbs sampler in order to identify and estimate the model. Additionally, the estimation of the missing data and the forecasting procedure are provided. The proposed methodology is illustrated with simulated and real-life data. [ABSTRACT FROM AUTHOR]

The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. Considering the small samples of fault data and non-stationarity of the production process with a considerable time lag, it is difficult to diagnose and eliminate the faults timely. This paper proposes for the first time a data and knowledge collaborative-driven framework to identify fault and perform self-healing control. Following the adaptive two-scale sequence division method, the prior knowledge matrix refined K2 algorithm is used to construct Bayesian networks to model the control process. When a new abnormal condition arises, the most similar historical sequence will be found, and inference on the corresponding Bayesian network can lead to fault identification results and self-healing control action. The proposed framework has achieved satisfactory results in simulation experiments and real data experiments. In the simulation data, the Bayesian network (BN) modeling achieved an accuracy of 98.75%, and the time required for structure learning of the BN was reduced by over 75.69%. In real data, the accuracy of identifying blast furnace faults reached 97.1%, and the results of self-healing control reasoning surpassed those of traditional BN reasoning methods in terms of correctness, order, completeness, and necessity. • A self-healing control framework for blast furnace is proposed. • Introducing prior knowledge into industry can efficiently benefit process modeling. • Put forward an adaptive sequence division method for non-stationary data. • The method is suitable for non-stationary and high safety requirements situations. [ABSTRACT FROM AUTHOR]