Dynamic risk assessment using failure assessment and Bayesian theory

To ensure the safety of a process system, engineers use different methods to identify the potential hazards that may cause severe consequences. One of the most popular methods used is quantitative risk assessment (QRA) which quantifies the risk associated with a particular process activity. One of QRA's major disadvantages is its inability to update risk during the life of a process. As the process operates, abnormal events will result in incidents and near misses. These events are often called accident precursors. A conventional QRA process is unable to use the accident precursor information to revise the risk profile. To overcome this, a methodology has been proposed based on the work of Meel and Seider (2006). Similar to Meel and Seider (2006) work, this methodology uses Bayesian theory to update the likelihood of the event occurrence and also failure probability of the safety system. In this paper the proposed methodology is outlined and its application is demonstrated using a simple case study. First, potential accident scenarios are identified and represented in terms of an event tree, next, using the event tree and available failure data end-state probabilities are estimated. Subsequently, using the available accident precursor data, safety system failure likelihood and event tree end-state probabilities are revised. The methodology has been simulated using deterministic (point value) as well as probabilistic approach. This Methodology is applied to a case study demonstrating a storage tank containing highly hazardous chemicals. The comparison between conventional QRA and the results from dynamic failure assessment approach shows the significant deviation in system failure frequency throughout the life time of the process unit.     

Effects of NO2 and SO2 on selective catalytic reduction of nitrogen oxides by ammonia

The selective catalytic reduction (SCR) characteristics of NO and NO(2) over V(2)O(5)-WO(3)-MnO(2)/TiO(2) catalyst using ammonia as a reducing agent have been determined in a fixed-bed reactor at 200-400 degrees C. The presence of NO(2) enhances the SCR activity at lower temperatures and the optimum ratio of NO(2)/NO(x) is found to be 0.5. During the SCR reactions, there are some side reactions occurred such as ammonia oxidation and N(2)O formation. At higher temperatures, the selective catalytic oxidation of ammonia and the nitrous oxide formation compete with the SCR reactions. The denitrification (DeNO(x)) conversion decreases at lower temperatures but it increases at higher temperatures with increasing SO(2) concentration. The presence of SO(2) in the feeds inhibits N(2)O formation.     

Effect of CO on NO oxidation over platinum based catalysts for hybrid fast SCR process

The oxidation characteristics of NO over Pt/TiO2 (anatase, rutile) catalysts have been determined in a fixed bed reactor as a function of O2, CO and SO2 concentrations in the presence of 8% water. The conversion of NO to NO2 increases with increasing O2 concentration up to 12% and it levels off. This saturation effect is more pronounced over rutile-Pt/TiO2 (r-Pt/TiO2) than that of anatase-Pt/TiO2 (a-Pt/TiO2). The presence of CO increases NO oxidation significantly and this enhanced effect is more pronounced on a-Pt/TiO2 than that on r-Pt/TiO2 with increasing CO concentration at lower temperatures. The same effect is also observed on the catalysts with different Pt and tungsten oxide (WO3) loadings. With increasing Pt and WO3 loadings on TiO2 support (Pt-WO3/TiO2), formation of NO2 is high even at lower temperatures. The presence of SO2 significantly suppresses the oxidation of NO over both r-Pt/TiO2 and a-Pt/TiO2 catalysts but it is less pronounced due to low stability of sulfate on a-Pt/TiO2.     

Increasing trend of primary NO2 exhaust emission fraction in Hong Kong

Despite the successful reduction in roadside NO( x ) levels, no such decrease has been detected in roadside NO(2) concentration in Hong Kong. One underlying cause could be the rising primary NO(2) fraction of the total emission of NO( x ). Primary NO(2) can be particularly detrimental to Hong Kong because a large fraction of the population are exposed to the traffic-related primary pollutants in the street canyons formed by congested high-rise buildings. In this study, hourly mean concentration data for roadside nitrogen oxides (NO( x )), nitrogen dioxide (NO(2)), and background ozone (O(3)) were used to estimate the mean primary NO(2) fraction from vehicle exhausts in Hong Kong. An overall increasing trend was observed for the primary NO(2) fraction (f-NO(2)) values in all the three roadside air monitoring sites. The primary NO(2) as a fraction of total NO( x ) (f-NO(2)) increased approximately from 2% in 1998 to 13% in 2008 in Hong Kong. The two particular periods of rising f-NO(2) coincided with the two implementation periods of the diesel retrofit programs for the light-duty vehicles and heavy-duty vehicles. Future vehicle emission control strategies should target not only total NO( x ) but also primary NO(2). Health benefit or disease burden estimates should be taken into account and updated in the process of policy planning and evaluation.     

Emission factor estimation for oil and gas facilities

Fugitive emission rate quantification in an oil and gas facility is an important step of risk management. There are several studies conducted by the United States Environmental Protection Agency (USEPA) and American Petroleum Institute (API) proposing methods of estimating emission rates and factors. Four major approaches of estimating these emissions, in the order of their accuracy, are: average emission factor approach, screening ranges emission factor approach, USEPA correlation equation approach, and unit-specific correlation equation approach. The focus of this study is to optimize the USEPA correlation equations to estimate the emission rate of different units in an oil and gas facility. In the developed methodology, the data available from USEPA (1995) is used to develop new sets of equations. A comparison between USEPA correlation equations and the proposed equations is performed to define the optimum sets of equations. It is observed that for pumps, flanges, open-ended lines, and others, the proposed developed equations provide a better estimation of emission rate, whereas for other sources, USEPA equations supply the better estimate of emission rate.     

Modelling of the effect of size on flocculent dust explosions

The effect of size on the severity of explosions involving flocculent materials has been simulated by means of a model previously developed for spherical particles and here extended to the cylindrical geometry of flock. The model consists of the identification of the regime (internal and external heating, pyrolysis/devolatilization reaction, and volatiles combustion) controlling the explosion by the evaluation of dimensionless numbers (Bi, Da, Th and Pc) and then of the estimation of the deflagration index as a function of flocculent size. The model has been validated by means of explosion data of polyamide 6.6 (nylon) at varying diameter and length. The comparison between model and experimental data show a fairly good agreement.     

A rough set-based game theoretical approach for environmental decision-making: A case of offshore oil and gas operations

Environmental decision-making in offshore oil and gas (OOG) operations can be extremely complex due to conflicting objectives or criteria, availability of vague and uncertain information, and interdependency among multiple decision-makers. Most existing studies ignore conflicting preferences and strategic interactions among decision-makers. This paper presents a game theoretical approach to solve multi-criteria conflict resolution problem under constrained and uncertain environments. Uncertainties in the quantification of imprecise data are expressed using rough numbers. A multi-criteria game is developed to model a decision problem in which three groups of decision-makers (i.e., operators, regulators and service engineers) are involved. This game is solved using the generalized maximin solution concept. With the solution (i.e., optimal weights of the criteria), the rough numbers can be aggregated to an expected payoff for each alternative. Finally, the weights of upper and lower limits of a rough number are employed to transform the expected payoff into a crisp score, based on which all alternatives are ranked to identify the best one. A numerical example is outlined to demonstrate the application of the proposed method to the selection of management scenarios of drilling wastes.     

Revised fire consequence models for offshore quantitative risk assessment

Offshore oil and gas platforms are well known for their compact geometry, high degree of congestion, limited ventilation and difficult escape routes. A small mishap under such conditions can quickly escalate into a catastrophe. Among all the accidental process-related events occurring offshore, fire is the most frequently reported. It is, therefore, necessary to study the behavior of fires and quantity the hazards posed by them in order to complete a detailed quantitative risk assessment. While there are many consequence models available to predict fire hazards-varying from point source models to highly complex computational fluid dynamic models—only a few have been validated for the unique conditions found offshore.

In this paper, we have considered fire consequence modeling as a suite of sub-models such as individual fire models, radiation model, overpressure model, smoke and toxicity models and human impact models. This comprehensive suite of models was then revised by making the following modifications: (i) fire models: existing fire models have been reviewed and the ones most suitable for offshore conditions were selected; (ii) overpressure impact model: a model has been developed to quantify the overpressure effects from fires to investigate the possible damage from the hot combustion gases released in highly confined compartments; (iii) radiation model: instead of a point/area model, a multipoint grid-based model has been adopted for better modeling and analysis of radiation heat flux consequences. A comparison of the performance of the revised models with the ones used in a commercial software package for offshore risk assessment was also carried out and is discussed in the paper.     

Space-time Bayesian small area disease risk models: development and evaluation with a focus on cluster detection

This paper extends the spatial local-likelihood model and the spatial mixture model to the space-time (ST) domain. For comparison, a standard random effect space-time (SREST) model is examined to allow evaluation of each model’s ability in relation to cluster detection. To pursue this evaluation, we use the ST counterparts of spatial cluster detection diagnostics. The proposed criteria are based on posterior estimates (e.g., misclassification rate) and some are based on post-hoc analysis of posterior samples (e.g., exceedance probability). In addition, we examine more conventional model fit criteria including mean square error (MSE). We illustrate the methodology with a real ST dataset, Georgia throat cancer mortality data for the years 1994–2005, and a simulated dataset where different levels and shapes of clusters are embedded. Overall, it is found that conventional SREST models fair well in ST cluster detection and in goodness-of-fit, while for extreme risk detection the local likelihood ST model does best.