Comprehensive quantitative dynamic accident modelling framework for chemical plants

This thesis introduces a comprehensive accident modelling approach that considers hazards associated with process plants including those that originate from the process itself; human factors including management and organizational errors; natural events related hazards; and intentional and security hazards in a risk assessment framework. The model is based on a series of plant protection systems, which are release, dispersion, ignition, toxicity, escalation, and damage control and emergency management prevention barriers. These six prevention barriers are arranged according to a typical sequence of accident propagation path. Based on successes and failures of these barriers, a spectrum of consequences is generated. Each consequence carries a unique probability of occurrence determined using event tree analysis. To facilitate this computation, the probability of failure for each prevention barrier is computed using fault tree analysis. In carrying out these computations, reliability data from established database are utilized. On occasion where reliability data is lacking, expert judgment is used, and evidence theory is applied to aggregate these experts’ opinion, which might be conflicting. This modelling framework also provides two important features; (i) the capability to dynamically update failure probabilities of prevention barriers based on precursor data, and (ii) providing prediction of future events. The first task is achieved effectively using Bayesian theory; while in the second task, Bayesian-grey model emerged as the most promising strategy with overall mean absolute percentage error of 18.07% based on three case studies, compared to 31.4% for the Poisson model, 22.37% for the first-order grey model, and 22.4% for the second-order grey model. The results obtained illustrated the potentials of the proposed modelling strategy in anticipating failures, identifying the location of failures and predicting future events. These insights are important in planning targeted plant maintenance and management of change, in addition to facilitating the implementation of standard operating procedures in a process plant

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

Operational risk analysis for undesired polymerization in overhead condenser of butadiene column

An accident model based on fault tree analysis was developed and applied to analyze the recurrence of undesired reactions producing popcorn polymers in the overhead condenser of an industrial butadiene distillation column. The modeling framework incorporates reliability data associated with asset integrity and human performance along with data on selected process variables. These variables, i.e., pressure, feed velocity and temperature of the condenser, which were identified from root cause analysis of the incident provided dynamic contributions to the model, and were represented in the form of Weibull distribution functions. The results obtained proved the potentials of the proposed methodology. Based on the case study considered, operating pressure was identified as the most influential process variables that needed closer monitoring. The methodology provides an opportunity for risk management to be implemented dynamically to facilitate maintenance plan and management of change

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

Modeling the impact of natural and security hazards in an LNG processing facility

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 manmade 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&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

Multi-state analysis functional models using Bayesian networks

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

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 furthe

Risk-based interventions for safer operation of a hydrogen station

In ensuring safety of plant operation, both the reliability of plant components and human resources are important. Moving towards optimising resources, there is a need to prioritise intervention measures such as maintenance program and review of standard operating procedures. In this paper, a technique known as basic event ranking approach (BERA) is applied to a hydrogen station. BERA examines the relative importance of plant components based on their probability of failure within the realm of fault tree analysis model, and yields values of importance index for each basic event investigated. To incorporate changes in reliability data throughout the plant lifetime, a dynamic extension to BERA is introduced. The vulnerability of plant components and human actions are ranked with respect to the selected top events of fault trees generated from plant functions. The results revealed the potential of BERA to facilitate risk-based intervention initiatives to support process safety

Control and optimization of aromatic compounds in multivariable distillation column

Product separations in petroleum refineries depend significantly on distillation process, which is known to be challenging to be optimally managed, especially when multiple products with variety of purity requirements are involved due to nonlinear dynamics and high degree of process interactions. In this paper, control and optimization aspects of a multivariable distillation process are discussed. A mathematical model of the system is simulated in MATLAB programming environment, and analyses of process behavior and control performances are carried out. The controllers are tuned using conventional Ziegler-Nichols method and L-V control configuration was adopted. The results on disturbance rejection and set point tracking capabilities, in order to maintain the purity of benzene in the distillate above 98.5 % are discussed. Based on these insights, the optimum operating conditions were determined, which serves as a good starting point for further works in addressing variety of problems related to process operations