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Domino effects in process facilities can lead to catastrophic consequences. Many previous models devoted to assessing domino effects, but the influence of the combination of heat radiation, overpressure and fragment on the escalation is not considered. In addition, although the quantity of dangerous substances stored in the affected unit has a non-negligible effect on the propagation of domino effects, their inclusion in the modeling of domino effects has hardly been taken into account. To address these challenges, in this work, using the Fuzzy Analytic Hierarchy Process (FAHP), the escalation of domino effects under multiple physical factors is assessed, while considering other factors in varying degrees such as meteorological, mitigation measures and the affected units. Finally, the conditional probability table (CPT) in Bayesian networks (BNs) is updated by the FAHP. The application of the methodology has been demonstrated using an illustrative case study; the comparison of the results with that of previous study illustrates the efficacy and practicality of the development methodology. This work will be beneficial for land-use planning.

Gas pressure is an important index for evaluating the outburst risk and determining the gas content in coal seams. It is recommended to predict coal-seam gas pressure of the workface at deep levels before extending mining activities to deeper levels. According to the prediction results, measurements are taken for gas outburst prevention and control and for workload estimation. At present, regression methods are always used to process the numerous gas pressure data for prediction. Because there are many factors that influence the gas pressure which could lead to a deviation from actual values, the measured data do not possess basic conditions for regression methods; this can cause unexpected dangers if the methods are adopted. Based on a statistical analysis of actual measured results of coal-seam gas pressure in a same geological section in certain coal mine, two symbol measured points are selected to make a line for prediction, i.e. safety line, and the other measured points should be below the line except the abnormal points due to the confined water. It has been successfully applied in numerous coal mines in China. Particularly, this method is analyzed in this paper for the case of the No. 82 coal seam in the Taoyuan coal mine in Huaibei coalfield, China. By comparatively analyzing the relationship between gas pressure and depth from surface using regression methods, it is found that the safety line method could lead to a better prediction for deep coal-seam gas pressure, and therefore promote early warning ability and mining safety. Language: en