Multi-state analysis of process status using multilevel flow modelling and Bayesian network

Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method

Dynamic control of sensor and actuator failures in multivariable distillation column

This paper examines the impact of sensor and actuator failures in the operation of a multivariable distillation column. Several failure scenarios are evaluated including failures of sensors and actuators in various scales of magnitudes and durations. The results obtained illustrate the ability of process controllers in suppressing the impact of these unwanted events. Closed-loop dynamic responses of the process revealed capabilities of these controllers in dealing with upsets that are small in magnitude and duration. In the case of larger and longer process upsets, process controllers are not adequate in providing the necessary corrective measures. This leaves the necessary interventions to be taken by the plant operators, following alarms that would have been triggered in typical plant operation scenario

Modelling an integrated impact of fire, explosion and combustion products during transitional events caused by an accidental release of LNG

In a complex processing facility, there is likelihood of occurrence of cascading scenarios, i.e. hydrocarbon release, fire, explosion and dispersion of combustion products. The consequence of such scenarios, when combined, can be more severe than their individual impact. Hence, actual impact can be only representedby integration of above mentioned events. A novel methodology is proposed to model an evolving accident scenario during an incidental release of LNG in a complex processing facility. The methodology is applied to a case study considering transitional scenarios namely spill, pool formation and evaporation of LNG, dispersion of natural gas, and the consequent fire, explosion and dispersion of combustion products using Computational Fluid Dynamics (CFD). Probit functions are employed to analyze individual impacts and a ranking method is used to combine various impacts to identify risk during the transitional events.The results confirmed that in a large and complex facility, an LNG fire can transit to a vapor cloud explosion ifthe necessary conditions are met, i.e.the flammable range, ignition source with enough energy and congestion/confinement level. Therefore, the integrated consequences are more severe than those associated with the individual ones, and need to be properly assessed. This study would provide an insight for an effective analysis of potential consequences of an LNG spill in any LNG processing facility and it can be useful for the safety measured design of process facilities

Accident modelling and analysis in process industries

Accident modelling is a methodology used to relate the causes and effects of events that lead to accidents. This modelling effectively seeks to answer two main questions: (i) Why does an accident occur, and (ii) How does it occur. This paper presents a review of accident models that have been developed for the chemical process industry with in-depth analyses of a class of models known as dynamic sequential accident models (DSAMs). DSAMs are sequential models with a systematic procedure to utilise precursor data to estimate the posterior risk profile quantitatively. DSAM also offers updates on the failure probabilities of accident barriers and the prediction of future end states. Following a close scrutiny of these methodologies, several limitations are noted and discussed, and based on these insights, future work is suggested to enhance and improve this category of models further

MODELING THE IMPACT OF NATURAL AND SECURITY HAZARDS IN AN LNG PROCESSING FACILITITY

Development of accident models based on cause and effect relationships facilitates the formulation of accident prevention and mitigation plans in the Chemical Process Industries (CPIs). In this paper, failures of accident prevention barriers triggered by man-made and natural hazards are causally modeled using Fault Trees (FTs) models. Additionally, updated technique of FTs basic and top events failure probabilities was applied using Hierarchy Bayesian Approach (HBA) based on basic events precursor data. This updated methodology overcomes the uncertainty limitation in the determination of FTs reliability data, as well as converge them into their accurate values. Moreover, it provides valuable information supporting risk based decision. The methodology was applied to LNG pipeline and liquefaction plant Dispersion Prevention Barrier (DPB). The result shows the capability of the methodology to model natural and security hazards (NE&amp;ISHs) in both qualitative and quantitative manners, as well as, to update FT events failure probabilities through the use of the precursor data to the HBA. Outcomes demonstrate that the average posterior failure probability of DPB of that particular case study increased from 0.0613 to 0.204232 which represents a 3.33 times increment compared with the prior. </jats:p

Accident modelling and safety measure design of a hydrogen station

An accident modelling approach is used to assess the safety of a hydrogen station as part of a ground transportation network. The method incorporates prevention barriers associated to human factors, management and organizational failures in a risk assessment framework. Failure probabilities of these barriers and end-states events are predicted using Fault Tree Analysis and Event Tree Analysis respectively. Results from the case study considered revealed the capability of the proposed method in estimating the likelihood of various outcomes as well as predicting their future probabilities. In addition, the scheme offers an opportunity to provide dynamic adjustment by updating the failure probability with actual plant data. Results from the analysis can be used to plan maintenance and management of change as required by the plant condition