Risk-based inspection (RBI) of a gas pressure reduction station

One of the principles of corrosion management is proper inspection methodology, and in particular the risk-based inspection (RBI). Occasionally a large percentage of the total risk of a unit is concentrated in a small part of the equipment, which can be reduced by using risk management techniques. In this study, a semi-quantitative risk-based inspection method has been performed in a gas station with for its components (pipelines, filters, etc.), to identify the existing damage mechanisms and also to rank its equipment in terms of inspection intervals. In this regard, RBI was performed for the gas station which consisted of phases (Phase A & Phase B) with Class 300 and Class 600 equipment. According to documented conditions and reviewed records the main damage mechanism for the investigated components in the gas station was identified as wall thinning due to erosion corrosion and external corrosion. In terms of inspection, results showed that most of the equipment studied had medium risk level and a few had medium to high level of risk. In this regard, the corrosion rates of Class 300 and Class 600 equipment were also studied and compared, and it was indicated that the corrosion rates were higher at locations for Class 300 equipment where more pressure drop had occurred. Overall, based on the obtained corrosion rates and obtained risk matrixes, it was concluded that wall thinning (erosion-corrosion) was the determining risk factor for equipment inspected in the gas pressure reduction station.     

Risk-based inspection for large-scale crude oil tanks

Periodic Internal Inspection Method often results in under-inspection or over-inspection for large-scale crude oil tank. Therefore, how to determine reasonable internal inspection interval (INTII) has great significance on balancing the safe operation requirement and inspection cost for crude oil tanks. Here, RBI (risk-based inspection) technology is used to quantitatively assess the risk of crude oil tanks in an oil depot in China. The risk comparison between tank shell and bottom shows that the risk of tank depends on the risk of tank bottom. The prediction procedure of INTII for crude oil tanks is also presented. The INTII predicted by RBI method is gradually extended with the increasing of the acceptable risk level. The method to determine the acceptable risk of crude oil tanks is proposed, by which 3.54E+04 are taken as the acceptable risk of the oil depot. The safety factor of 0.8 is proposed to determine the final INTIIs for 18 crude oil tanks. The INTII requirement in China code SY/T 5921, 5-7 years, is very conservative and lower than predicted service life of tanks. The INTIIs predicted by Gumbel method are smaller than by RBI method for tanks with short INTII. Therefore, this paper recommends RBI method to predict the INTII for crude oil tanks.     

Performance evaluation of process industries resilience: Risk-based with a network approach

Risk management can be defined as coordinated activities to conduct and control an organization with consideration of risk. Recently, risk management strategies have been developed to change the approach to hazards and risks. Resilience as a safety management theory considers the technical and social aspects of systems simultaneously. Resilience in process industries, as a socio-technical system, has four aspects of early detection, error-tolerant design, flexibility, and recoverability. Meanwhile, process industries' resilience has three phases: avoidance, survival, and recovery, determining the transition between normal state, process upset event, and catastrophic event. There may be various technical and social failures such as regulatory and human or organizational items that can lead to upset or catastrophic events. In the avoidance phase, the upset event is predicted, and thus, the system remains in a normal state. For the survival phase, the system state is assumed to be an upset process event, and the system tries to survive through the unhealthy process conditions or remains in the same state, probably with low performance. In the recovery phase, the system is supposed to be catastrophic, and the emergency barriers are prioritized to show the severity of the consequences and response time, leading to a resumption of a normal state. Therefore, a resilience-based network can be designed for process industries to show its inherent dynamic transition in nature. In this study, network data envelopment analysis (DEA), as a mathematical model, is used to evaluate the relative efficiency of the process industries regarding a network transition approach based on the system's internal structure. First, a resilience-based network is designed to consist of three states of normal, upset, and catastrophic events. Then, the efficiency of each industrial department, which is defined as decision-making units (DMUs), is evaluated using network DEA. As a case study, a refinery that is considered a critical process industry is assessed. Using the proposed model shows the efficient and inefficient DMUs in each of three states of normal, upset, and catastrophic events of the process and the projection onto efficient frontiers. Besides calculating the network efficiency, the performance of each state is extracted to precisely differentiate between DMUs. The results of this study, which is one of the fewest cases in the area of performance evaluation of process industries with a network approach, indicated a robust viewpoint for monitoring and assessment of risks.     

Developing a risk-based maintenance model for a Natural Gas Regulating and Metering Station using Bayesian Network

During the last decades, the vital role of maintenance activities in industries including natural gas distribution system has cleared up progressively. High costs may induce to reduced maintenance and, in turn, lead to a lower availability and high risk of undesired events. Therefore, a probabilistic model, based on an acceptable level of risk, is required to avoid under and over estimation of maintenance time interval. This paper presents an advanced Risk-based Maintenance (RBM) methodology to optimize maintenance time schedule. Bayesian Network (BN) is applied to model the risk and the associated uncertainty. The developed method can assist the asset managers to work out the exact maintenance time for each component according to the risk level. To demonstrate and discuss the applicability of the methodology, a case study of Natural Gas Reduction and Measuring Station in Italy is considered. Results prove that the most critical components are the calculator and pilots, while the most reliable one is the odorization. Furthermore, the pressure and temperature gauge (PTG), the remote control system (RCS) and the meter are predicted as the components that require less time to transit from minor risk to catastrophic risk.     

Development of a risk-based maintenance (RBM) strategy for a power-generating plant

The unexpected failures, the down time associated with such failures, the loss of production and, the higher maintenance costs are major problems in any process plant. Risk-based maintenance (RBM) approach helps in designing an alternative strategy to minimize the risk resulting from breakdowns or failures. Adapting a risk-based maintenance strategy is essential in developing cost-effective maintenance policies.The RBM methodology is comprised of four modules: identification of the scope, risk assessment, risk evaluation, and maintenance planning. Using this methodology, one is able to estimate risk caused by the unexpected failure as a function of the probability and the consequence of failure. Critical equipment can be identified based on the level of risk and a pre-selected acceptable level of risk. Maintenance of equipment is prioritized based on the risk, which helps in reducing the overall risk of the plant.The case study of a power-generating unit in the Holyrood thermal power generation plant is used to illustrate the methodology. Results indicate that the methodology is successful in identifying the critical equipment and in reducing the risk of resulting from the failure of the equipment. Risk reduction is achieved through the adoption of a maintenance plan which not only increases the reliability of the equipment but also reduces the cost of maintenance including the cost of failure.     

A framework to estimate the risk-based shutdown interval for a processing plant

Petrochemical plants and refineries consist of hundreds of pieces of complex equipment and machinery that run under rigorous operating conditions and are subjected to deterioration over time due to aging, wear, corrosion, erosion, fatigue and other reasons. These devices operate under extreme operating pressures and temperatures, and any failure may result in huge financial consequences for the operating company. To minimize the risk and to maintain operational reliability and availability, companies adopt various maintenance strategies. Shutdown or turnaround maintenance is one such strategy. In general, shutdown for inspection and maintenance is based on the original equipment manufacturer's (OEM) suggested recommended periods. However, this may not be the most optimum strategy given that operating conditions may vary significantly from company to company.The framework proposed in this work estimates the risk-based shutdown interval for inspection and maintenance. It provides a tool for maintenance planning and decision making by considering the probability of the equipment or system for failure and the likely consequences that may follow. The novel risk-based approach is compared with the conventional fixed interval approach. This former approach, characterized as it is by optimized inspection, maintenance and risk management, leads to extended intervals between shutdowns. The result is the increase in production and the consequent income of millions of dollars.The proposed framework is a cost effective way to minimize the overall financial risk for asset inspection and maintenance while fulfilling safety and availability requirements.     

On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions

Since gas plants are progressively increasing near urban areas, a comprehensive tool to plan maintenance and reduce the risk arising from their operations is required. To this end, a comparison of three Risk-Based Maintenance methodologies able to point out maintenance priorities for the most critical components, is presented in this paper. Moreover, while the literature is mostly focused on probabilistic analysis, a particular attention is directed towards consequence analysis throughout this study. The first developed technique is characterized by a Hierarchical Bayesian Network to perform the occurrence analysis and a Failure Modes, Effects and Criticality Analysis to assess the magnitude of the adverse outcomes. The second approach is a Quantitative Risk Analysis carried out via a software named Safeti. Finally, another software called Synergi Plant is adopted for the third methodology, which provides a Risk-Based Inspection plan, through a semiquantitative risk analysis. The proposed study can assist asset manager in adopting the most appropriate methodology to their context, while highlighting priority components. To demonstrate the applicability of the approaches and compare their rankings, a Natural Gas Regulating and Measuring Station is considered as case study. The results showed that the most suited method strongly depends on the available data.     

Development of collision risk indicators for autonomous subsea inspection maintenance and repair

The objective of this article is to present a method for developing collision risk indicators applicable for autonomous remotely operated vehicles (AROVs), which are essential for promoting situation awareness in decisions support systems. Three suitable risk based collision indicators are suggested for AROVs namely, time to collision, mean time to collision and mean impact energy. The proposed indicators are classified into different thresholds; low, intermediate and high. An AROV flight path is simulated to gather input data to calculate the proposed indicators and three collision targets are established, i.e., subsea structure, seabed and a cooperating AROV. The proposed indicator development method together with the case study show a proof-of-concept that the combination of mean time to collision and mean impact energy indicators can identify risk prone waypoints in the AROV path. The method results in an overall risk picture for a given AROV path. The results may provide useful input in replanning of mission paths and for implementation of risk reducing measures. Even though the method focuses on collision risk, it can be used for other accident scenarios for AROVs.     

Loss prevention in turnaround maintenance projects by selecting contractors based on safety criteria using the analytic hierarchy process (AHP)

Turnaround maintenance (TAM) is a usual plant shutdown that is done by process industries to perform asset inspections, repairs, and overhauls. TAM engineering projects are needed on a periodic basis to optimize a plant's performance. Process industrial plants usually don't have enough in-house manpower to carry out needed TAM jobs; it is not uncommon for a process industrial plant to outsource its TAM manpower supply to external contractors. This will create a peculiar management challenge regarding how to assure orientation of safety practices for the newly hired labor. Although accidents during process plant shutdowns may have severe consequences, the safety management systems in place for many companies only cover normal operations and few explicitly address TAM projects. Usually, contractors are evaluated from a safety perspective after handling the project by monitoring safety indicators such as number of injuries, incidents, etc. This paper emphasizes safety attributes to be included in selecting contractors at the bidding stage to assure loss prevention and better safety orientation during TAM implementation. The paper starts with reviewing TAM literature and defining main needed safety attributes to prequalify contractors. It develops an analytic hierarchy process (AHP) model that is applied to rank TAM contractors' selection. A case study is presented for a petrochemical plant in Saudi Arabia to demonstrate the AHP model.     

Risk-based quantitative method for determining blast-resistant and defense loads of petrochemical buildings

Petrochemical buildings are usually distributed near chemical installations and have a high risk of explosion because of the concentration of people. In order to effectively design and protect buildings against explosion, it is needed to determine the blast-resistant and defense loads reasonably. Based on the theory of risk, a triangular pyramid explosion risk model was established in this study, which combined the overpressure p, duration t, and frequency f of the explosion scene at the same time. The first principle of “acceptable cumulative frequency” and the key principle of “maximum explosion risk” were formulated. According to this method, the explosion risk of eight leakage units with 10 groups of leakage hole size and three dangerous wind directions were obtained. According to the cumulative explosion frequency curve and the explosion risk curve, blast-resistant and defense loads of the four walls were determined quantitatively. Among the four walls, the explosion overpressure were 44.0–74.5 kPa, and the corresponding duration were 34.1–39.1 ms. The cumulative explosion frequency were 2.11E−5 to 8.58E−5 times annually. The explosion risk value were 3.64E−3 to 5.35E−3 kPa·ms annually. The results indicated that it was of great importance for the calculation of the explosion risk to reasonably divide the leakage unit and determine the leakage frequency. The explosion scene and its frequency, the volume of the obstructed region, and the distance of the explosion source were the key variables that affected the explosive load. The final blast-resistant and defense load values were found in the case of the middle hole size leakage. Blast-resistant and defense loads not only met the risk acceptance standard but also considered the overpressure and the duration of explosion. At present, they have been extensively applied in the blast-resistant design and engineering transformation of buildings in SINOPEC.     

Dynamic Inherently Safer Modifications: Metric development and its validation for fire and explosion prevention

Of the numerous inherent safety assessment tools, a dynamic metric capable of investigating and incorporating the temporal risk evolution when conducting Inherently Safer Modifications (ISMs) is yet to be established. To this end, this work developed a Dynamic Inherent Safety Metric (DISM) and validated its functionality and viability through a case study. Firstly, the Information-Flow-based Accident-causing Model (IFAM) was adapted to construct the topology of Bayesian Networks (BN). Then, Bayesian deductive reasoning was executed to do crucial risk identification by ranking posterior probabilities. Finally, risk-based ISMs were performed to address the relatively contributing risk factors. The case study results show that the fire and explosion risk decreased by approximately a third after implementing ISMs, thus demonstrating that the modified processing scenario could be inherently safer than the original processing scenario. The newly developed inherent safety metric (i.e., DISM) can assist in temporal risk identification and assessment, and it is expected to function as a novel assessment tool for measuring and comparing the inherent safeness before and after implementing ISMs with simultaneous considerations on the time-varying risk factors.     

A Bayesian network-based approach for the assessment and management of ageing in major hazard establishments

Currently, there is an increasing attention towards ageing of industrial equipment, as the phenomenon has been recognised as a cause of severe accidents, recorded in the last years in many process establishments. Recent studies described ageing through a number of key-factors affecting the phenomenon by accelerating or slowing it down. The Italian Competent Authority for the prevention of chemical accidents (Seveso III Directive) adopted a short-cut method, accounting for the assessment of these factors, to evaluate the adequateness of ageing management during inspections at Seveso sites. In this paper, a Bayesian Network was developed, by using the data gathered during the first application of the short-cut method, with the aim to verify the robustness of the approach for ageing assessment and the validity of the a priori assumptions used in assessing the key-factors. The structure of the Bayesian network was established by using experts’ knowledge, whereas the Counting Learning algorithm was adopted to execute the parameter learning by means of the software Netica. The results showed that this network could effectively explore the complex logical and uncertain relationships amongst factors affecting equipment ageing. Results of the present study were exploited to improve the short-cut method.     

Improving safety and availability of complex systems using a risk-based failure assessment approach

This paper presents a structured risk-based failure assessment (RBFA) approach, which provides a complete solution to avoid repeated and potential failures to improve overall plant safety and availability. Technological advancements and high product demand have encouraged designers to design mega-capacity systems to enhance system utilization and improve revenues. However, these benefits make the systems more complex and thus prone to unnoticed failure. It is an overwhelming task to address all the failures due to the limited resources and time constraints. This leads to substandard and poor quality failure assessments, which cause repeated failures. To address this common industry concern, a four phase RBFA framework is proposed which is not limited to the identification of root cause(s) but also includes other actions such as failure monitoring. The four phases include the plan phase, the assessment phase, the analysis phase and the implementation-tracking phase. These phases cover identification of failure, failure analysis, root cause(s) analysis, and failure monitoring. In this paper, the applicability and advantages of the proposed approach are examined through two real case studies pertaining to bearing failure and drive coupling failure. By implementing the proposed approach, significant improvements have been experienced in the system availability in both the cases.     

Technical,human, and organizational factors affecting failures of firefighting systems (FSs) of atmospheric storage tanks: Providing a risk assessment approach using Fuzzy Bayesian Network (FBN) and content validity indicators

Most risk assessment methods have problems such as uncertainty, static structure, and lack of validation. Also, in most of these studies, less attention has been paid to human, managerial, and organizational issues. Therefore, this study proposes a risk assessment method based on the Fuzzy Bayesian Network (FBN) to prevent failure of firefighting systems (FFSs) in the atmospheric Storage Tanks of a Petrochemical Industry. The first stage of the study is the development of a fault tree (FT) and investigation of basic events (BEs). In this study, content validity indices and brainstorming technique were used to validate the FT structure and reduce the uncertainties of Completeness, Modeling, and Parameter. After determining the probability of basic events (BEs) by the expert team opinions and fuzzy logic, events were transmitted to the Bayesian Network (BN) and then analyzed with deductive and inductive reasoning, followed by sensitivity analysis in the GeNIe software. Finally, results of a case study in the Atmospheric Storage Tanks of the Methanol Floating Roof of a Petrochemical Industry showed that FBN simulation and FT validation could provide a practical way to determine FFSs probabilities, identify impactful events, and reduce the above uncertainties. Also, taking account of hidden factors of events, such as organizational and managerial factors, can help managers to prevent FFSs in tanks.     

A new approach for layout optimization in maintenance workshops with safety factors: The case of a gas transmission unit

In this study, an Integrated Simulation-Data Envelopment Analysis (DEA) approach is presented for optimum facility layout of maintenance workshop in a gas transmission unit. The process of repair of incoming parts includes various operations on different facilities. The layout problem in this system involves determining the optimum location of all maintenance shop facilities. Layout optimization plays a crucial role in this type of problems in terms of increasing the efficiency of main production line. Standard types of layouts including U, S, W, Z and straight lines are considered. First, the maintenance workshop is modeled with discrete-event-simulation. Time in system, average waiting time, average machine utilization, average availability of facilities, average queue length of facilities (AL) and average operator utilization are obtained from simulation as key performance indicators (KPIs) of DEA. Also, safety index and number of operators are considered as other KPIs. Finally, a unified non-radial Data Envelopment Analysis (DEA) is presented with respect to the stated KPIs to rank all layouts alternatives and to identify the best configuration. Principle Component Analysis (PCA) is used to validate and verify the results. Previous studies do not consider safety factor in layout design problems. This is the first study that presents an integrated approach for identification of optimum layout in a maintenance workshop of gas transmission unit by incorporating safety and conventional factors.     

Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management

Reliability data reflects equipment safety and provides a reference for setting inspection period, thereby serving as crucial information for the implementation of equipment integrity management policies. The calculation foundation of reliability data is maintenance records of adequate data quality. However, maintenance records of doubtful quality are common. Despite excluding poor quality recodes and using only the remaining maintenance recodes to calculate the reliability data, the calculated results generally lack a sufficient degree of confidence. This study applied data mining technology, including quality metrics, the association rule, and clustering, to explore the cause of low-quality maintenance data. The results revealed that the low data quality of maintenance records was due to ineffective maintenance policies, the low integrity of key system columns, nonadherence to the policy, and misunderstanding of column definitions. The proposed method successfully identified the causes of low-quality maintenance records. By incorporating the method into the function module of a CMMS, operators can equip the system with self-diagnosis, self-supervision, and continuous optimization functions.     

Qualitative risk assessment of a Dual Fuel (LNG-Diesel) system for heavy-duty trucks

A Dual Fuel (LNG-Diesel) system can be applied to heavy-duty diesel trucks for achieving environmental benefits in comparison to existing diesel vehicles. Because of lack of reports about risk assessment of this technology, we performed a qualitative assessment based on a framework of some literature techniques for risk identification, analysis and evaluation. After constructing a Reliability Block Diagram (RBD) to establish the context, we conducted bow-tie analysis, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), likelihood and consequence analysis, and used a risk matrix. We applied these methods and techniques qualitatively to identify causes (e.g. collisions), critical events (e.g. releases of natural gas), related consequences (e.g. fires and explosions), and different possible pathways from a specific cause to its consequence, and to assess some negative accident scenarios related to use and parking of the vehicle. The bow-tie analysis also allowed to make explicit barriers and controls that prevent critical events and/or mitigate consequences. Therefore, we identified a set of safety measures, including design, technical, management, and emergency actions, which shall be implemented in each step of the system's life cycle.Our risk assessment showed that the risk level of the Dual Fuel (LNG-Diesel) system is similar to the risk level of a traditional diesel system. Future research will overcome current lack of data and, therefore, permit a quantitative rating of the risk of the Dual Fuel (LNG-Diesel) system.