The global shift toward sustainable energy solutions emphasises the urgent need to harness renewable sources for green hydrogen production, presenting a critical opportunity in the transition to a low-carbon economy. Despite its potential, integrating renewable energy with electrolysis to produce green hydrogen faces significant technological and economic challenges, particularly in achieving high efficiency and cost-effectiveness at scale. This review systematically examines the latest advancements in electrolysis technologies—alkaline, proton exchange membrane electrolysis cell (PEMEC), and solid oxide—and explores innovative grid integration and energy storage solutions that enhance the viability of green hydrogen. The study reveals enhanced performance metrics in electrolysis processes and identifies critical factors that influence the operational efficiency and sustainability of green hydrogen production. Key findings demonstrate the potential for substantial reductions in the cost and energy requirements of hydrogen production by optimising electrolyser design and operation. The insights from this research provide a foundational strategy for scaling up green hydrogen as a sustainable energy carrier, contributing to global efforts to reduce greenhouse gas emissions and advance toward carbon neutrality. The integration of these technologies could revolutionise energy systems worldwide, aligning with policy frameworks and market dynamics to foster broader adoption of green hydrogen.

The innumerable impact of climate change is a global menace to human health. This paper conveys a comprehensive review of scientific literature to explore the relationship between climate change, air pollutants, and human health. The integral relationship between climate change and health is complex and has a significant impact on every facet of human life. The impact can either be direct (e.g., exposures due to extreme heat, storms, flooding, and air pollution) or indirect (e.g., displacement, food security, and variation in water). The rising temperature of the planet could lead to increasingly severe health impacts from climate change in the future. It is important to take stringent climate actions to mitigate the climate change risk and adapt to the impacts that are already happening. To lessen the speed and severity of climate change, mitigation focuses on cutting greenhouse gas emissions. Options for adaptation include things like advancing to higher ground to stop sea levels from increasing, growing new crops that can grow in a new environment, or using novel construction methods. Investing in novel or enhanced technology, infrastructure, and research is frequently required for adaptation. The review emphasized the importance of considering both short-term and long-term adaptation strategies as well as mitigation efforts, which call for steps to address the root cause by halting or reducing the growth in fossil fuel emissions that might severely and completely increase the earth's scorching temperatures. The results of this study provide insightful viewpoints on adaptation measures, and mitigation strategies for decision-makers, experts in public health, and researchers working in the field of climate change and its effects on human health.

Climate change presents profound challenges, demanding effective policies to mitigate its impacts. This paper reviews the effectiveness of climate policies in the United States, analyzing scholarly articles, government reports, and relevant literature. The review covers diverse policies such as greenhouse gas regulations, renewable energy initiatives, energy efficiency programs, and adaptation measures. It assesses their success in reducing carbon emissions, advancing a low-carbon economy, and enhancing resilience to climate impacts. Key findings highlight significant successes, such as the rise in renewable energy due to renewable portfolio standards and reduced emissions from vehicle fuel efficiency standards. State-level initiatives, like carbon pricing and renewable energy targets, also show promising outcomes. However, the review identifies substantial challenges, including political and economic obstacles, inconsistent policies across states, and the need for better federal-state-local collaboration. Additionally, disparities in policy benefits, with marginalized communities facing greater burdens and limited access to renewable resources, are significant concerns. To enhance the effectiveness of climate policies, the study recommends fostering coherent policies across all government levels, promoting equitable and inclusive climate solutions, raising public awareness, and supporting innovation in clean technologies. This comprehensive review provides valuable insights for policymakers and stakeholders to develop strategies that effectively address climate change, promote sustainability, and ensure a fair transition to a low-carbon future.

The integration of wind and solar energy with green hydrogen technologies represents an innovative approach toward achieving sustainable energy solutions. This review examines state-of-the-art strategies for synthesizing renewable energy sources, aimed at improving the efficiency of hydrogen (H2) generation, storage, and utilization. The complementary characteristics of solar and wind energy, where solar power typically peaks during daylight hours while wind energy becomes more accessible at night or during overcast conditions, facilitate more reliable and stable hydrogen production. Quantitatively, hybrid systems can realize a reduction in the levelized cost of hydrogen (LCOH) ranging from EUR 3.5 to EUR 8.9 per kilogram, thereby maximizing the use of renewable resources but also minimizing the overall H2 production and infrastructure costs. Furthermore, advancements such as enhanced electrolysis technologies, with overall efficiencies rising from 6% in 2008 to over 20% in the near future, illustrate significant progress in this domain. The review also addresses operational challenges, including intermittency and scalability, and introduces system topologies that enhance both efficiency and performance. However, it is essential to consider these challenges carefully, because they can significantly impact the overall effectiveness of hydrogen production systems. By providing a comprehensive assessment of these hybrid systems (which are gaining traction), this study highlights their potential to address the increasing global energy demands. However, it also aims to support the transition toward a carbon-neutral future. This potential is significant, because it aligns with both environmental goals and energy requirements. Although challenges remain, the promise of these systems is evident.

Two-phase flow regime identification is an essential transdisciplinary topic that spans digital signal processing, artificial intelligence, chemical engineering, and energy. Multiphase flow systems significantly impact pipeline safety, heat transfer, and pressure drop; therefore, precisely identifying the governing flow regime is crucial for effective modeling and design. However, it is challenging due to the geometrical complexity of flow regimes in multiphase flow. With the advances in sensor measurement and machine learning, applying non-destructive tests and self-supervised learning to practical industrial problems has become technically feasible and costeffective. This study applies a weak-supervised learning-based twophase flow regime identification solution using a nondestructive tests ultrasonic sensor in an S-shape riser experimental bed by proposing a self-supervised feature extraction algorithm. The proposed selfsupervised feature extraction algorithm reduces time/labor consumption and human error in data annotation using SSL, which provides full supervision without manual annotation. The self-supervised feature extraction algorithm uses a bottlenecked neural network and encoder-decoder structure to extract compact features. The self-supervised feature extraction algorithm performance is evaluated using an established convolutional neural network-based classifier. The source data was collected from a 10 x 50 m riser experimental rig. The dataset is made available to the community as part of this study. The performance of the approach is comparable with state-of-the-art methods and is also the first successful attempt to apply selfsupervised learning to multiphase flow regime ultrasonic signal identification. This study achieved 98.84%, 0.000663, 0.00312, and 7.71 x 105 in accuracy, root mean square error, categorical cross-entropy, and model complexity, respectively. The practical experiment justifies the robustness, fairness, and practicability in the practical application environment. The proposed self-supervised feature extraction brings new approaches and inspirations for the feature extraction step in identifying a two-phase flow regime, and it will be beneficial to generalize this study in different riser shapes in the future.

This study emphasises the growing relevance of hydrogen as a green energy source in meeting the growing need for sustainable energy solutions. It foregrounds the importance of assessing the environmental consequences of hydrogen-generating processes for their long-term viability. The article compares several hydrogen production processes in terms of scalability, cost-effectiveness, and technical improvements. It also investigates the environmental effects of each approach, considering crucial elements such as greenhouse gas emissions, water use, land needs, and waste creation. Different industrial techniques have distinct environmental consequences. While steam methane reforming is cost-effective and has a high production capacity, it is coupled with large carbon emissions. Electrolysis, a technology that uses renewable resources, is appealing but requires a lot of energy. Thermochemical and biomass gasification processes show promise for long-term hydrogen generation, but further technological advancement is required. The research investigates techniques for improving the environmental friendliness of hydrogen generation through the use of renewable energy sources. Its ultimate purpose is to offer readers a thorough awareness of the environmental effects of various hydrogen generation strategies, allowing them to make educated judgements about ecologically friendly ways. It can ease the transition to a cleaner hydrogen-powered economy by considering both technological feasibility and environmental issues, enabling a more ecologically conscious and climate-friendly energy landscape.

Hydrogen (H2) usage was 90 tnes (Mt) in 2020, almost entirely for industrial and refining uses and generated almost completely from fossil fuels, leading to nearly 900 Mt of carbon dioxide emissions. However, there has been significant growth of H2 in recent years. Electrolysers' total capacity, which are required to generate H2 from electricity, has multiplied in the past years, reaching more than 300 MW through 2021. Approximately 350 projects reportedly under construction could push total capacity to 54 GW by the year 2030. Some other 40 projects totalling output of more than 35 GW are in the planning phase. If each of these projects is completed, global H2 production from electrolysers could exceed 8 Mt by 2030. It's an opportunity to take advantage of H2S prospects to be a crucial component of a clean, safe, and cost-effective sustainable future. This paper assesses the situation regarding H2 at the moment and provides recommendations for its potential future advancement. The study reveals that clean H2 is experiencing significant, unparalleled commercial and political force, with the amount of laws and projects all over the globe growing quickly. The paper concludes that in order to make H2 more widely employed, it is crucial to significantly increase innovations and reduce costs. The practical and implementable suggestions provided to industries and governments will allow them to fully capitalise on this growing momentum.

The problem of classifying gas-liquid two-phase flow regimes from ultrasonic signals is considered. A new method, belt-shaped features (BSFs), is proposed for performing feature extraction on the preprocessed data. A convolutional neural network (CNN/ConvNet)-based classifier is then applied to categorize into one of the four flow regimes: 1) annular; 2) churn; 3) slug; or 4) bubbly. The proposed ConvNet classifier includes multiple stages of convolution and pooling layers, which both decrease the dimension and learn the classification features. Using experimental data collected from an industrial-scale multiphase flow facility, the proposed ConvNet classifier achieved 97.40%, 94.57%, and 94.94% accuracy, respectively, for the training set, testing set, and validation set. These results demonstrate the applicability of the BSF features and the ConvNet classifier for flow regime classification in industrial applications.

Background: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. Methods: A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. Results: 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. Conclusions: Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. Contributions: This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers.

Water droplet size variation has been established in the literature as an important variable that influences the behavior and characteristics of water in fuel emulsion. However, with the growing demand for sustainable aviation fuels (SAF), no data is available that shows how these fuels will affect the size of dispersed water droplets and their frequency distribution. To address this lack of knowledge, this study explores and presents experimental results on the characterization of dispersed water droplets in alternative fuels and Jet A-1 fuel under dynamic conditions. The alternative fuels comprised of two fully synthetic fuels, two fuels synthesized from bio-derived materials, and one bio-derived fuel. The data and statistics presented reveal that water droplet frequency and size distribution are sensitive to changes in fuel composition. Observations showed an evident transition of the droplet percentile over time in the cumulative frequency distribution; this could be attributed to droplet coalescence to form larger droplets. Mean droplet diameters between 3 and 6 mu m were observed for all the fuels tested. With further analysis based on recommendations proposed in this work, the data may assist in providing insight to filter manufacturers.

A method for classifying flow regimes is proposed that employs a neural network with inputs of extracted features from Doppler ultrasonic signals of flows using either the Discrete Wavelet Transform (DWT) or the Power Spectral Density (PSD). The flow regimes are classified into four types: annular, churn, slug, and bubbly flow regimes. The neural network used in this work is a feedforward network with 20 hidden neurons. The network comprises four output neurons, each of which corresponds to the target vector's element number. 13 and 40 inputs are used for features extracted from PSD and DWT respectively. Experimental data were collected from an industrial-scale multiphase flow facility. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets in the classification and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thus showing the superiority of the DWT in feature extraction of flow regime classification. The approach demonstrates the applicability of a neural network and DWT for flow regime classification in industrial applications using a clamp-on Doppler ultrasonic sensor. The scheme has significant advantages over other techniques as only a non-radioactive and non-intrusive sensor is used. To the best of our knowledge, this is the first known successful attempt for the classification of liquid-gas flow regimes in an S-shape riser system using an ultrasonic sensor, PSD-DWTs features, and a neural network.

Slugging flow is a condition caused by a liquid obstruction at the riser base. It exhibits cyclic behaviour. The cycle consists of a protracted time of no gas production at the riser's top, followed by the arrival of a liquid slug with a length greater than the riser height, and ultimately the breakthrough of a significant gas surge. The cycle time might range from a few minutes to a few hours, depending on the system size and flow conditions. In offshore oil production, feedback control is a practical and cost-effective way to prevent slug flow. To control the flow rate or the pressure in the pipeline, adjusting the choke valve opening on the topside facility is generally utilised as the control input. From a practical standpoint, designing a control system based on topside data rather than seabed measurements is preferable. Controlling the topside pressure alone is difficult and ineffective in reality, but combining it with the flow rate results in a more reliable control solution. Measuring the flow rate of a multiphase flow, on the other hand, is difficult and expensive. All the topside measurements-based slug control techniques was critically reviewed and necessary recommendations for enhanced control performance provided. In conclusion, this review acknowledged that slugging is a well-defined flow pattern, yet despite having been studied for several decades, current slug control methods still have robustness issues. Slug flow problems are expected to become even more intense in the future as a result of longer vertical risers driven by deep-water Exploration and Production (E&P).

Slugging flow poses significant challenges to the offshore multiphase flowline and riser systems. Slug flow is characterized by an uneven flow regime whereby pipeline pressures, temperatures, or flow volume rates fluctuate. One of the most common causes of severe slugging is low pressures which causes buildup of fluid over time, consequentially causes flow and pressure oscillations. This mostly occurs in vertical risers or wells. The negative effects of severe slugging have prompted numerous studies, investments, and efforts to reduce or eliminate the slugging flow. Several active slugging control techniques have been investigated in the oil and gas industries for decades. However, many of these techniques still run the risk of limiting hydrocarbon production due to inappropriate over choking. Other challenges for active slug control include the fact that some systems rely mainly on subsea measurements such as riser base pressure, and most of these subsea measurements are costly, difficult to maintain, not always available, and can be unreliable. As a result, to achieve an efficient slugging control performance, reliable, robust, and efficient measurements that are more sensitive to slugging flow for control are required, which is the motivation for this work. The control of riser slug flow using non-radioactive, non-invasive, and non-intrusive Continuous-wave Doppler ultrasound has been investigated in this work, and provides good control performance. It achieved a larger valve opening than an open-loop unstable system. This outperforms manual choking, which maintains stability at a much lower valve opening.

The current handling of gas associated with oil production poses an environmental risk. This gas is being flared off due to the technical and economic attractiveness of this option. As flared gases are mainly composed of methane, they have harmful greenhouse effects when released into the atmosphere. This work discusses the effectiveness of using this gas for enhanced oil recovery (EOR) purposes as an alternative to flaring. In this study, a micromodel was designed with properties similar to a sandstone rock with a porosity of 0.4, and computational fluid dynamics (CFD) techniques were applied to design an EOR system. Temperature effects were not considered in the study, and the simulation was run at atmospheric pressure. Five case studies were carried out with different interfacial tensions between the oil and gas (0.005 N/m, 0.017 N/m, and 0.034 N/m) and different injection rates for the gas (1 × 10−3 m/s, 1 × 10−4 m/s, and 1 × 10−6 m/s). The model was compared with a laboratory experiment measuring immiscible gas flooding. Factors affecting oil recoveries, such as the interfacial tension between oil and gas, the viscosity, and the pressure, were studied in detail. The results showed that the surface tension between the oil and gas interphase was a limiting factor for maximum oil recovery. The lower surface tension recovered 33% of the original oil in place. The capillary pressure was higher than the pressure in the micromodel, which lowered the amount of oil that was displaced. The study showed the importance of pressure maintenance to increase oil recovery for immiscible gas floods. It is recommended that a wider set of interfacial tensions between oil and gas be tested to obtain a range at which oil recovery is maximum for EOR with flared gas.

Energy supplies that are safe, environmentally friendly, dependable, and cost-effective are important for society's long-term growth and improved living standards, though political, social, and economic barriers may inhibit their availability. Constantly increasing energy demand is induced by substantial population growth and economic development, putting an increasing strain on fossil fuel management and sustainability, which account for a major portion of this rising energy demand and, moreover, creates difficulties because of greenhouse gas emissions growth and the depletion of resources. Such impediments necessitate a global shift away from traditional energy sources and toward renewables. Aside from its traditional role, H2 is viewed as a promising energy vector and is gaining international attention as a promising fuel path, as it provides numerous benefits in use case scenarios and, unlike other synthesized carbon-based fuels, could be carbon-free or perhaps even negative on a life-cycle criterion. Hydrogen (H2) is one of the most significant chemical substances on earth and can be obtained as molecular dihydrogen through various techniques from both non-renewable and renewable sources. The drive of this paper is to deliver a technological overview of hydrogen production methods. The major challenges, development and research priorities, and potential prospects for H2 production was discussed.

The Paris Climate Agreement seeks to keep global temperature increases under 2° Celsius, ideally 1.5° Celsius. This goal necessitates significant emission reductions. By 2030, emissions are expected to range between 52 and 58 GtCO2e from their 2016 level of approximately 52 GtCO2e. This review paper explores a number of low and zero-carbon renewable fuels, such as hydrogen, green ammonia, green methanol, biomethane, natural gas, and synthetic methane (with natural gas and synthetic methane subject to CCUS both at processing and at final use) as alternative solutions for providing a way to rebalance transition paths in order to achieve the goals of the Paris Agreement while also reaping the benefits of other sustainability targets. The results show renewables will need to account for approximately 90% of total electricity generation by 2050 and approximately 25% of non-electric energy usage in buildings and industry. However, low and zero-carbon renewable fuels currently only contributes about 15% to the global energy shares, and it will take about 10% more capacity to reach the 2050 goal. The transportation industry will need to take important steps toward energy efficiency and fuel switching in order to achieve the 20% emission reduction. Therefore, significant new commitments to efficient low-carbon alternatives will be necessary to make this enormous change. According to this paper, investing in energy efficiency and low-carbon alternative energy must rise by a factor of about five by 2050 in comparison to 2015 levels if the 1.5 °C target is to be realised.

The problem of gas-liquid (two-phase) flow regime identification in an S-shaped riser using an ultrasonic sensor and convolutional recurrent neural networks (CRNN) is addressed. This research systematically evaluates three different schemes with four CRNN-based classifiers over fourteen experiments. Four metrics are used as the evaluation criteria: categorical accuracy, categorical cross-entropy, mean square error (MSE), and computation graph complexity. Compared with existing results, a compatible performance is achieved while considerably reducing the model complexity. The testing and validation accuracies were 98.13% and 98.06%, while the complexity decreased by 98.4% (only 117,702 parameters). The proposed approach is i) accurate, low complexity, and non-intrusive and hence suitable for industry, and ii) could provide a benchmark for flow regime identification.

Machine learning approaches are now being used in the process industries to objectively classify two-phase flow regimes in pipes such as into bubbly, slugging, churning, or annular flow. However, they lack explainability, which makes the results difficult to be trusted by plant operators in the field. This paper presents a complete machine learning workflow for flow regime identification with improved explainability through the use of virtual flow regime maps and feature relevance determination. The workflow was applied on an S-shape riser fitted with a non-intrusive ultrasonic Doppler sensor. Statistical features were first extracted via discrete wavelet transform (DWT). An improved virtual flow regime map was then constructed by projecting the DWT features onto 2-D space using kernel principal components analysis (KPCA). Within KPCA, anisotropic kernels are proposed in this study (referred to as Aniso-KPCA) to reveal which input DWT features have the best flow-distinguishing power. Kernel parameters in Aniso-KPCA were searched by maximizing the goodness of 2-D clustering of data points using the genetic algorithm. Finally, the support vector machine (SVM) emerged as the recommended classifier of flow regimes on the basis of repeatability and accuracy, as compared to other models such as neural networks, naive Bayes, k-nearest neighbors, and ensembled trees. Overall, the proposed workflow is a step towards improving the explainability of future machine learning based flow regime classifiers.

A suitable initial point for understanding multiphase flows is a phenomenological description of the mechanism of geometric distributions or flow patterns that are observed. The challenge however is the prediction of the flow patterns for a combination of flow operating conditions and the characteristics of the phases as well as points of transition from one pattern to the other. Different flow patterns occur in different pipeline configurations for which U-shape risers are part. In the quest to stabilise unstable slug flow in the U-shape riser, an experimental study of gas-liquid flow mixture is conducted to understand the behaviour of the flow in the riser.This paper seeks to understand the flow dynamics in a 2-inch internal diameter U-shape pipeline riser system with much emphasis on unstable slug flow. The initiation of this flow instabilities in the U-shape pipeline riser system and the impact of the downcomer on the flow behaviour is investigated experimentally. Understanding the flow behaviour in the U-shape riser could help in developing effective control techniques to stabilise the multiphase flows in the flowlines. Experimentally, flow patterns observed from the U-shape pipeline riser configuration is used to develop a flow regime map which was then compared to that observed in literature and similarly to a purely vertical riser with similar pipe diameter. Thus, a slug envelope was developed for the U-shape riser to help identify which regions slugging could occur in the system.

This paper addresses the issues of slug detection and characterization in air-water two-phase flow in a vertical pipeline. A novel non-invasive measurement technique using continuous-wave Doppler ultrasound (CWDU) and bandpass power spectral density (BPSD) is proposed for multiphase flow applications and compared with the more established gamma-ray densitometry measurement. In this work, analysis using time-frequency analysis of the CWDU is performed to infer the applicability of the BPSD method for observing the slug front and trailing bubbles in a multiphase flow. The CWDU used a piezo transmitter/receiver pair with an ultrasonic frequency of 500 kHz. Signal processing on the demodulated signal of Doppler frequency was done using the Butterworth bandpass filter on the power spectral density which reveals slugs from background bubbles. The experiments were carried out in the 2" vertical pipeline-riser at the process system engineering laboratory at Cranfield University. The 2-inch test facility used in this experiment is made up of a 54.8 mm internal diameter and 10.5 m high vertical riser connected to a 40 m long horizontal pipeline. Taylor bubbles were generated using a quick-closing air valve placed at the bottom of the riser underwater flow, with rates of 0.5 litres/s, 2 litres/s, and 4 litres/s. The CWDU spectrum of the measured signal along with the BPSD method is shown to describe the distinctive nature of the slugs.