| 1. |
- Abouhatab, Assem
(författare)
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Assessing Green Governance Efficiency in China: Does Technological and Environmental Concerns Matter in Energy Efficiency?
- 2021
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Ingår i: Frontiers in Energy Research. - : Frontiers Media SA. - 2296-598X. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Considering the significance of green governance in economic restructuring and the green technology revolution, this study examines the impact of China’s recent green governance policies and their implications in various regions; it also examines new models, methods, and development directions for China’s green governance in the future. Green governance efficiency and spatial patterns have been studied through 2008–2018 data using a three-stage generalized panel Data Envelopment Analysis (DEA) model, spatial autocorrelation model, spatial gravity model, and social network analysis. The study summarizes the status and role of each provincial region in green governance based on the social network of green governance efficiency under the network architecture. It concludes that (1) green governance efficiency in China’s provinces has a U-shaped trend, with non-managerial elements in the external environment masking genuine green governance efficiency. (2) During the study period, the overall efficiency of the industrial system improved. The efficiency of the manufacturing and wastewater stages has been substantially enhanced, but no significant gains were observed in the treatment stages of solid and gas waste. (3) Although China has made progress in enhancing the overall efficiency of its industrial system, there is still potential for improvement. (4) China has established a “three horizontal and two vertical grid-type” green governance spatial correlation structure among the sub-stages of the industrial system, and the radiation impact of major provincial areas would increase overall green governance efficiency.
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| 2. |
- Ahmadi Moghaddam, Elham, et al.
(författare)
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Gas Hydrates as a Means for Biogas and Biomethane Distribution
- 2021
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Ingår i: Frontiers in Energy Research. - : Frontiers Media SA. - 2296-598X. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Biomethane is receiving great attention as a renewable energy gas with lower environmental impacts and diversified sources of production. However, availability of gas infrastructure is an important factor in biomethane development and use. Biomethane can be distributed by the natural gas or local biogas grid. Biomethane can also be road-transported as compressed biomethane (CBG) or liquefied bio-methane (LBG). Biomethane could be distributed via gas hydration technology, where methane molecules are physically trapped within the crystalline structures of frozen host water molecules as gas hydrate compounds. Using life cycle assessment methodology, this study compared the energy performance and climate impact of two gas hydrate scenarios, biogas hydrate and biomethane hydrate, with that of a base case distributing biomethane as CBG. The technical system, from biogas upgrading, hydration, compression and road transport to filling station of biomethane as CBG, was included in the analysis. Results of this study show that distribution of biomethane as gas hydrates had a lower energy performance and higher climate impact than compressed biomethane distribution. The low energy performance was due to high electricity demand in hydrate formation and dissociation processes. The gas hydrate scenarios also had higher climate impacts as a result of high methane losses from hydrate formation and dissociationdissociation and emissions related to energy source use. Biogas upgrading to biomethane also significantly contributed to methane losses and climate impact of the scenarios studied.
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| 3. |
- Ahmed, Ubaid, et al.
(författare)
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Short-term wind power forecasting using integrated boosting approach
- 2024
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Ingår i: Frontiers in Energy Research. - : Frontiers Media S.A.. - 2296-598X. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Rapidly increasing global energy demand and environmental concerns have shifted the attention of policymakers toward the large-scale integration of renewable energy resources (RERs). Wind energy is a type of RERs with vast energy potential and no environmental pollution is associated with it. The sustainable development goals: affordable and clean energy, climate action, and industry, innovation and infrastructure, can be achieved by integrating wind energy into the existing power systems. However, the integration of wind energy will bring instability challenges due to its intermittent nature. Mitigating these challenges necessitates the implementation of effective wind power forecasting models. Therefore, we have proposed a novel integrated approach, Boost-LR, for hour-ahead wind power forecasting. The Boost-LR is a multilevel technique consisting of non-parametric models, extreme gradient boosting (XgBoost), categorical boosting (CatBoost), and random forest (RF), and parametric approach, linear regression (LR). The first layer of the Boost-LR uses the boosting algorithms that process the data according to their tree development architectures and pass their intermediary forecast to LR which is deployed in layer two and processes the intermediary forecasts of layer one models to provide the final predicted wind power. To demonstrate the generalizability and robustness of the proposed study, the performance of Boost-LR is compared with the individual models of CatBoost, XgBoost, RF, deep learning networks: long short-term memory (LSTM) and gated recurrent unit (GRU), Transformer and Informer models using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and normalized root mean square error (NRMSE). Findings demonstrate the effectiveness of the Boost-LR as its forecasting performance is superior to the compared models. The improvement in MAE of Boost-LR is recorded as to be 31.42%, 32.14%, and 27.55% for the datasets of Bruska, Jelinak, and Inland wind farm, respectively as compared to the MAE of CatBoost which is revealed as the second-best performing model. Moreover, the proposed study also reports a literature comparison that further validates the effectiveness of Boost-LR performance for short-term wind power forecasting.
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| 4. |
- Aravind, P. V., et al.
(författare)
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Negative emissions at negative cost-an opportunity for a scalable niche
- 2022
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Ingår i: Frontiers in Energy Research. - : Frontiers Media SA. - 2296-598X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- In the face of the rapidly dwindling carbon budgets, negative emission technologies are widely suggested as required to stabilize the Earth's climate. However, finding cost-effective, socially acceptable, and politically achievable means to enable such technologies remains a challenge. We propose solutions based on negative emission technologies to facilitate wealth creation for the stakeholders while helping to mitigate climate change. This paper comes up with suggestions and guidelines on significantly increasing carbon sequestration in coffee farms. A coffee and jackfruit agroforestry-based case study is presented along with an array of technical interventions, having a special focus on bioenergy and biochar, potentially leading to "negative emissions at negative cost. " The strategies for integrating food production with soil and water management, fuel production, adoption of renewable energy systems and timber management are outlined. The emphasis is on combining biological and engineering sciences to devise a practically viable niche that is easy to adopt, adapt and scale up for the communities and regions to achieve net negative emissions. The concerns expressed in the recent literature on the implementation of emission reduction and negative emission technologies are briefly presented. The novel opportunities to alleviate these concerns arising from our proposed interventions are then pointed out. Our analysis indicates that 1 ha coffee jackfruit-based agroforestry can additionally sequester around 10 tonnes of CO2-eq and lead to an income enhancement of up to 3,000-4,000 Euros in comparison to unshaded coffee. Finally, the global outlook for an easily adoptable nature-based approach is presented, suggesting an opportunity to implement revenue-generating negative emission technologies on a gigatonne scale. We anticipate that our approach presented in the paper results in increased attention to the development of practically viable science and technology-based interventions in order to support the speeding up of climate change mitigation efforts.
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| 5. |
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| 6. |
- Beurey, Claire, et al.
(författare)
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Review and Survey of Methods for Analysis of Impurities in Hydrogen for Fuel Cell Vehicles According to ISO 14687:2019
- 2021
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Ingår i: Frontiers in Energy Research. - : Frontiers Media S.A.. - 2296-598X. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Gaseous hydrogen for fuel cell electric vehicles must meet quality standards such as ISO 14687:2019 which contains maximal control thresholds for several impurities which could damage the fuel cells or the infrastructure. A review of analytical techniques for impurities analysis has already been carried out by Murugan et al. in 2014. Similarly, this document intends to review the sampling of hydrogen and the available analytical methods, together with a survey of laboratories performing the analysis of hydrogen about the techniques being used. Most impurities are addressed, however some of them are challenging, especially the halogenated compounds since only some halogenated compounds are covered, not all of them. The analysis of impurities following ISO 14687:2019 remains expensive and complex, enhancing the need for further research in this area. Novel and promising analyzers have been developed which need to be validated according to ISO 21087:2019 requirements. © 2021 Beurey, Gozlan, Carré, Bacquart, Morris, Moore, Arrhenius, Meuzelaar, Persijn, Rojo and Murugan.
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| 7. |
- Bian, Boshen, et al.
(författare)
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Scalability of Nek5000 on High-Performance Computing Clusters Toward Direct Numerical Simulation of Molten Pool Convection
- 2022
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Ingår i: Frontiers in Energy Research. - : Frontiers Media SA. - 2296-598X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- In a postulated severe accident, a molten pool with decay heat can form in the lower head of a reactor pressure vessel, threatening the vessel’s structural integrity. Natural convection in molten pools with extremely high Rayleigh (Ra) number is not yet fully understood as accurate simulation of the intense turbulence remains an outstanding challenge. Various models have been implemented in many studies, such as RANS (Reynolds-averaged Navier–Stokes), LES (large-eddy simulation), and DNS (direct numerical simulation). DNS can provide the most accurate results but at the expense of large computational resources. As the significant development of the HPC (high-performance computing) technology emerges, DNS becomes a more feasible method in molten pool simulations. Nek5000 is an open-source code for the simulation of incompressible flows, which is based on a high-order SEM (spectral element method) discretization strategy. Nek5000 has been performed on many supercomputing clusters, and the parallel performance of benchmarks can be useful for the estimation of computation budgets. In this work, we conducted scalability tests of Nek5000 on four different HPC clusters, namely, JUWELS (Atos Bullsquana X1000), Hawk (HPE Apollo 9000), ARCHER2 (HPE Cray EX), and Beskow (Cray XC40). The reference case is a DNS of molten pool convection in a hemispherical configuration with Ra = 1011, where the computational domain consisted of 391 million grid points. The objectives are (i) to determine if there is strong scalability of Nek5000 for the specific problem on the currently available systems and (ii) to explore the feasibility of obtaining DNS data for much higher Ra. We found super-linear speed-up up to 65536 MPI-rank on Hawk and ARCHER2 systems and around 8000 MPI-rank on JUWELS and Beskow systems. We achieved the best performance with the Hawk system with reasonably good results up to 131072 MPI-rank, which is attributed to the hypercube technique on its interconnection. Given the current HPC technology, it is feasible to obtain DNS data for Ra = 1012, but for cases higher than this, significant improvement in hardware and software HPC technology is necessary.
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| 8. |
- Biendicho, Jordi Jacas, et al.
(författare)
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New Opportunities for Air Cathode Batteries; in-Situ Neutron Diffraction Measurements
- 2018
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Ingår i: Frontiers in energy research. - : Frontiers Media SA. - 2296-598X. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Batteries with air electrodes are gaining interest as Energy Storage Systems (ESSs) for Electrical Vehicles (EVs) because of their high specific energy density. The electrochemical performance of these batteries is limited by the metallic electrode, which suffers structural transformations and corrosion during cycling that reduces the cycle life of the battery. In this context, relevant information on the discharge products may be obtained by in-situ neutron diffraction, a suitable technique to study electrodes that contain light elements or near neighbor elements in the periodic table. Case studies of MH-air and Fe-air batteries are highlighted.
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| 9. |
- Chawade, Aakash
(författare)
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Additives as a Support Structure for Specific Biochemical Activity Boosts in Anaerobic Digestion: A Review
- 2020
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Ingår i: Frontiers in Energy Research. - : Frontiers Media SA. - 2296-598X. ; 8
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Forskningsöversikt (refereegranskat)abstract
- Anaerobic digestion is an attractive technology for resource management of organic waste and stubble. The state of biochemical reactions and activities in anaerobic digestion determines the process stability. Biochemical activities, such as syntrophic, metabolic, catalytic, and enzymatic activities, regulate the anaerobic efficiency for improved methane yield. Inhibitors, such as access to volatile fatty acids, ammonia, sulfur, and heavy metals, may slow down the anaerobic digestion and may cause reactor failure. However, additives for various biochemical activities may help to diminish the effect of inhibitors as well as improve process stability for enhanced methane yield. This manuscript presents an overview of various additives for enhancing the biochemical activities. The overview consists of the application of (a) conductive material for improving syntrophic activity, (b) trace metals for metabolic activities, (c) nanoparticles for improving catalytic activity, metabolism, and symbiosis during AD, (d) biological additives for enzymatic action, and (e) application of zeolite for introducing cation exchange properties in anaerobic digestion. Also, a comparison of various additives as per biochemical activity has also been performed for a deeper insight into the application of additives in anaerobic digestion.
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| 10. |
- Cousins, Dylan S., et al.
(författare)
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Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover
- 2022
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Ingår i: Frontiers in Energy Research. - : Frontiers Media S.A.. - 2296-598X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Feedstock heterogeneity is a key challenge impacting the deconstruction and conversion of herbaceous lignocellulosic biomass to biobased fuels, chemicals, and materials. Upstream processing to homogenize biomass feedstock streams into their anatomical components via air classification allows for a more tailored approach to subsequent mechanical and chemical processing. Here, we show that differing corn stover anatomical tissues respond differently to pretreatment and enzymatic hydrolysis and therefore, a one-size-fits-all approach to chemical processing biomass is inappropriate. To inform on-line downstream processing, a robust and high-throughput analytical technique is needed to quantitatively characterize the separated biomass. Predictive correlation of near-infrared spectra to biomass chemical composition is such a technique. Here, we demonstrate the capability of models developed using an “off-the-shelf,” industrially relevant spectrometer with limited spectral range to make strong predictions of both cell wall chemical composition and the relative abundance of anatomical components of the corn stover, the latter for the first time ever. Gaussian process regression (GPR) yields stronger correlations (average R2v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R2v = 84% for chemical composition and 92% for anatomical relative abundance). In nearly all cases, both GPR and PLS outperform models generated using neural networks. These results highlight the potential for coupling NIRS with predictive models based on GPR due to the potential to yield more robust correlations.
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