It is proposed to use the new approach to assessing quantitative risk indicators. This approach allows to consider the temporal non-stationarity of the number of processes, including the development of an accident and the spatial movements of people.
The greatest uncertainty in the risk analysis with an explosive and fire hazard component is not the frequency of initiating events used, but, for example, data on the probability of ignition. The range of variation of this probability is about two orders of magnitude (relatively speaking, from 1 % to 100 %), and the criteria and factors that determine the choice of this value are not always clearly defined. The paper proposes an approach that considers the probability of ignition as a dependence on the time that passed after the start of emergency depressurization. Knowing this dependence, it is possible to consider several scenarios with different ignition time after the start of the release and assign certain consequences and probabilities to each scenario. Moreover, it is possible for each single scenario on a specific piece of equipment (pipeline section) to obtain non-stationary, namely time-varying potential risk fields.
The example of an accident on the oil pipeline is considered, the risk indicators of such an accident are calculated, it is shown that the risks can change over time, namely they are non-stationary characteristics.
Further, this fact is transformed into the development of theoretical foundations for quantitative risk assessment, considering the non-stationarity of various processes occurring during emergency situations arising during the operation of equipment, individual behavior of people and changes in external conditions.
The results obtained show the importance of considering the changes that occur during an emergency on the main oil and product pipelines. It is concluded that the proposed approach allows to reduce the conservatism of assessments provided by traditional methods. In real practice this approach can reasonably reduce the risk indicators by several times, sometimes by orders of magnitude.
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