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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.

Offshore structures and operations significantly impact the surrounding environment, and future offshore renewable energy developments are no exception. Many Australian offshore oil and gas platforms have the potential to act as artificial reefs, creating an excellent opportunity for the growth of marine communities that are often in danger due to other human activities or environmental impacts. Decommissioning of these platforms will impose substantial risks to the existing ecosystem in those habitats. Therefore, there is a need to understand the environmental impacts of installation, operation, and maintenance (O and M) and decommissioning activities in new offshore renewable energy infrastructure prior to developments. This becomes more important in the case of future offshore wind farm development in Australia, where no historical information is available. This paper proposes a game-theoretical approach to identifying the optimum risk mitigation strategies for minimizing the environmental impact of future offshore wind infrastructure and operations. As a case study, the method is applied to examine the risks imposed by constructing and installing an offshore wind farm in the Bass Strait. The governments and regulators can adopt the proposed mitigation strategies to develop policies and legislation to ensure the sustainability of marine environments.

It is critical to comprehend the safety aspects of hydrogen fuel cell vehicles (FCVs) in semi-confined and confined environments. The hydrogen jet fire is a key hazard resulting from coincidental hydrogen release from onboard storage followed by ignition. The rise in temperature and depletion of oxygen inside the tunnel may cause calamitous debacles. In this study, comprehensive computational fluid dynamics (CFD) simulations were designed to understand the interactions of multiple hydrogen fires in a confined environment. CFD simulations for hydrogen and liquefied petroleum gas (LPG) jet fires were conducted inside a reduced scale model tunnel. The model is initially validated against the experimental data for a single LPG fire scenario. A parametric study was then made to understand the impact of the fire location in the tunnel and the ventilation velocity. The results show an increasing-decreasing trend in the temperature over the two fire sources before the temperature reaches a quasi-steady state for the cases without ventilation speed. However, in the presence of ventilation velocity, the temperature rises are seen until a quasi-steady-state is reached. The vicinity of the two flames in the tunnel influences ignition proficiency, dependent on the heat feedback enhancement and air supply restriction instruments. A temperature drop was seen at the ceiling as we move along the passage length. The ventilation velocity influences the proportion of fuel and oxygen, driving the burning proficiency to either increase or decrease. The overall ceiling temperatures were seen to reduce in the presence of higher ventilation velocity. In conclusion, for hydrogen and LPG fire interaction, a distinction in the ceiling temperature pattern was seen between the two because of the disparity in the emissivity of the two fuels.

The formation of a butt weld bead occurs at the conjoined pipes during the process of butt-welding for pipelines in water and wastewater networks. Pressure losses in pipes with weld beads are often underestimated in design practice that affects the safety and reliability of the system. This paper focuses on the effect of weld beads and pressure loss in polyethylene (PE) pipes using computational fluid dynamics (CFD) simulations. The CFD model is first validated against the theoretical results and previous weld bead pressure loss experimental data. Both k-e and k-ω turbulent models are examined, confirming k-ω shear stress transport (SST) model agree better with the theoretical pressure loss, which was considered for all inlet Reynolds numbers. The CFD simulations are conducted for long PE weal bead pipes with varying pipe diameters. The results confirm that as the pipe diameter increases the local head loss increases although the difference between 500 mm and 800 mm at lower inlet Reynolds number is marginal. Finally, the effect of weal bead shape was studied for the 800 mm pipe. Among bead’s height, width and cleavage depth, the bead’s height has a more significant impact, demonstrating that the pressure head loss is minimised when the bead’s height is minimum. The results confirm the head loss can be reduced by 5% for optimised weld bead shape, ensuring the water network is more cost-effective and reliable.

To identify and assess the impact of various hazards threaten agriculture water supply systems, the development of a risk analysis framework is inevitable in promoting sustainable agricultural development. This study aims to develop a novel multi-hazard risk assessment model by applying a Hybrid Bayesian Network for agricultural water supply and distribution systems. This model consists of discrete and continuous nodes and their probable interactions. The structure of this model is designed to assess the risk associated with the agricultural water system's for supply and distribution sections separately considering various factors such as river discharge, the inflow of water distribution system and its fluctuation, and the demand of water. The developed model is applied to the Roodasht Irrigation district located in the center of Iran to demonestrate its capability. This Irrigation district is under the threat of drought, improper performance of the ditch-riders, and operational losses. The results showed that the model in both training and test datasets has proper accuracy and performance with the root mean square error of 0.069 and 0.076, coefficient of determinations equal to 0.717 and 0.690, and the overall index of model performance equal to 0.787 and 0.671, respectively. The results of this research and the proposed model will help stakeholders and decision-makers to be aware of the probable causes and the extent of system failure and its components due to the threatening hazards. Also, it will assist in planning the allocation of irrigation water based on predictable risk associated with various hazards.

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.