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Optimisation of the circular economy based on the resource circulation equation
Get PDFThe lack of effective evaluation methods and implementation guidelines has led to frequent obstacles in the process of circular economy in enterprises. The efficiency equation for resource circulation can effectively evaluate the efficiency of an enterprise’s circular economy resource circulation from three perspectives: input, circulation, and output. Additionally, it delves into each link to identify weak points, offering guidance for optimising the enterprise’s circular economy. Utilising a value flow analysis within the context of a circular economy, this paper evaluates circular economy efficiency using a resource circulation efficiency equation. It conducts factor analysis across three dimensions: resource input, resource circulation, and waste output. This analysis aims to evaluate the corresponding resource productivity, added value output rate, and environmental efficiency. Factor decomposition techniques were then employed to identify the underlying factors contributing to poor circular economy outcomes. Furthermore, based on the relationships among three resource circulation indicators, this paper forecasts the potential advantages of integrating circular economy improvement measures and proposes practical optimisation approaches. The enhanced resource circulation efficiency resulting from the proposed optimisation approaches was validated through a case study with an aluminium company.This work was supported by the National Social Science Foundation of China (No.:21BGL184).Sustainabilit
Agricultural intensification reduces selection of putative plant growth-promoting rhizobacteria in wheat
Get PDFThe complex evolutionary history of wheat has shaped its associated root microbial community. However, consideration of impacts from agricultural intensification has been limited. This study investigated how endogenous (genome polyploidization) and exogenous (introduction of chemical fertilizers) factors have shaped beneficial rhizobacterial selection. We combined culture-independent and -dependent methods to analyze rhizobacterial community composition and its associated functions at the root–soil interface from a range of ancestral and modern wheat genotypes, grown with and without the addition of chemical fertilizer. In controlled pot experiments, fertilization and soil compartment (rhizosphere, rhizoplane) were the dominant factors shaping rhizobacterial community composition, whereas the expansion of the wheat genome from diploid to allopolyploid caused the next greatest variation. Rhizoplane-derived culturable bacterial collections tested for plant growth-promoting (PGP) traits revealed that fertilization reduced the abundance of putative plant growth-promoting rhizobacteria in allopolyploid wheats but not in wild wheat progenitors. Taxonomic classification of these isolates showed that these differences were largely driven by reduced selection of beneficial root bacteria representative of the Bacteroidota phylum in allopolyploid wheats. Furthermore, the complexity of supported beneficial bacterial populations in hexaploid wheats was greatly reduced in comparison to diploid wild wheats. We therefore propose that the selection of root-associated bacterial genera with PGP functions may be impaired by crop domestication in a fertilizer-dependent manner, a potentially crucial finding to direct future plant breeding programs to improve crop production systems in a changing environment.Biotechnology and Biological Sciences Research Council (BBSRC)Rothamsted Research acknowledges strategic funding from the Biotechnology and Biological Sciences Research Council of the United Kingdom (BBSRC).This work was supported by the bilateral BBSRC-Embrapa grant on “Exploitation of the rhizosphere microbiome for sustainable wheat production” (BB/N016246/1); “Optimization of nutrients in soil-plant systems: How can we control nitrogen cycling in soil?” (BBS/E/C/00005196);.Institute Strategic Programmes “S2N – Soil to nutrition – Work package 1 – Optimizing nutrient flows and pools in the soil-plant-biota system” (BBS/E/C/000I0310) and “Growing Health Institute Strategic Programme [BB/X010953/1]; Work package 2: bio-inspired solutions for healthier agroecosystems: Understanding soil environments”(BBS/E/RH/230003B).The ISME Journa
Understanding and predicting flow behaviour of water and air in serpentine pipes using machine learning and CFD modelling
Get PDFLao, Liyun - Associate SupervisorThe flow behaviour of two-phase fluid (water and air) in serpentine pipes is
complex and poorly understood. This study aims to address this research
problem by using machine learning concepts to gain a deeper understanding of
the flow behaviour and optimize the geometry of serpentine pipes. Experimental
data from the Cranfield process engineering laboratory was used in this work, for
a fixed pipe diameter and varying water and gas velocities. Regression models
were developed and trained. The study aimed to accurately predict the pressure
drop, void fraction and liquid film thickness in serpentine pipes in a timely manner
with high accuracy.
In addition, Computational Fluid Dynamics (CFD) models were developed to
predict the flow behaviour with two different geometries, and machine learning
was applied to determine the best model for capturing the intermediate geometry
flow behaviour. Results provide valuable insights into the behaviour of two-phase
fluid in serpentine pipes. The use of machine learning in this research contributes
to the field by offering a new approach for optimizing the geometry of serpentine
pipes with improved accuracy and efficiency.
The findings demonstrate the potential for machine learning to play a role in
improving our understanding of two-phase fluid flow in serpentine pipes. This
research is expected to have potential future applications in various sectors,
including automotive, electronics cooling systems, and industrial and chemical
processing systems.MSc by Research in Energy and Powe
Implementation and demonstration of autonomous ultrasonic track inspection using cloud-based AI rail flaw analyzer
Get PDFThis research successfully demonstrated autonomous rail inspection feasibility up to Technology Readiness Level (TRL) 7. A
prototype integrating an autonomous rail vehicle and Sperry's Ultrasound Testing (UT) system was developed at Cranfield
University. It was first tested at Cranfield’s Railways Innovation Test Area (RITA) at TRL 5 and tested at heritage operational
railway, in Idridgehay, Derbyshire, UK achieving TRL 7. Experimental works included a 15-meter track test at RITA and nine
rounds demonstration of a 250-meter track inspection at Idridgehay, showcasing inspection, localization, navigation accuracy,
and defect location precision. The prototype successfully detected artificial rail defect during the demonstration and promptly
communicated to command centre via email. We characterised the vehicle performance by measuring the positional error and
detection rate. The positional accuracy measurements, verified through GPS and odometry, revealed an odometry-based error
of 0.27-3.2 metres and an 8-metre GPS-associated error. The absence of differential GPS and a data fusion approach
contributing to these errors. In addition, Weak 4G signal coverage in the fields impacted operator-vehicle communication and
data uploading. Future iterations should address these limitations, exploring alternatives for enhanced accuracy and advancing
defect-sizing technology.This project has received funding from the Shift2Rail Joint Undertaking (JU) under grant agreement No 826255. The JU receives support from the European Union's Horizon 2020 research and innovation programme and the Shift2Rail JU members other than the Union.12th International Conference on Through-life Engineering Services – TESConf202
A risk assessment method for mid-air collisions in urban air mobility operations
Get PDFThis paper proposes a method to systematically assess the risk of mid-air collisions in Urban Air Mobility (UAM) operations, considering unique flight characteristics, mission requirements, and the evolving airspace dynamics. The method encompasses three pivotal phases: the encounter leading to collision, the loss of control post-collision, and the resulting harm to third parties on the ground or in the air. Instead of focusing solely on the collision risk, this method quantifies potential harms, introducing the metric of “fatalities per flight hour” akin to conventional aviation. Three main barriers, strategic mitigation, tactical mitigation, and collision avoidance, are modelled to calculate the probability of mid-air collisions. The gas model evaluates the probability of strategic mitigation failure, while an encounter timeline concept determines the probability of tactical mitigation failure. This paper concludes with Monte Carlo simulations validating the proposed model and a real-world case study demonstrating its applicability for regulators, operators, and stakeholders in ensuring the safety and efficiency of future UAM operations.This work was partially funded by the SESAR JU under grant agreement No 101017702, as part of the European Union’s Horizon 2020 research and innovation programme: AMU-LED (Air Mobility Urban - Large Experimental DemonstrationsIEEE Transactions on Intelligent Vehicle
Impact of Irpex lenis and Schizophyllum commune endophytic fungi on Perilla frutescens: enhancing nutritional uptake, phytochemicals, and antioxidant potential
Get PDFBackground: Endophytic fungi (EF) reside within plants without causing harm and provide benefits such as enhancing nutrients and producing bioactive compounds, which improve the medicinal properties of host plants. Selecting plants with established medicinal properties for studying EF is important, as it allows a deeper understanding of their influence. Therefore, the study aimed to investigate the impact of EF after inoculating the medicinal plant Perilla frutescens, specifically focusing on their role in enhancing medicinal properties. Results: In the current study, the impact of two EF i.e., Irpex lenis and Schizophyllum commune isolated from A. bracteosa was observed on plant Perilla frutescens leaves after inoculation. Plants were divided into four groups i.e., group A: the control group, group B: inoculated with I. lenis; group C: inoculated with S. commune and group D: inoculated with both the EF. Inoculation impact of I. lenis showed an increase in the concentration of chlorophyll a (5.32 mg/g), chlorophyll b (4.46 mg/g), total chlorophyll content (9.78 mg/g), protein (68.517 ± 0.77 mg/g), carbohydrates (137.886 ± 13.71 mg/g), and crude fiber (3.333 ± 0.37%). Furthermore, the plants inoculated with I. lenis showed the highest concentrations of P (14605 mg/kg), Mg (4964.320 mg/kg), Ca (27389.400 mg/kg), and Mn (86.883 mg/kg). The results of the phytochemical analysis also indicated an increased content of total flavonoids (2.347 mg/g), phenols (3.086 mg/g), tannins (3.902 mg/g), and alkaloids (1.037 mg/g) in the leaf extract of P. frutescens inoculated with I. lenis. Thus, overall the best results of inoculation were observed in Group B i.e. inoculated with I. lenis. GC-MS analysis of methanol leaf extract showed ten bioactive constituents, including 9-Octadecenoic acid (Z)-, methyl ester, and hexadecanoic acid, methyl ester as major constituents found in all the groups of P. frutescens leaves. The phenol (gallic acid) and flavonoids (rutin, kaempferol, and quercetin) were also observed to increase after inoculation by HPTLC analysis. The enhancement in the phytochemical content was co-related with improved anti-oxidant potential which was analyzed by DPPH (% Inhibition: 83.45 µg/ml) and FRAP (2.980 µM Fe (II) equivalent) assay as compared with the control group. Conclusion: Inoculation with I. lenis significantly enhances the uptake of nutritional constituents, phytochemicals, and antioxidant properties in P. frutescens, suggesting its potential to boost the therapeutic properties of host plants. Graphical Abstract: (Figure presented.)Microbial Cell Factorie
Development of an integrated energy management system for off-grid solar applications with advanced solar forecasting, time-of-use tariffs, and direct load control
Get PDFEffectively managing and maximizing the integration of renewable energy sources is essential for a sustainable power grid due to the stochastic and intermittent nature of renewable energy generation. This study develops a comprehensive Integrated Energy Management System incorporating supply-demand side management in the form of time-of-use credit, direct load control, and generator control to enhance photovoltaic utilization in off-grid applications. A novel three-step solar energy forecasting approach is proposed in this paper, utilizing low-level data fusion and regression models to predict next-day photovoltaic generation with improved accuracy, and a rule-based decision algorithm is developed to correct forecast errors and manage loads dynamically. A techno-economic analysis covering a 20-year duration is carried out for scenarios with and without the integrated energy management system; three configurations are investigated for supplying an off-grid residential home, including diesel generator, diesel generator/photovoltaic system, and diesel generator/photovoltaic system/integrated energy management system. Results reveal that the hybrid configuration with integrated energy management system achieved 44 % and 46 % reductions in costs and carbon dioxide emissions compared to the diesel generator alone, and 8 % and 9 % compared to the diesel generator/photovoltaic setup respectively. The Integrated Energy Management System further enhanced photovoltaic utilisation rate by over 113 % when compared to the diesel generator/photovoltaic system. Further evaluations include customer behaviour impacts, demonstrating that a fully automated system with 100 % compliance significantly outperforms systems with manual customer control, highlighting the detrimental effect of overrides on the efficiency of direct load control. The flexibility of the Integrated Energy Management System framework allows potential adaptation for on-grid applications, showcasing its utility in diverse operational contexts.Sustainable Energy, Grids and Network
Evaluating erosion risk models in a Scottish catchment using organic carbon fingerprinting
Get PDFPurpose: Identification of hotspots of accelerated erosion of soil and organic carbon (OC) is critical to the targeting of soil conservation and sediment management measures. The erosion risk map (ERM) developed by Lilly and Baggaley (Soil erosion risk map of Scotland, 2018) for Scotland estimates erosion risk for the specific soil conditions in the region. However, the ERM provides no soil erosion rates. Erosion rates can be estimated by empirical models such as the Revised Universal Soil Loss Equation (RUSLE). Yet, RUSLE was not developed specifically for the soil conditions in Scotland. Therefore, we evaluated the performance of these two erosion models to determine whether RUSLE erosion rate estimates could be used to quantify the amount of soil eroded from high-risk areas identified in the ERM.
Methods: The study was conducted in the catchment of Loch Davan, Aberdeenshire, Scotland. Organic carbon loss models were constructed to compare land use specific OC yields based on RUSLE and ERM using OC fingerprinting as a benchmark. The estimated soil erosion rates in this study were also compared with recently published estimates in Scotland (Rickson et al. in Developing a method to estimate the costs of soil erosion in high-risk Scottish catchments, 2019).
Results: The region-specific ERM most closely approximated the relative land use OC yields in streambed sediment however,
the results of RUSLE were very similar, suggesting that, in this catchment, RUSLE erosion rate estimates could be used to
quantify the amount of soil eroded from the high-risk areas identified by ERM. The RUSLE estimates of soil erosion for
this catchment were comparable to the soil erosion rates per land use estimated by Rickson et al. (Developing a method
to estimate the costs of soil erosion in high-risk Scottish catchments, 2019) in Scottish soils except in the case of pasture/
grassland likely due to the pastures in this catchment being grass ley where periods of surface vegetation cover/root network
absence are likely to have generated higher rates of erosion.
Conclusion: Selection of suitable erosion risk models can be improved by the combined use of two sediment origin techniques— erosion risk modelling and OC sediment fingerprinting. These methods could, ultimately, support the development of targeted sediment management strategies to maintain healthy soils within the EU and beyond.Journal of Soils and Sediment
Leakage quantification in metallic pipes under different corrosion exposure times
Get PDFThe combined effects of aqueous corrosion, stress factors, and seeded cracks on leakage in cast iron pipes have not been thoroughly examined due to the complexity and difficulty in predicting their interactions. This study seeks to address this gap by investigating the interdependencies between corrosion, stress, and cracks in cast iron pipes to optimise the material selection and design in corrosive environments. Leakage experiments were conducted under simulated localised corrosive conditions and internal pressure, revealing that leakage increased from 0 to 25 mL with crack sizes of 0.5 mm, 0.8 mm, 1 mm, and 1.2 mm, along with corrosion times of 0, 120, 160, and 200 h, and varying stress levels. An empirical model was developed using a curve-fitting approach to map the relationships among corrosion time, crack propagation, and leakage amount. The results demonstrate that the interaction between corrosion, stress, and crack propagation was complex and nonlinear, and the leakage amount increased from 0.7 to 0.10 mm every 15 min, as evidenced by SEM microstructure images and empirical data.Processe
2D3C measurement of velocity, pressure and temperature fields in a intake flow of an air turbine by Filtered Rayleigh Sattering (FRS) and validation with LDV and PIV
Get PDFA Filtered Rayleigh Scattering Technique is implemented in two different experimental setups and compared to the established velocity measurement techniques Laser Doppler Anemometry (LDA) and Particle Image Velocimetry (PIV). The Frequency Scanning Filtered Rayleigh Scattering Method employed uses an imagefiber bundle which allows for the simultaneous observation of the flow situation from six independent perspectives, utilizing only one sCMOS camera. A testrig with a nominal diameter of 80 mm was implemented by ILA R&D GmbH. Here measurements with straight pipe flow and a swirl generator were realised, as well as comparisions with LDA. A second experiment utilized Cranfields University’s Complex Intake Facility (CCITF), enabling the simulation of the flow field for an engine intake as observed behind an S-Duct diffuser. The diameter in the measuring plane was 160 mm. Measurements up to a mach number of 0.4 were performed and compared with HighSpeed Stereo-PIV (S-PIV) measurements. Good agreement was achieved in respect to both the absolute magnitude of the velocity measurements as well as to the resolution of complex flow structures. The developed FRS multi-view Setup is able to simultaneously determine the 3D velocity components, the pressure and the temperature on a measurement plane with high resolution and without seeding. After calibration the FRS system yields the pressure and temperature within 3 percent respectively 0.8 percent of the reference values. The measured velocity was within 1-2 m/s of the reference.This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 886521 (European Union’s Horizon 2020)21st International Symposium on Applications of Laser and Imaging Techniques to Fluid Mechanic