• Energy Research

This paper presents a novel methodology for site selection of Offshore Renewable Energy (ORE) systems, addressing the growing global energy demand and the need for sustainable solutions to climate change. Focusing on the wave energy, a relatively untapped renewable energy source, this research focuses on optimizing the Wave Energy Converter (WEC) deployment amidst the uncertainties of the offshore environment. The study involves a comprehensive evaluation of potential sites, considering key factors like power generation capacity, mooring system fatigue life, and tether response to extreme loads. Initial wave data analysis for various locations is followed by numerical simulations of a point absorber WEC under different environmental conditions. A Bayesian Network (BN) model is then employed to integrate uncertainties into a multi-criteria decision-making (MCDM), enhancing the robustness of site selection. This approach facilitates the calculation of utility values for various sites, leading to the identification of the optimal decision alternative based on maximum expected utility. This work provides a detailed framework for renewable energy stakeholders, helping in the assessment of both profitability and survivability of WECs in chosen locations. It significantly contributes to minimizing economic and performance risks associated with ORE system installations, promoting efficient and sustainable energy production from ocean resources.

Abstract 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.

Abstract 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.

Abstract Fluidisation and mixing of sand and biomass particles have significant effects on product yields in bubbling fluidised bed reactors (BFBR). There is a lower limit for the velocity of fluidising media known as minimum fluidisation velocity. Similarly, an upper limit is defined for velocity where the bio-oil production is maximised – known as the maximum effective velocity (MEV). A computational fluid dynamics (CFD) simulation for biomass fast pyrolysis process in a 2-D lab-scale BFBR is developed to analyse the variation of MEV as the sand particle size varies. The model is first validated using the experimental data. Then, a parametric study is conducted for the carrier gas velocities in a range of 0.3–1.1 m/s where the sand particle sizes vary from 0.4 to 1 mm, and the biomass particles are in a range of 0.2–0.5 mm. Effects of the packing limit are also analysed. For this purpose, the different sand particle sizes and their corresponding MEV are tested at the sand packing limits of 45, 55, 65, and 75 mm in which the optimal packing limit is found to be 55 mm. A detailed thermodynamic study is performed with the focus on the sand particle size and packing limit affecting the heat transfer rates between phases. A decrease in required heat transfer rates is directly linked to an increase in bio-oil yield. It is observed that heat transfer between sand-biomass is much more efficient than the heat transfer between nitrogen-biomass, confirming the importance of the sand particles as the heat carriers.

Green hydrogen is a key element that has the potential to play a critical role in the global pursuit of a resilient and sustainable future. However, like other energy production methods, hydrogen comes with challenges, including high costs and safety concerns across its entire value chain. To overcome these, low-cost productions are required along with a promised market. Offshore renewables have an enormous potential to facilitate green hydrogen production on a large scale. Their plummeting cost, technological advances, and rising cost of carbon pave a pathway where green hydrogen can be cost-competitive against fossil-fuel-based hydrogen. Offshore industries, including oil and gas, aquaculture, and shipping, are looking for cleaner energy solutions to decarbonize their systems/operations and can serve as a substantial market. Offshore industrial nexus, moreover, can assist the production, storage, and transmission of green hydrogen through infrastructure sharing and logistical support. The development of offshore green hydrogen production facilities is in its infancy and requires a deeper insight into the key elements that govern decision-making during their life-cycle. This includes the parameters that reflect the performance of hydrogen technology with technical, socio-political, financial, and environmental considerations. Therefore, this study provides critical insight into the influential factors discovered through a comprehensive analysis that governs the development of an offshore green hydrogen system. Insights are also fed into the requirements for modelling and analysis of these factors, considering the synergy of hydrogen production with the offshore industries, coastal hydrogen hub and onshore energy demand. The results of this critical review will assist the researchers and developers in establishing and executing an effective framework for offshore site selection in largely uncertain and hazardous ocean environments. Overall, the study will facilitate the stakeholders and researchers in developing ...

Microbial fuel cell (MFC) is a variant of the bioelectrochemical system that uses microorganisms as biocatalysts to generate bioenergy by oxidizing organic matter. Due to its two-prong feature of simultaneously treating wastewater and generating electricity, it has drawn extensive interest by scientific communities around the world. However, the pollution purifying capacity and power production of MFC at the laboratory scale have tended to remain steady, and there have been no reports of a performance breakthrough. In recent years, research related to MFC has demonstrated a new trend, namely the coupling of MFC with other wastewater treatment technologies to create a 1 + 1 > 2 impact. MFC-based coupling/hybrid technologies such as sediment MFC (SMFC), constructed wetland MFC (CW-MFC), membrane bioreactor MFC (MBR-MFC), microbial desalination cell (MDC), and MFC coupled nutrient recovery technology, etc. have been increasingly studied. Therefore, this review aims to overview these already-emerging MFC coupling technologies and explores their development trends and challenges to serve as a guide for determining priority research topics in this area. Among these MFC-based coupling/hybrid technologies, literature seems to support that CW-MFC is a good example of integrated MFC technology where CWs are already employed at the field level for wastewater treatment application. MFC-Electroflocculation and MBR-MFCs are typical emerged hybrid systems to own promising potential. However, scalability and practical application potential of these integrated technologies are the challenge towards their reality except for ideal performance in small scale trials.

Abstract Deepwater drilling is one of the high-risk operations in the oil and gas sector due to large uncertainties and extreme operating conditions. In the last few decades Managed Pressure Drilling Operations (MPD) and Underbalanced Drilling (UBD) have become increasingly used as alternatives to conventional drilling operations such as Overbalanced Drilling (OVD) technology. These newer techniques provide several advantages however the blowout risk during these operations is still not fully understood. Blowout is regarded as one of the most catastrophic events in offshore drilling operations; therefore implementation and maintenance of safety measures is essential to maintain risk below the acceptance criteria. This study is aimed at applying the Bayesian Network (BN) to conduct a dynamic safety analysis of deepwater MPD and UBD operations. It investigates different risk factors associated with MPD and UBD technologies, which could lead to a blowout accident. Blowout accident scenarios are investigated and the BNs are developed for MPD and UBD technologies in order to predict the probability of blowout occurrence. The main objective of this paper is to understand MPD and UBD technologies, to identify hazardous events during MPD and UBD operations, to perform failure analysis (modelling) of blowout events and to evaluate plus compare risk. Importance factor analysis in drilling operations is performed to assess contribution of each root cause to the potential accident; the results show that UBD has a higher occurrence probability of kick and blowout compared to MPD technology. The Rotating Control Devices (RCD) failure in MPD technology and increase in flow-through annulus in UBD technology are the most critical situations for kick and blowout.

The possibility of using membrane bioreactors (MBRs) in simultaneous nitrification-anammox-denitrification (SNAD) by considering periodic aeration cycles was investigated. Two separate reactors were operated to investigate the effect of different anammox biomass in the presence of nitrifying and denitrifying biomass on the final nitrogen removal efficiency. The results illustrated that the reactor with higher anammox biomass was more robust to oxygen cycling. Around 98% Total Nitrogen (TN) and 83% Total Organic Carbon (TOC) removal efficiencies were observed by applying one hour aeration over a four-hour cycle. Decreasing the aeration time to 30, 15, and 2 min during a four-hour cycle affected the final TN removal efficiencies. However, the effect of decreasing aeration on the TN removal efficiencies in the reactor with higher anammox biomass was much lower compared to the regular reactor. The nitrous oxide (N2O) emission was a function of aeration as well, and was lower in the reactor with higher anammox biomass. The results of q-PCR analysis confirmed the simultaneous co-existence of nitrifiers, anammox, and denitrifiers in both of the reactors. To simulate the TN removal in these reactors as a function of the aeration time, a new model, based on first order reaction kinetics for both denitrification and anammox was developed and yielded a good agreement with the experimental observations.

Abstract Life cycle assessment (LCA) has been an emerging feature of modern structural health monitoring techniques that aims at evaluating equipment degradation process and it can be used for development of dynamic maintenance plans. A novel framework based on LCA is proposed in this paper for dynamic maintenance planning of hydro-turbines. Using a Hidden Markov Model (HMM) and the inspection data of hydro-turbine runner cracks from actual operations, the transition probability matrix and sojourn time of different crack states are obtained. This is utilized for predicting the expected remaining useful life (RUL) and the formulation of maintenance intervals. Moreover, maintenance plans are developed from the influence of cracks in different states leading to critical conditions. The proposed method, as demonstrated in the case study, efficiently updates the maintenance intervals and reduces the likelihood of failure events. The results of this paper provide a valid maintenance framework for achieving higher operational safety of hydropower stations with minimal resource losses.