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This paper presents a systematic approach to evaluate the time interval of optimal maintenance strategy for the subsea process system influenced by Microbiological Influenced Corrosion (MIC) within multiple defects. The proposed method incorporates the non-homogeneous Poisson, homogeneous gamma, and non-homogeneous Markov processes for modeling the generation of multiple defects, the average pit depth growth, and maximum pit depth, respectively. The maintenance strategy comprises industrial procedure, probability of failure detection, errors sizing in-line inspection tools, management actions costs, and failure cost. The developed framework simulates maintenance strategies considering time interval, cost, probability of detection, average pit depth, and maximum pit depth and identifies the optimal strategy. The practical application is demonstrated in a North Sea subsea pipeline system under MIC’s influence. This work assists decision-makers in selecting the optimal conditioned-based maintenance strategy for the processing system. While the application is demonstrated to subsea process systems under MIC influence, the developed approach is equally applicable to other process systems.

Pipelines are the most efficient and safest means for the transportation of oil, gas, and refined petroleum products. Potentially severe consequences of pipeline failures make reliability and risk assessment an essential aspect of safe operation. However, due to limited access to industrial data, reliability and risk assessment studies often rely on experimental, synthetic, or unreliable data, which often raises questions on the proposed method’s credibility. The authors had the opportunity to access a comprehensive dataset from consecutive inline inspection (ILI) runs reporting more than seven years of degradation due to external corrosion of more than 200 km of a cross-country pipeline. This paper presents a step-by-step data processing approach and detailed statistical analysis of a cross-country pipeline’s ILI data. The paper presents stochastic models and defines the parameters required for modeling time-dependent structural integrity and risk assessment, i.e., corrosion-induced failure probability, burst pressure assessment, and containment loss. The accompanying dataset and proposed models for stochastic progress of external corrosion are hoped to serve as an essential source for pipeline risk and reliability studies.

Corrosion is known as the gradual destruction of materials, leading to structural integrity loss and deteriorates the surface function. Regarding sewage pipelines, corrosion is vital due to its substantial financial, health, and safety costs for society, and it is considered as one of the biggest problems facing water and wastewater infrastructure. Also, it is the primary cause of chemical property alteration, efficiency loss, life span reduction, etc. To overcome the resulting problems, various researches have been performed to understand not only the effective parameters leading to corrosion in sewer pipes but also monitoring the infrastructure conditions. Studies have depicted that developments in sensing systems to detect effective parameters in pipe corrosion such as temperature, H2S, and pH, have significantly reduced damage to the industrial equipment of sewage pipelines caused by corrosion. This paper presents a critical review of the effective factors resulting in sewer pipeline corrosion and discusses advanced sensing systems utilized for relevant monitoring. Also, microbiologically induced corrosion and effective factors are individually discussed. Moreover, various data analysis techniques adopted to evaluate outputs of the sensors for corrosion prediction have been explored. Finally, recommendations and future directions for improving sensing accuracy and robustness are detailed.

The main objective of this paper is to develop accurate novel frameworks for the estimation of the maximum pitting corrosion depth in oil and gas pipelines based on data-driven techniques. Thus, different advanced approaches using Artificial Intelligence (AI) models were applied, including Artificial Neural Network (ANN), M5 Tree (M5Tree), Multivariate Adaptive Regression Splines (MARS), Locally Weighted Polynomials (LWP), Kriging (KR), and Extreme Learning Machines (ELM). Additionally, a total of 259 measurement samples of maximum pitting corrosion depth for pipelines located in different environments were extracted from the literature and used for developing the AI-models in terms of training and testing.Furthermore, an investigation was carried out on the relationship between the maximum pitting depths and several combinations of probable factors that induce the pitting growth process such as the pipeline age, and the surrounding environmental properties. The results of the proposed AI-frameworks were compared using various criteria. Thus, statistical, uncertainty and external validation analyses were utilized to compare the efficiency and accuracy of the proposed AI-models and to investigate the main contributing factors for accurate predictions of the maximum pitting depth in the oil and gas pipeline.

This paper presents a methodology for quantifying the effect of harsh environmental conditions on the reliability of human actions in performing complex physical operations. A review of current human reliability techniques confirms that there is a lack of methodology for quantifying human errors while conducting complex physical operations in extreme environments. The developed methodology is based on a hierarchical Bayesian network accounting for causal dependencies among environmental factors, human error modes, and scenario-based activities. Also, a new model is developed with three reference points (awareness of the situation, access to a system, and action) that derives human error modes (HEMs) from physiological failure mechanisms and helps an analyst identify the root causes of human errors. The proposed methodology is applied to estimate the likelihood of human error in two different scenarios in harsh operating conditions in floating offshore structures. The two scenarios are a set of different human activities in a workplace under defined operational and environmental conditions. The proposed methodology helps enhance the safety of human performance while considering effective physical factors. It will also help to reform current regulations for working in harsh environments.

Produced water is a waste of significant concern due to its high volume being produced every day and complex chemical composition. In order to meet environmental regulations and standards, different techniques can be used to treat produced water. This paper first summarizes produced water composition, its related environmental impact, regulations, and standards, as well as a possible combination of different treatment techniques. This paper aims to develop a generic framework for a risk-based approach to produced water management. The proposed methodology considers the integration of environmental, technical, and economic risks in the decision-making process for produced water management. Environmental risk assessment is conducted by DREAM, Failure Mode and Effects Analysis is used to estimate technical risk, and cost-benefit analysis is performed to calculate economic risk. To integrate all the risk values, acceptable risk levels are set and compared to the calculated risk values. Experts assign weighting factors by using pair-wise comparison. The sum of the multiplied weighting factors to the ratio of calculated-acceptable risk values gives the final integrated risk. This framework can help to examine and select the most suitable treatment or reuse technique or identify potential areas for improvement in a specific site. The estimated risk can be used to justify the selection process. A case study on the produced water treatment in Thunder Horse Oil Field is presented to demonstrate the application of the proposed framework.

Failure modelling and reliability assessment of repairable systems has been receiving a great deal of attention due to its pivotal role in risk and safety management of process industries. Meanwhile, the level of uncertainty that comes with characterizing the parameters of reliability models require a sound parameter estimator tool. For the purpose of comparison and cross-verification, this paper aims at identifying the most efficient and minimal variance parameter estimator. Hierarchical Bayesian modelling (HBM) and Maximum Likelihood Estimation (MLE) approaches are applied to investigate the effect of utilizing observed data on inter-arrival failure time modelling. A case study of Natural Gas Regulating and Metering Stations in Italy has been considered to illustrate the application of proposed framework. The results highlight that relaxing the renewal process assumption and taking the time dependency of the observed data into account will result in more precise failure models. The outcomes of this study can help asset managers to find the optimum approach to reliability assessment of repairable systems.

The significant cost required for implementation of WEC sites and the uncertainty associated with their performance, due to the randomness of the marine environment, can bring critical challenges to the industry. This paper presents a probabilistic methodology for predicting the long-term power generation of WECs. The developed method can be used by the operators and designers to optimize the performance of WECs by improving the design or in selecting optimum site locations. A Markov Chain model is constructed to estimate the stationary distribution of output power based on the results of hydrodynamic analyses on a point absorber WEC. To illustrate the application of the method, the performance of a point absorber is assessed in three locations in the south of Tasmania by considering their actual long-term sea state data. It is observed that location 3 provides the highest potential for energy extraction with a mean value for absorbed power of approximately 0.54MW, while the value for locations 1 and 2 is 0.33MW and 0.43MW respectively. The model estimated that location 3 has the capacity to satisfy industry requirement with probability 0.72, assuming that the production goal is to generate at least 0.5MW power.

The blast load distribution is an important factor affecting the accuracy of structural damage and domino effect analysis caused by explosions in petroleum and chemical industries. The distribution is usually treated as the uniform or non-uniform and the latter is more suitable for the real scenarios. However, the applicability of the uniform distribution has not been studied in details. In the present study, both the blast load intensity model (BLIM) and blast damage intensity model (BDIM) are developed to represent uniform or non-uniform blast loads quantitatively. Three explosion types (free air burst, air burst and surface burst) and three target structural surfaces (rectangular plates, cylindrical shells and spherical shells) are considered in BLIM and BDIM. The element superposition method based on finite element model (FEM) is proposed, which can solve BLIM and BDIM accurately. Furthermore, the relative difference between the uniform and non-uniform distribution can be obtained on basis of BLIM and BDIM. Finally, the application condition for the rectangular plates - critical stand-off distances with the relative difference of 5%, is defined and verified to distinguish the uniform and non-uniform distribution. The study can provide an insight into the proper application of blast load distribution.

Quantitative risk assessment (QRA) techniques methodically assess the likelihood, impact, and subsequently the risk of adverse events. In a typical QRA method, one of the main intentions is to identify the critical root events which mostly contribute to the risk of the top event (TE) and requiring subsequent corrective actions (CAs). Finding the critical events which contribute to the risk is significantly dependent on the assumptions and methods applied to integrate the probabilities of different events and these events may have really low probability of occurrence. Thus, the probability reduction of the critical root events and subsequently the system’s failure does not lead to an optimal solution. For this reason, ranking the CAs without consideration of several aspects such as their influence on root events probability, inter-relationships, and direct/indirect cost, is not an appropriate approach. This study aims to introduce an approach to deal with the aforementioned situations. A novel extension to DEMATEL (decision making trial and evaluation laboratory) named Pythagorean fuzzy DEMATEL is proposed on a common probabilistic safety analysis. As a case study, a collapse in an offshore facility platform (including 42 basic events and corresponding 30 CAs) is considered to illustrate the effectiveness of the presented approach. The application of the model confirms its robustness in prioritizing critical root events and CAs compared with a conventional model, consideration of the influencing factors, and a dynamic and flexible structure.

An accidental leakage of Liquefied Natural Gas (LNG) can occur during processes of production, storage and transportation. LNG has a complex dispersion characteristic after release into the atmosphere. This complex behaviour demands a detailed description of the scientific phenomena involved in the dispersion of the released LNG. Moreover, a fugitive LNG leakage may remain undetected in complex geometry usually in semi-confined or confined areas and is prone to fire and explosion events. To identify location of potential fire and/or explosion events, resulting from accidental leakage and dispersion of LNG, a dispersion modelling of leakage is essential. This study proposes a methodology comprising of release scenarios, credible leak size, simulation, comparison of congestion level and mass of flammable vapour for modelling the dispersion of a small leakage of LNG and its vapour in a typical layout using Computational Fluid Dynamics (CFD) approach. The methodology is applied to a case study considering a small leakage of LNG in three levels of equipment congestion. The potential fire and/or explosion hazard of small leaks is assessed considering both time dependent concentration analysis and area-based model. Mass of flammable vapour is estimated in each case and effect of equipment congestion on source terms and dispersion characteristics are analysed. The result demonstrates that the small leak of LNG can create hazardous scenarios for a fire and/or explosion event. It is also revealed that higher degree of equipment congestion increases the retention time of vapour and intensifies the formation of pockets of isolated vapour cloud. This study would help in designing appropriate leak and dispersion detection systems, effective monitoring procedures and risk assessment.

Fires are major primary events in domino effects in chemical and process industries, and released thermal radiation is a main cause of accident propagation. In fire-induced domino effects, synergistic effect of multiple burning units will increase risk of domino effects, and the synergistic effect is time-dependent. In this study, a superimposition based new approach is proposed to model the contribution of synergistic effect of fires for domino effects, and a numerical solution of the approach is developed. In this approach, the synergistic effect of fires is modeled dynamically through time-variant target unit wall temperature and received thermal flux, and the receivable thermal dose is proposed as failure criterion and is modeled. The contributions of synergistic effect on the risk of domino effects are assessed by time to failure and escalation probability of target unit. The proposed approach is able to not only model the synergistic effect of fires, but also to understand the temporal evolution of synergistic effect when higher-level accidents occur. A case study demonstrates the effectiveness, advantages, extension of the proposed approach to model the contribution of synergistic effect for domino effects risk.

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 represented by 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 if the 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.

In highly complex industries, capturing and employing expert systems is significantly important to an organization's success considering the advantages of knowledge-based systems. The two most important issues within the expert system applications in risk and reliability analysis are the acquisition of domain experts' professional knowledge and the reasoning and representation of the knowledge that might be expressed. The first issue can be correctly handled by employing a heterogeneous group of experts during the expert knowledge acquisition processes. The members of an expert panel regularly represent different experiences and knowledge. Subsequently, this diversity produces various sorts of information which may be known or unknown, accurate or inaccurate, and complete or incomplete based on its cross-functional and multidisciplinary nature. The second issue, as a promising tool for knowledge reasoning, still suffers from lack of deficiencies such as weight and certainty factor, and are insufficient to accurately represent complex rule-based expert systems. The outputs in current expert system applications in probabilistic risk assessment could not accurately represent the increasingly complex knowledge-based systems. The reason is the lack of certainty and self-assurance of experts when they are expressing their opinions. In this paper, a novel methodology is presented based on the concept of Z-numbers to overcome this issue. A case study in a high-tech process industry is provided in detail to demonstrate the application and feasibility of the proposed methodology.

Human activities, including design, construction, operation, management and maintenance are all a predominant part of the daily tasks in offshore operations. It is not surprising to observe major failures are related to human error, since humans are susceptible to making mistakes. Due to the high level of uncertainty in human activities, predicting all causes of human errors is not an easy process. This may lead to inaccurate results which may affect the overall safety and reliability of marine operations. Evaluation of human endurance during activity on board is a key factor in minimizing risk of human failure. This study aims to examine uncertainties over the time of the marine operation to estimate accurate human reliability assessment. A framework is developed to model the uncertainty of human performance factors by considering a hydrodynamic analysis of the structure along with a subjective analysis of human activities under different weather conditions. Subsequently, a model based on Dynamic Bayesian approach is developed to evaluate the time duration effect on human performance throughout the operation. The developed methodology has been applied to a case study of an offshore vessel storing extracted oil. The framework demonstrates that probability of human failure increases towards the end of its operational days, however the high variation in human reliability is greatly dependent on weather conditions. The present study is able to improve the safety of human life in marine operations by predicting the reliability of performances as a function of time during a specific operation.

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.

A methodology for maintenance planning is developed which helps in improving the reliability of the components and safety performance in process facilities. This methodology helps design an optimum safety maintenance investment plan by integrating the optimization techniques and a fuzzy dynamic risk-based method. Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) is applied to deal with uncertain data. The proposed approach employs multi-experts’ knowledge which helps to optimize the maintenance investments. A separator system in an offshore process facility platform is selected as a case study to demonstrate the application of the proposed methodology. A practical example in the separator system is surveyed and potential failures and Basic Events (BEs) are identified. Finally, a risk-based maintenance plan is provided for future safety investment analysis. The results indicate that the developed methodology estimates the risk more accurately, which enhances the reliability of future process operations.

This paper focuses on the safety analysis of a nonlinear hydro-generating unit (HGU) running under different loads. For this purpose, a dynamic balance experiment implemented on an existing hydropower station in China is considered, to qualitatively investigate the stability of the system and to obtain the necessary indices for safety assessment. The experimental data are collected from four on-load units operating at different working heads including 431 m, 434 m, 437 m, and 440 m. A quantitative analysis on the safety performance of the four units was carried out by employing an integration of entropy weights method with grey correlation analysis. This assisted in obtaining the safety degree of each unit, providing the risk prompt to the operation of nonlinear hydro-generating units. The results confirm that unit 4 has the highest level of safety while unit 3 operates with the lowest safety condition. This provides the optimal operational schedule of HGUs to cope with the fluctuations of electricity demand in the studied station. The proposed methodology in this paper is not only applicable to the HGUs in the studied station but could also be adopted to assess the safety degree of any hydropower facility.

This paper presents a numerical study of operating factors on the product yields of a fast pyrolysis process in a 2-D standard lab-scale bubbling fluidized bed reactor. In a fast pyrolysis process, oxygen-free thermal decomposition of biomass occurs to produce solid biochar, condensable vapours and non-condensable gases. This process also involves complex transport phenomena and therefore the Euler-Euler approach with a multi-fluid model is applied. The eleven species taking part in the process are grouped into a solid reacting phase, condensable/non-condensable phase, and non-reacting solid phase (the heat carrier). The biomass decomposition is simplified to ten reaction mechanisms based on the thermal decomposition of lignocellulosic biomass. For coupling of multi-fluid model and reaction rates, the time-splitting method is used. The developed model is validated first using available experimental data and is then employed to conduct the parametric study. Based on the simulation results, the impact of different operating factors on the product yields are presented. The results for operating temperature (both sidewall and carrier gas temperature) show that the optimum temperature for the production of bio-oil is in the range of 500–525 °C. The higher the nitrogen velocity, the lower the residence time and less chance for the secondary crack of condensable vapours to non-condensable gases and consequently higher bio-oil yield. Similarly, when the height of the biomass injector was raised, the yields of condensable increased and non-condensable decreased due to the lower residence time of biomass. Biomass flow rate of 1.3 kg/h can produce favourable results. When larger biomass particle sizes are used, the intraparticle temperature gradient increases and leads to more accumulated unreacted biomass inside the reactor and the products’ yield decreases accordingly. The simulation indicated that the larger sand particles accompanied by higher carrier gas velocity are favourable for bio-oil production. Providing a net heat equivalent of 6.52 W to the virgin biomass prior to entering the reactor bed leads to 7.5% higher bio-oil yields whereas other products’ yields stay steady. Results from different feedstock material show that the sum of cellulose and hemicellulose content is favourable for the production of bio-oil whereas the biochar yield is directly related to the lignin content.

Maritime characteristics make the progress of automatic operations in ships slow, especially compared to other means of transportation. This caused a great progressive deal of attention for Autonomy Degree (AD) of ships by research centers where the aims are to create a well-structured roadmap through the phased functional maturation approach to autonomous operation. Application of Maritime Autonomous Surface Ship (MASS) requires industries and authorities to think about the trustworthiness of autonomous operation regardless of crew availability on board the ship. Accordingly, this paper aims to prognose the health state of the conventional ships, assuming that it gets through higher ADs. To this end, a comprehensive and structured Hierarchal Bayesian Inference (HBI)-based reliability framework using a machine learning application is proposed. A machinery plant operated in a merchant ship is selected as a case study to indicate the advantages of the developed methodology. Correspondingly, the given main engine in this study can operate for 3, 17, and 47 weeks without human intervention if the ship approaches the autonomy degree of four, three, and two, respectively. Given the deterioration ratio defined in this study, the acceptable transitions from different ADs are specified. The aggregated framework of this study can aid the researchers in gaining online knowledge on safe operational time and Remaining Useful Lifetime (RUL) of the conventional ship while the system is being left unattended with different degrees of autonomy.

Microbial fuel cells (MFCs) have recently emerged as green technology for the direct electricity generation from polluted (waste)water loaded with organic and inorganic contaminants. Despite the remarkable progress in applying MFCs to deal with different types of (waste)water, several issues, including the power density, durability, and costs of the electrode materials, are still to be tackled towards the commercialization of these technologies. The present manuscript provides a critical review of the recent advances and existing challenges in applying engineered nanomaterials (ENMs) to optimize the properties and performance of MFCs. The main advantages of the application of ENMs in the structure of MFCs are to provide a high specific surface area (SSA) for the electrodes, promote the electron transfer and oxygen reduction reactions, thereby representing a high level of biocompatibility for the adhesion of microbial communities, and being durable and cost-effective, especially when fabricated from natural resources. The sustainability aspects of ENMs-based MFC technologies and recommendations for future studies towards the development of sustainable nanomaterials-enabled developments of MFCs are discussed.

A new, facile, low-cost, and green sol–gel route for the synthesis of the MgFe2O4@CoCr2O4 magnetic nanocomposite is reported. The photocatalytic performances of the prepared magnetic nanocomposite was investigated for the degradation of organic dye under visible light irradiation. The synthesized magnetic photocatalyst depicted high degradation performance for Reactive Blue 222 dye under the optimized conditions. The nanocomposite dosage, initial dye concentration, dark and visible light, irradiation time, and reusability of photocatalysis had a notable influence on dye degradation performance. Fourier transforms infrared spectroscopy (FTIR), powder X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), dispersive X-ray analysis (EDX), Brunauer-Emmett-Teller (BET), vibrating sample magnetometer (VSM), UV–Vis diffuse reflectance spectroscopy (DRS), and elemental mapping (MAP) analysis were considered to thoroughly characterize the synthesized magnetic nanocomposite. The analysis confirmed that the MgFe2O4@CoCr2O4 had a spinel cubic structure, with a crystallite size of 11 nm, ferromagnetic activity, uniform spherical morphology, narrow bandgap, and spatial distribution of all elements that can be noticed as a merit of this method over other methods developed techniques. The fast and high-efficiency degradation for RB222 dye with 40 mg/L concentration under ambient conditions was 93% in only 10 min. Photodegradation mechanism of RB222 dye was specified in presence of radical scavenger agents and degradation pathway was verified by Gas chromatography–mass spectrometry (GC–MS) analysis. Furthermore, the as synthesized magnetic nanocomposite could be easily separated from the solution by an external magnet, structural integrity and stability of reused photocatalyst attested by FTIR, XRD, SEM, and EDX analysis and their photocatalysis performance was maintained even after four continuous runs in the same optimized condition.

Extreme value analysis (EVA) is occasionally used to predict corrosion progress. This paper adopts EVA to predict the depth of extreme pits to prioritize inspection and maintenance. It considers the peaks over threshold (POT) method to illustrate the predictive capacity of this method in assessing degradation progress based on consecutive inspection reports. The proposed approach uses distribution parameters to establish stochastic corrosion models. Four consecutive inline inspections of a pipeline are used to validate the model. As the block maxima (BM) method is often used in extreme value analysis of corrosion damage depths, the POT approach is compared to the BM's predictive results. The POT approach is considerably more capable (33%) of assessing failures in individual sections than the same workflow implemented with BM. With the downside of increased falsely categorized failures (10.6%). The method's performance in assessing failures makes it most useful for data-driven maintenance of process systems.

There is significant risk associated with increased oil and gas exploration activities in the Arctic Ocean. This paper presents a probabilistic methodology for Ecological Risk Assessment (ERA) of accidental oil spills in this region. A fugacity approach is adopted to model the fate and transport of released oil, taking into account the uncertainty of input variables. This assists in predicting the 95th percentile Predicted Exposure Concentration (PEC95%) of pollutants in different media. The 5th percentile Predicted No Effect Concentration (PNEC5%) is obtained from toxicity data for 19 species. A model based on Dynamic Bayesian Network (DBN) is developed to assess the ecological risk posed to the aquatic community. The model enables accounting for the occurrence likelihood of input parameters, as well as analyzing the time-variable risk profile caused by seasonal changes. It is observed through the results that previous probabilistic methods developed for ERA can be overestimating the risk level.

The significant increase in global energy demand has drawn the attention of oil and gas industries to exploration of less-exploited resources. Arctic offshore region is reported to hold a great proportion of un-discovered oil reserves. While this can be a promising opportunity for the industry, more exploration activities will also increase the possibility of oil spill during the entire process including production and transport. A comprehensive risk assessment based on Ecological Risk Assessment (ERA) method is then required during the planning and operation stages of future Arctic oil production facilities. In the exposure analysis stage, ERA needs an evaluation of the oil concentration profile in all media. This paper presents a methodology for predicting the stochastic fate and transport of spilled oil in ice-infested regions. For this purpose, level IV fugacity models are used to estimate the time-variable concentration of oil. A hierarchical Bayesian approach (HBA) is adopted to estimate the probability of time to reach a concentration (TRTC) based on the observations made from a fugacity model. To illustrate the application of the proposed method, a subsea pipeline accident resulting in the release of 100 t of Statfjord oil into the Labrador Sea is considered as the case study.

Maritime transportation poses risks regarding possible accidents resulting in damage to vessels, crew members and to the ecosystem. The safe navigation of ships, especially in the Arctic waters, is a growing concern to maritime authorities. This study proposes a new risk model applicable to the Northern Sea Route (NSR) to investigate the possibility of marine accidents such as collision, foundering and grounding. The model is developed using Bayesian Network (BN). The proposed risk model has considered different operational and environmental factors that affect shipping operations. Historical data and expert judgments are used to estimate the base value (prior values) of various operational and environmental factors. The application of the model is demonstrated through a case study of an oil-tanker navigating the NSR. The case study confirms the highest collision, foundering and grounding probabilities in the East Siberian Sea. However, foundering probabilities are very low in all five regions. By running uncertainty and sensitivity analyses of the model, a significant change in the likelihood of the occurrence of accidental events is identified. The model suggests ice effect as a dominant factor in accident causation. The case study illustrates the priority of the model in investigating the operational risk of accidents. The estimated risk provides early warning to take appropriate preventive and mitigative measures to enhance the overall safety of shipping operations.

The significant increase in the demand for shipping transportation using large vessels in restricted waters, such as cruising cargo vessels in channels, draws worldwide maritime industries’ attention to mitigating potential grounding risks. Safer ship navigation requires a more accurate prediction tool to estimate the likelihood of a ship striking the seabed. This study presents a safety framework for under keel clearance failure analysis of vessels crossing shallow waters. The developed methodology can be applied by the designers, operators and port managers to maintain their shipping fleets operating at an acceptable level of grounding safety. A Hierarchical Bayesian Analysis is applied to estimate the probability of touching the seabed based on the results of dynamic under keel clearance obtained from time-domain hydrodynamic simulations. To illustrate the application of the proposed method, the performance of a large vessel is assessed when entering the Queensland coastal zone with maximum water depth of 12 m. The framework suggests that for a safe navigation with maximum failure probability of 3×10−5, the vessel should cross the passage at a speed lower than 3 m/s where the maximum tolerable incident wave height is 0.5 m.

Marine floating structures are widely used in various fields of industry from oil and gas to renewable energy. The predominant dynamic responses of these structures are controlled by mooring lines. In recent years, a number of high-profile mooring failures have highlighted the high risk of this element in floating structures. A reliable design of mooring liness is necessary to improve the safety of offshore operations. This paper proposes a novel methodology to conduct reliability analysis of moored floating structures using Bayesian network (BN). The long-term distributions of extreme responses of the floating object are estimated using analytical frequency domain method, while mooring failure probability is estimated using limit state function in the proposed BN framework. Application of the methodology is demonstrated by estimating the failure probabilities of a floating cylinder with tensioned mooring system. The proposed study also explains how the hydrodynamic and reliability analysis could be integrated with BN to assess the overall safety of the offshore structures. The methodology presented can be employed to mitigate associated risk with marine structures brought about by stochastic hydrodynamic loads.

The globally expanding shipping industry has several hazards such as collision, capsizing, foundering, grounding, stranding, fire, and explosion. Accidents are often caused by more than one contributing factor through complex interaction. It is crucial to identify root causes and their interactions to prevent and understand such accidents. This study presents a detailed review and analysis of fire and explosion accidents that occurred in the maritime transportation industry during 1990–2015. The underlying causes of fire and explosion accidents are identified and analysed. This study also reviewed potential preventative measures to prevent such accidents. Additionally, this study compares properties of alternative fuels and analyses their effectiveness in mitigating fire and explosion hazards. It is observed that Cryogenic Natural Gas (CrNG), Liquefied Natural Gas (LNG) and methanol have properties more suitable than traditional fuels in mitigating fire risk and appropriate management of their hazards could make them a safer option to traditional fuels. However, for commercial use at this stage, there exist several uncertainties due to inadequate studies, and technological immaturity. This study provides an insight into fire and explosion accident causation and prevention, including the prospect of using alternative fuels for mitigating fire and explosion risks in maritime transportation.

Accidents involving vessels and/or offshore structures (henceforth referred to as marine structures) may pose high financial, environmental and fatality risk. To effectively manage these risks a methodical approach is required to model accident load and the stochastic behaviour of the marine structure that are arising from storm effects. This paper introduces a proactive framework that identifies and considers all the initial relevant risks. Compared to the conventional approaches that rely on precursor data for accident modelling, the developed methodology utilizes the critical stochastic variables directly from the hydrodynamic analysis of the floating structure. For this purpose, a novel numerical model is proposed to replicate a storm based on Endurance Wave Analysis (EWA) method. This approach reduces the computational cost (time and load) of the simulations. The critical stochastic variables are subsequently used in Bayesian Network (BN) to develop the risk model. The EWA and BN based integrated methodology assists in better understanding of accident causation and associated risk in changing operational conditions. The application of the methodology is demonstrated through a Floating Storage Unit (FSU) experiencing capsizing scenario.

Marine floating structures may experience harsh environmental conditions during their operational life span. In order to maintain a safe operation, it is necessary to evaluate the performance of the structure in extreme conditions such as storm. Previously, various approaches were introduced to analyse the response of an offshore structure in different sea states. However, the proposed methods are computationally time consuming requiring a large number of simulations and it is not the most realistic approach to analyse the dynamic behavior of a structure by separately replicating each level of storm. In this study, a novel numerical model of storm is developed based on Endurance Wave Analysis (EWA) concept. The developed model will reduce the computational cost to only one storm record of 1100 s taking into account the randomness of sea environment. The application of this method is demonstrated by assessing a Floating Storage Unit (FSU) responses encountering a storm in the North Sea. The results show that the response of structure is likely to exceed the survival condition while encountering storm level 10 corresponding to a wave height of 12.56 m. The proposed method is beneficial for future risk and reliability analyses that require a great deal of data.

Degradation of subsea pipelines in the presence of corrosive agents and cyclic loads may lead to the failure of these structures. In order to improve their reliability, the deterioration process through pitting and corrosionfatigue phenomena should be considered simultaneously for prognosis. This process starts with pitting nucleation, transits to fatigue damage and leads to fracture and is influenced by many factors such as material and process conditions, each incorporating a high level of uncertainty. This study proposes a novel probabilistic methodology for integrated modelling of pitting and corrosion-fatigue degradation processes of subsea pipelines. The entire process is modelled using a Dynamic Bayesian Network (DBN) methodology, representing its temporal nature and varying growth rates. The model also takes into account the factors influencing each stage of the process. To demonstrate its application, the methodology is applied to estimate the remaining useful life of high strength steel pipelines. This information along with Bayesian updating based on monitoring results can be adopted for the development of effective maintenance strategies.

The increasing demand by the world for energy has prompted the development of offshore oil and gas pipelines as the mode of transportation for hydrocarbons. The maintenance of these structures has also gained much attention for research and development with novel methodologies that can increase the efficiency of integrity management. This paper presents a probabilistic methodology for monitoring the condition of offshore pipelines and predicting the reliability when consideration is given to structure deterioration. Hydrodynamic simulations are carried out for an offshore pipeline to obtain the time history data from which the stress ranges are computed using a rainflow counting algorithm. To model the fatigue damage growth, a Bayesian Network (BN) is established based on a probabilistic solution of Paris’ law. Corrosion effects are also incorporated into the network providing a more realistic prediction of the degradation process. To demonstrate the application of the proposed methodology, a case study of a Steel Catenary Riser (SCR) subjected to fatigue cracks and corrosion degradation is studied. This method provided the growth rate of a crack during its lifetime during which the safety of operation can be assessed and efficient maintenance plans can be scheduled by the asset managers. The proposed method can also be applied by the designer to optimize the design of pipelines for specific environments.