Thermal risk in batch reactors: Theoretical framework for runaway and accident

Thermal safety and risk of accidents are still challenging topics in the case of batch reactors carrying exothermic reactions. In the present paper, the authors develop an integrated framework focusing on defining the governing parameters for the thermal runaway and evaluating the subsequent risk of accident. A relevant set of criteria are identified in order to find the prior conditions for a thermal runaway: failure of the cooling system, critical temperature threshold, successive derivatives of the temperature (first and second namely) vs. time and no detection in due time (reaction time) of the runaway initiation. For illustrative purposes, the synthesis of peracetic acid (PAA) with hydrogen peroxide (HP) and acetic acid (AA) is considered as case study. The critical and threshold values for the runaway accident are identified for selected sets of input data. Under the conditional probability of prior cooling system failure, Monte Carlo simulations are performed in order to estimate the risk of thermal runaway accident in batch reactors. It becomes then possible to predict the ratio of reactors, within an industrial plant, potentially subject to thermal runaway accident.     

Alternative method to prevent thermal runaway in case of error on operating conditions continuous reactor

Thermal runaway was studied in a continuous tubular pilot reactor under steady-state regime. Different accident scenarii were conducted by making some errors on reactant concentrations and/or temperature feed. To prevent thermal runaway, control by direct contact by solvent injection was used at different reactor locations. This injection allowed controlling the maximum reaction temperature. A simplified analytical method to estimate the maximum reaction temperature along the reactor was used.Benefit of this control method was the diminution of computational time. Furthermore, by injecting solvent to control maximum reaction temperature, there is no need to shut down the unit. The control method was validated experimentally.     

Uncertainty analysis framework for tubular connection sealability of underground gas storage wells

The seal failure of tubing and casing connections compromises underground gas storage well safety. This work proposes a systematic uncertainty analysis framework for connection sealability assessment. The framework covers reliability analysis and reliability sensitivity analysis and attempts to provide more effective support for the reliability design of connection seals. The reliability analysis introduces an adaptive Kriging with stopping criterion P-Monte Carlo simulation (AKP-MCS) method, which can provide a satisfactory estimate of failure probability with a small number of performance function evaluations. This metamodeling technology can effectively reduce the numerical efforts required for the reliability assessment of connection sealability. In the reliability sensitivity analysis, the refined metamodel obtained from the reliability analysis is coupled into a single-loop Monte Carlo simulation (MCS) method. The classifier attribute of this metamodel can meet the requirement of the single-loop MCS method to classify the signs of sampling points. This attribute enables sample matrices to be evaluated on this metamodel instead of the performance function, making the reliability sensitivity assessment more feasible. The proposed method is first demonstrated with four academic examples with promising results. Next, an illustrative tubular connection case is provided. The proposed scheme gives estimates of the failure probability and reliability sensitivity close to the classical model but requires less computational cost. The results of the analysis can provide useful information for the scheme decision-making and reliability optimization of connection seal design.     

On the operator action analysis to reduce operational risk in research reactors

Human errors during operation and the resulting increase in operational risk are major concerns for nuclear reactors, just as they are for all industries. Additionally, human reliability analysis together with probabilistic risk analysis is a key element in reducing operational risk. The purpose of this paper is to analyze human reliability using appropriate methods for the probabilistic representation and calculation of human error to be used alongside probabilistic risk analysis in order to reduce the operational risk of the reactor operation. We present a technique for human error rate prediction and standardized plant analysis risk. Human reliability methods have been utilized to quantify different categories of human errors, which have been applied extensively to nuclear power plants. The Tehran research reactor is selected here as a case study, and after consultation with reactor operators and engineers human errors have been identified and adequate performance shaping factors assigned in order to calculate accurate probabilities of human failure.