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Methanol and carbon monoxide co-production via methane decomposition: techno-economic and environmental analysis
No full textThis study presents the development and optimisation of a co-production process for methanol and carbon monoxide via methane decomposition using Aspen Plus. The process model was designed to evaluate the system's energy efficiency, economic viability, and environmental impact. The overall energy efficiency of the process was calculated to be 89.4%, demonstrating its high performance in energy utilisation. The levelised cost of methanol production was determined to be 17.5 € per GJ, indicating competitive economic feasibility. Furthermore, a total life cycle CO2 emission of 0.5 kgCO2 kgproduct−1 was achieved, highlighting the process's potential for reduced environmental impact compared to conventional methods. The results suggest that methane decomposition for the co-production of methanol and carbon monoxide offers a promising pathway for sustainable chemical production, combining high energy efficiency with low carbon emissions.Sustainable Energy & Fuel
Comprehensive review of agriculture spraying UAVs challenges and advances: modelling and control
No full textThe integration of unmanned aerial vehicles (UAVs) into agriculture has emerged as a transformative approach to enhance resource efficiency and enable precision farming. UAVs are used for various agricultural tasks, including monitoring, mapping and spraying of pesticides, providing detailed data that support targeted and sustainable practices. However, effective deployment of UAVs in these applications faces complex control challenges. This paper presents a comprehensive review of UAVs in agricultural applications, highlighting the sophisticated control strategies required to address these challenges. Key obstacles, such as modelling inaccuracies, unstable centre of gravity (COG) due to shifting payloads, fluid sloshing within pesticide tanks and external disturbances like wind, are identified and analysed. The review delves into advanced control methodologies, with particular focus on adaptive algorithms, backstepping control and machine learning-enhanced systems, which collectively enhance UAV stability and responsiveness in dynamic agricultural environments. Through an in-depth examination of flight dynamics, stability control and payload adaptability, this paper highlights how UAVs can achieve precise and reliable operation despite environmental and operational complexities. The insights drawn from this review underscore the importance of integrating adaptive control frameworks and real-time sensor data processing, enabling UAVs to autonomously adjust to changing conditions and ensuring optimal performance in agriculture. Future research directions are proposed, advocating for the development of control systems that enhance UAV resilience, accuracy and sustainability. By addressing these control challenges, UAVs have the potential to significantly advance precision agriculture, offering practical and environmental benefits crucial to sustaining global food production demands.This research was funded by the Turkiye Republic Ministry of Education.The Aeronautical Journa
Vortex core detection in turbulent simulations based on machine learning approaches
Get PDFThe identification of vortex cores in fluid mechanics is a challenging task that re-
quires sophisticated techniques. Commonly employed local detection methods, such
as the Q, delta, or swirling-strength criterion, rely on the local velocity gradient
tensor to locate the vortex cores. Despite their reasonable accuracy, these methods
tend to produce false positives and negatives, necessitating user-defined tuning pa-
rameters to maintain an acceptable error level. Moreover, this method presupposes
prior knowledge of the vortices, limiting its robustness and self-contained nature.
To overcome this shortcoming, a hybrid computer vision and machine learning ap-
proach is proposed to enhance the detection features of vortex cores and reduce
false positives and negatives. Initially, computer vision was employed to identify
the areas of vortex structures, followed by machine learning to identify the vortex
core within the vortex region. A convolutional neural network (CNN) was trained to
analyse streamline plots based on line integral convolution (LIC) for the computer
vision process. Through the use of computer vision, false positives and negatives
in flow-specific problems are reduced without the need for calibrating user-defined
parameters. Furthermore, the trained CNN was successfully applied to three test
cases, indicating its universal applicability. As such, computer vision offers a reli-
able convolutional neural network approach to detect vortex areas, which requires
training once and is suitable for a broad range of flow scenarios. To identify the
exact location of vortex cores, machine learning was combined with the computer
vision approach. Various sets of input features were tested for both hybrid and pure
machine learning approaches, starting with primitive variables such as velocity and
pressure and expanding to more derived quantities such as velocity gradients, pres-
sure gradients, Q-criterion, vorticity, and magnitude of vector quantities. In addi-
tion, a method for automatically labelling the dataset using K-means clustering was
proposed to preprocess input images for machine learning. Results demonstrated
that the K-means clustering-based labelling approach had a mean square error of
only 0.45%, comparable to the manual labelling approach. The hybrid approach
significantly reduced training time for all tested cases and led to fewer false posi-
tives and negatives when using primitive variables and their derivatives compared
to pure machine learning using the Artificial Neural Network approach applied to
the entire flow. At the same time, using the variable set with all possible inputs
does not provide a more accurate prediction of vortex cores and thus the hybrid
approach is demonstrated as an effective way to reduce false positives and negatives
entirely using just the primitive variables and their derivatives.PhD in Aerospac
Performance evaluation approach for design space explorations of propulsive fuselage aircraft concepts
Get PDFA promising architecture to enhance the performance of next-generation commercial aircraft involves embedding the propulsion system within the airframe, thereby capturing energy from the fuselage through boundary layer ingestion. However, in cases of strong aerodynamic coupling, traditional accounting methods break down, necessitating alternative approaches. The lack of consensus surrounding the interpretation and quantification of these benefits underscores the need for a unified assessment method. In this work, commonly used near-field momentum-based bookkeeping schemes are discussed and unified with a more holistic energy-based approach to evaluate aero-propulsive performance and facilitate more intuitive physical interpretations of the aerodynamics. The contribution of this work lies in the correction of the power balance, leading to the development of new metrics for assessing the efficiencies of both the aircraft and the boundary layer ingestion propulsion system. Notably, a surrogate for propulsive efficiency and limits to the power saving coefficient are given, which address inconsistencies present in the literature. Despite their application to an axi-symmetric propulsive fuselage, the metrics introduced are applicable to higher levels of representativity of propulsive fuselage concepts. The potential impact of this work is the transformation of established evaluation practices by employing these newly introduced metrics to assess aircraft and system efficiencies.This work was conducted within the SUBLIME (Supporting Understanding of Boundary-Layer Ingestion Model Experiment) project as part of the Clean Sky 2 Joint Undertaking which has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 864803.CEAS Aeronautical Journa
Seakeeping analysis of catamaran and barge floats for floating solar arrays: a CFD study with experimental validation
Get PDFWhilst floating photovoltaic (FPV) is gaining attention for ocean-based applications, their motion response in waves significantly affects structural integrity and power generation efficiency. In particular, FPV is expected to operate in arrays consisting of extensive solar panels, and thus, floating solar systems are required to be analysed with neighbouring devices connected by joints. This study investigates the seakeeping characteristics of two FPV systems in arrays, comparing conventional barge floats with twin-hull (catamaran) floats under various wave conditions. A systematic investigation using Computational Fluid Dynamics (CFD) was conducted for the hydrodynamic response of both isoslated-single-floater and multi-body (1 × 3) configurations in regular waves, with non-dimentional wavelength ratio (λ/L) 1.62-4.27 to the floater length. Wave tank experiments were conducted to validate the CFD model, showing agreement with less than 10% discrepancies. The study focused on the multi-body behavior of heave and pitch Response Amplitude Operators (RAOs) and mooring line forces. Results show that the multi-catamaran configuration exhibited lower heave RAOs (by approximately 20°%) compared to multi-barge pontoons in long waves (λ/L > 2.47) while maintaining similar pitch responses. However, in shorter waves (λ/L < 2), the catamaran configuration showed up to 15% higher RAOs than barge's. The multi-body arrangement demonstrated significant array effects, with the leading float experiencing 30% higher mooring loads than the trailing float. The leading float also experiences the highest mooring forces. As the wavelength ratio increases, the barge float's front mooring force increases dramatically, reaching nearly twice that of the catamaran at a ratio of 4.27. These findings align with the RAO results, indicating that the barge float is more wave-sensitive under long-wavelength conditions, whereas the catamaran demonstrates superior station-keeping with lower mooring forces. This work provides quantitative guidance for selecting appropriate floater forms for FPV applications based on expected wave conditions.L.H. acknowledges grants received from Innovate UK (No. 10048187, 10079774, 10081314), the Royal Society (IEC∖NSFC∖223253, RG∖R2∖232462) and UK Department for Transport (TRIG2023 – No. 30066).Ocean Engineerin
Examining the burial contexts and trauma patterns of fallen soldiers and civilian victims from the Spanish Civil War: a comparative investigation
Get PDFThis paper compares the context of the burials of combatants with the burial circumstances of civilian victims from the Spanish Civil War and dictatorship, along with the biological profile and trauma patterns of the remains and associated artefacts. The burial context of the 41 individuals from two cemetery mass grave sites was compared to remains from the International Brigades that were found outside in Central Spain. Different patterns were apparent regarding material culture associations, such as in the presence of ammunition with the brigadiers who had been left where they died in combat, whilst the civilians had few personal effects. Trauma patterns were also compared among the different sites, and the trauma sustained by the Brigadiers differed from that of the civilians who had been killed. Awareness of differing patterns common to combatants and civilians during the Spanish Civil War can assist in efforts to identify remains from this conflict.Journal of Conflict Archaeolog
What drives Generation Z to choose green apparel? Unraveling the impact of environmental knowledge, altruism and perceived innovativeness
Get PDFThis study proposes to determine the influence of ‘Environmental Knowledge’ (EK), ‘Altruism’ (Atr), ‘Consumer Confidence’ (CC) and constructs of ‘Theory of Planned Behaviour’ (TPB) like Attitude” (Atd), ‘Subjective Norm’ (Sub) and ‘Perceived behavioural control’ (Pbhc) on consumers’ intention to purchase ‘Green Apparel Products’ (GAPI). Moreover, the moderating effect of ‘Perceived Innovativeness’ (PInn) on the relationship between ‘Attitude’ (Atd), ‘Subjective Norm’ (Sub), ‘Perceived behavioural control’ (Pbhc), ‘EK’, ‘Atr’ and ‘CC’ was studied. To test the research model and hypothesis, a survey of 349 Generation Z consumers (18–26 years) was conducted. Cronbach’s alpha and a ‘Confirmatory Factor Analysis’ (CFA) were used to determine the scale’s reliability and validity. ‘Structural Equation Modelling’ (SEM) validated the given model and hypotheses. In this research, six hypotheses were tested, and it was found that three hypotheses showed a direct relationship. Specifically, the result of SEM showed that ‘Atd’, ‘Sub’ and ‘CC’ were positively related to GAPI. Also, six hypotheses were formulated testing the moderating role of ‘PInn’. The results established that ‘PInn’ moderated the relationship between ‘Atd’, ‘Sub’, ‘CC’ and ‘GAPI’ significantly. This research provides a novel framework to explore the relationship between the ‘EK’, ‘Atr’ and ‘CC’ and Generation Z consumer’s ‘GAPI’.International Journal of Sustainable Engineerin
Tipping cascades between conflict and cooperation in climate change
Get PDFNo new data are used in this paper. Data for Fig. 5b are based on the GUARD project and are openly available via the data statement in Aquino et al. (2019). Code for the GUARD project is not accessible publicly as it was a government project and is only available to internal government and stakeholder end users through UK DSTL.Abstract. Following empirical research on the dynamics of conflict and cooperation under climate change, conditions, pathways, and societal responses in the climate–security nexus are analysed. Complex interactions between climate risks and conflict risks are connected to models of tipping points, compounding and cascading risks in the context of multiple crises. System and agent models of conflict and cooperation are considered to analyse dynamic trajectories, equilibria, stability, and chaos, along with adaptive decision rules in multi-agent interaction and related tipping, cascading, networking, and transformation processes. In particular, a bi-stable tipping model is applied to study transitions between conflict and cooperation, depending on internal and external factors and on multi-layered interaction networks of agents, showing how negative forces can reduce resilience to and induce collapse of violent conflict. The case study of Lake Chad is used for illustration to bridge disciplines and demonstrate climate change as a risk multiplier from a modelling perspective. These models relate to realities on the ground, where governance approaches and community behaviour can either lower or raise barriers to climate-induced conflict, exemplified by forced migration and militant forces lowering barriers and chances for cooperation. Adaptive and anticipative governance (AAG) based on integrative research and agency are discussed to prevent and contain climate-induced tipping to violent conflict and induce positive tipping towards cooperative solutions and synergies, e.g. through civil conflict transformation (CCT), environmental peacebuilding, and forward-looking policies for Earth system stability.The authors acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Excellence Strategy EXC 2037 (project number: 390683824) “Climate, Climatic Change, and Society” (CLICCS) contributing to the Center for Earth System Research and Sustainability (CEN) of University of Hamburg (Jürgen Scheffran); the USAFOFSR Networked Social Influence and Acceptance in a New Age of Crises (grant no. FA8655-20-1-7031) and the Alan Turing Institute (D&S) GUARD project (Weisi Guo); strategic research support from the Ministry of Foreign Affairs of Norway and Sweden (Florian Krampe); and a UKRI Future Leaders Fund (grant no.MR/V022318/1) offered to Uche OkparaEarth System Dynamic
Metatranscriptomic insights into aerobic biotransformation of 6:2 fluorotelomer sulfonate by an enrichment culture under sulfur-limiting conditions
Get PDF6:2 Fluorotelomer sulfonate (6:2 FTS), an alternative for PFOS, has become an environmental concern due to its toxicity. This study investigated the aerobic biotransformation of 6:2 FTS under sulfur-limiting conditions using an enrichment culture, SXC01. The enriched culture achieved ≥99.5% degradation of 6:2 FTS at initial concentrations of 0.5, 2.5, and 5 mg/L within 14 days, with notable corresponding defluorination ratios of 77.2%, 28%, and 16.8%, respectively. Eleven transformation products were identified and quantified over time, and the accumulation of intermediate product 6:2 FTUCA suggested that its further degradation may represent a rate-limiting step. Furthermore, the production of PFHxA is more significant than that of PFBA, suggesting the transformation of 6:2 FTUCA via 5:2 sFTOH pathway is more dominant than the other proposed alternative pathway. Metatranscriptomic analysis revealed the upregulation of key genes associated with desulfonation (e.g., ssuEADCB cluster, cysI, sbp, iscS) and defluorination (e.g., ladA, dehH, crcB, dhaA), indicating a synergistic metabolic network driving 6:2 FTS biotransformation. Active genera identified included Brucella, Rhodococcus, and Pseudoclavibacter. Moreover, the predominant Brucella anthropi SX009 was successfully isolated and shown to completely degrade 6:2 FTS within 14 days. This study provides novel insights into the mechanisms of 6:2 FTS biotransformation.This research was funded by the National Natural Science Foundation of China (NSFC)-EU Environmental Biotechnology Joint Program (No. 32061133001), and the National Natural Science Foundation of China (No. 42277029). We acknowledge the cooperation between China and the EU through the EiCLaR project (European Union’s Horizon 2020, No. 965945).Environmental Science & Technolog
S4RoboFormer: scribble-supervised surgical robotic segmentation transformer via augmented consistency training
Get PDFAdvancements in deep learning for surgical instrument segmentation have notably improved the proficiency, safety, and efficacy of minimally invasive robotic surgeries. The effectiveness of deep learning, however, is contingent upon the availability of large datasets for training, which are often associated with substantial annotation costs. Given the dynamic nature of surgical robots, scribble-based labeling emerges as a more viable and cost-effective alternative to traditional pixel-wise dense labeling. This paper introduces the Scribble-Supervised Surgical Robotic Segmentation Transformer (S4RoboFormer), designed to mitigate the challenges posed by resource-intensive annotations. S4RoboFormer incorporates a Vision Transformer (ViT)-based U-shaped segmentation network, enhanced with a specialized Weakly-Supervised Learning (WSL) strategy that comprises consistency training through (i) data-based perturbation using a data-mixed interpolation technique, and (ii) network-based perturbation via a self-ensembling strategy. This methodology promotes uniform predictions across different levels of perturbation under conditions of limited-signal supervision. S4RoboFormer outperforms existing state-of-the-art baseline WSL frameworks with both convolutional neural network(CNN)-and ViT-based segmentation networks on a pre-processed public dataset. The code of S4RoboFormer, all baseline methods, pre-processed data, and scribble simulation algorithm are all made publicly available at https://github.com/ziyangwang007/CV-WSL-Robot.IEEE Transactions on Medical Robotics and Bionic