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Terrestrial groundwater travels through subterranean estuaries before reaching the sea. Groundwater‐derived nutrients drive coastal water quality, primary production, and eutrophication. We determined how dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved organic nitrogen (DON) are transformed within subterranean estuaries and estimated submarine groundwater discharge (SGD) nutrient loads compiling > 10,000 groundwater samples from 216 sites worldwide. Nutrients exhibited complex, nonconservative behavior in subterranean estuaries. Fresh groundwater DIN and DIP are usually produced, and DON is consumed during transport. Median total SGD (saline and fresh) fluxes globally were 5.4, 2.6, and 0.18 Tmol yr−1 for DIN, DON, and DIP, respectively. Despite large natural variability, total SGD fluxes likely exceed global riverine nutrient export. Fresh SGD is a small source of new nutrients, but saline SGD is an important source of mostly recycled nutrients. Nutrients exported via SGD via subterranean estuaries are critical to coastal biogeochemistry and a significant nutrient source to the oceans.

High-density (HDA) and low-density amorphous ices (LDA) are believed to be counterparts of the high- and low-density liquid phases of water, respectively. In order to better understand how the vibrational modes change during the transition between the two solid states, we present infrared spectroscopy measurements, following the change of the decoupled OD-stretch (vOD) (similar to 2460 cm-1) and OH-combinational mode (vOH + v2, vOH + 2vR) (similar to 5000 cm-1). We observe a redshift from HDA to LDA, accompanied with a drastic decrease of the bandwidth. The hydrogen bonds are stronger in LDA, which is caused by a change in the coordination number and number of water molecules interstitial between the first and second hydration shell. The unusually broad uncoupled OD band also clearly distinguishes HDA from other crystalline high-pressure phases, while the shape and position of the in situ prepared LDA are comparable to those of vapor-deposited amorphous ice.

In this study, a bi-level model is developed to quantify the value of orderly electric vehicle (EV) charging in carbon reduction. Specifically, the upper-level model optimizes each EV driver's charging schedule to diminish the total carbon emissions without impacting their travel plans, and the lower-level problem aims to fulfill electricity demands with minimal electricity dispatch cost. Based on real-world operation data obtained from 3,777 battery EVs (BEVs) in Shanghai over 11 months and local power plant data, the total carbon emissions generated by BEVs in Shanghai is calculated as 1,176,637 tons over this period, averaging 73 gCO2/km per BEV. By administering charging control to all BEVs in Shanghai, the above emission could be curtailed by 39%. Sensitivity analyses uncover that augmenting battery capacity and integrating wind power can significantly enhance emission reductions, while increasing the flexibility of the power plant might diminish the effectiveness of orderly EV charging.

The membrane-cryogenic hybrid process is a promising CO2 capture process, which combines the advantages of membrane and cryogenic, such as high efficiency (up to 98 % CO2 captured) and low energy consumption (specific energy consumption around 1.7 MJ/kg CO2 avoided). Through pretreatment by membranes, CO2 concentration can be increased, which makes it possible to separate CO2 via phase change in the cryogenic unit. This work reviews the current status of the development of membrane-cryogenic hybrid processes. The synergy between membrane and cryogenic separation is summarized to identify the bottleneck of such processes and provide insights for process improvement. It was found that cold temperatures would be beneficial to reduce CO2 activation energy and then improve CO2 selectivity of membranes. To further improve the CO2 separation performance, the potential intensification methods of the membrane-cryogenic hybrid process including cold-membrane synthesis, process optimization via heat integration are discussed and envisioned.

Global warming and global food security make it critical to develop energy -efficient, cost effective and sustainable food production systems with the least environmental footprint. Today, urban farming is supplying 15 -20 % of the world ‘s food and will play a more pivotal role in ensuring food security for burgeoning populations in the future. As one of the most critical growth -promoting agents for plants, carbon dioxide (CO 2 ) is commonly added in urban farming to enhance the yield and productivity, which can be about 9 -45 %. In order to improve energy efficiency and reduce cost, judicious selection and integration of CO 2 enrichment technologies are crucial. This reviews the features and limitations of various technologies that have been used or can be used for urban farms. Key performance indicators, including CO 2 purity, enrichment energy consumption and enrichment cost, have been employed for technology comparison. Combustion and decomposing technologies are characterized as low cost and simple equipment; however, the pollutants and odor produced by these technologies limit their applications in urban farms. Direct air CO 2 capture technologies are emerging technologies; however, their high investment cost and energy consumption need to be reduced before they can be widely adopted. Suggestions on future research and development to advance the capabilities of urban farms are also provided.

Integrating carbon dioxide (CO2) capture in biomass or waste-fired combined heat and power (CHP) plants has been considered a key measure to achieve negative emissions. To support decision-making, an accurate assessment of the potential contribution and the associated cost from the national perspective is urgently needed. This paper proposed a bottom-up approach based on a dynamic modelling to evaluate the potental of nationwide negative emissions. As heat supply is often prioritized by CHP plants, unchanged heat generation is a prerequisite of this study. Two operating modes (OMs) for the integration of CO2 capture are investigated, which can represent the upper and lower boundaries of CO2 capture: OM1 aims to maximize the amount of captured CO2, while electricity generation can be sacrificed; OM2 aims to maximize the amount of captured CO2, while the electricity generation is maintained unchanged. Sweden is employed as a case study. Results show that operating CO2 capture in OM1 can achieve 8.7 million ton CO2 nationwide negative emissions a year, while operating CO2 capture in OM2 can generate 4.3 million ton CO2 positive emissions a year, which represents a reduction of 6.3 million tonCO2 a year compared with the reference plant without CO2 capture. The levelized costs of CO2 avoided are 36.9 USD/tonCO2 and 52.0 USD/tonCO2 for OM1 and OM2, respectively. The biogenic fraction of waste has a significant influence on negative emissions. According to the Swedish climate goal about bioenergy with CO2 capture and storage (BECCS), to achieve 3 million ton negative CO2 emissions a year, the minimum biogenic fractions should be 32.8% and 84.3% for operating CO2 capture in OM1 and OM2, respectively; in contrast, to achieve 10 million ton negative emissions a year, biomass and waste-fired CHP plants have to operate CO2 capture in OM1 and the biogenic fraction needs to be over 59.9%.

Vehicle-to-grid (V2G) technology enables electric vehicles (EVs) to serve as flexible load storage resources, which is expected to play a pivotal role in pursuing carbon neutrality. However, existing studies on the effect of V2G at different stages of carbon neutrality is not sufficient, and there is a lack of discussion on the optimal adoption period for V2G in the context of electricity marketization trading. To fill this gap, a new methodology is proposed in this study to analyze multi-dimension effects of V2G towards carbon neutrality. The model is a novel attempt of applying partial market equilibrium model to depict the interaction between electricity suppliers and V2G adopters. By applying in a China's case, the results demonstrate that: (1) EVs with V2G can substitute 22.2 %–30.1 % energy storage and accelerate the phase-out of coal-fired power. (2) V2G can effectively mitigate electricity price fluctuations, moreover, more fast charging infrastructure will strengthen such effect. (3) V2G only become attractive when renewable energy penetration rate reaches 80 %, otherwise, it cannot effectively reduce the total social cost and carbon emission. (4) In the carbon neutrality scenario with limited emission, the emission reduction effect of V2G is weakened, however, its economic benefit keeps increasing.

The backup power system (BPS) is essential to data centers (DCs) in case of grid outage. Currently, the diesel generator (DG) is the most commonly used solution. However, due to the need of decarbonization, fuel cells (FCs) are emerging as a promising alternative. This paper conducts a holistic economic analysis to investigate the benefit from using FCs to replace DGs, which takes into account more key impact factors. Different requirements by different Tiers of DCs, such as on the BPS runtime and the BPS test time, are considered. Under the benchmark condition, i.e. 800 USD/kW for the FC capital cost, 4.8 USD/kg, 1.25 USD/L and 90 USD/ton for hydrogen, diesel, and CO2 prices, the payback period is 13.4 years and 13.1 years when using FCs to replace DGs for Tier 1–2 DCs and Tier 3 DCs. Since Tier 4 DCs require more units, FCs can be economically feasible if the FC capital cost reduces by 20.3 %, the diesel price increases by 19.7 %, or the green hydrogen price decreases by 35.5 %, compared to the benchmark conditions. Among the factors, the FC capital cost, the diesel price, and the hydrogen price are found to affect the payback period mostly.

The performance of lithium-ion batteries (LIBs) is sensitive to the operating temperature, and the design and operation of battery thermal management systems reply on accurate information of LIBs' temperature. This study proposes a data-driven model based on neural network (NN) for estimating the temperature profile of a LIB module. Only one temperature measurement is needed for the battery module, which can assure a low cost. The method has been tested for battery modules consisting of prismatic and cylindrical batteries. In general, a good accuracy can be observed that the root mean square error (RMSE) of esitmated temperatures is less than 0.8 °C regardless of the different operating conditions, ambient temperatures, and heat dissipation conditions.

Integrating CO2 capture with biomass-fired combined heat and power (bio-CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass, including waste, and the dynamic operation of bio-CHP plants leads to large fluctuations in the flowrate and CO2 concentration of the flue gas (FG), which further affect the operation of post-combustion CO2 capture. To optimize the dynamic operation of CO2 capture, a reliable model to predict the FG flowrate and CO2 concentration in real time is essential. In this paper, a data-driven model based on the Transformer architecture is developed. The model validation shows that the root mean squared error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (PPMCC) of Transformer are 0.3553, 0.0189, and 0.8099 respectively for the prediction of FG flowrate; and 13.137, 0.0318, and 0.8336 respectively for the prediction of CO2 concentration. The potential impact of various meteorological parameters on model accuracy is also assessed by analyzing the Shapley value. It is found that temperature and direct horizontal irradiance (DHI) are the most important factors, which should be selected as input features. In addition, using the near-infrared (NIR) spectral data as input features is also found to be an effective way to improve the prediction accuracy. It can reduce RMSE and MAPE for CO2 concentration from 0.2982 to 0.2887 and 0.0158 to 0.0157 respectively, and RMSE and MAPE for FG flowrate from 4.9854 to 4.7537 and 0.0141 to 0.0121 respectively. The Transformer model is also compared to other models, including long short-term memory network (LSTM) and artificial neural network (ANN), which results show that the Transformer model is superior in predicting complex dynamic patterns and nonlinear relationships., AI-BECCS

Using metal hydride for hydrogen storage in stationary applications and for transportation is a promising technology due to its advantages of large hydrogen storage capacity, low pressure and low energy consumption. Combining the metal hydride reactor with PCM is expected to recover the heat generated during the hydrogen absorption and use it for hydrogen desorption, thus improving the energy efficiency of the system. This paper proposes a metal hydride reactor integrated with honeycomb fins and PCM to enhance heat transfer. Based on simulations, the results show that the achieved hydrogen storage capacity is 1.326 wt%, the gravimetric and volumetric storage densities are 0.411% and 14.76 kg of H2 per m3, respectively, and the mean saturated rates are 1.222 × 10−3 g s−1 and 0.773 × 10−3 g s−1 for absorption and desorption processes. Compared with the reactor without fins, the mass and volume of the reactor using honeycomb fins are increased, resulting in a decrease in gravimetric and volumetric storage density, but a increase in reaction rate during hydrogen absorption and desorption processes. Based on this structure, we also propose a honeycomb fin reactor filled with sandwich PCM to further accelerate the heat transfer in the reaction process. Compare to the reactor with PCM only filled on the periphery of the honeycomb fins, the hydrogen absorption and desorption times are shortened by about 86.4% and 81.1%, respectively. In addition, different reactor structures are compared using multiple KPIs to provide relevant suggestions for the reactor optimization. The obtained research results can provide a reference for effective thermal management methods in MH storage systems.

The current vehicle-to-grid (V2G) project pilots generally face the problem of low user participation willingness, mainly due to the lack of detailed consideration of trade mechanisms and incentive policies. To address the potential threat posed by the large-scale application of electric vehicles (EVs) to the power grid system, an analysis of the promotion strategies of V2G technology among EV owners is deemed necessary. In this study, a new simulation framework for V2G adoption and heterogeneous trade mechanism evaluation based on social network theory is constructed. The diffusion process of V2G adoption and charging/discharging behavior is simulated under three trading mechanism scenarios: Time-of-Use (ToU) pricing + fixed service fee (ToU-F), regulated pricing + fixed service fee (Reg-F), and dynamic pricing + fixed service fee (Dyn-F). The research results indicate that (1) In terms of V2G adoption scale, both the Reg-F and Dyn-F scenarios have reached the maximum number of adopters, increasing by 41.8% compared to the ToU-F scenario. The main reason is that the former two trading mechanisms achieve a larger price difference, creating more opportunities for charge and discharge arbitrage. (2) Regarding EV load regulation, the discharge amount of EVs under the Reg-F and Dyn-F scenarios is much higher than that under the ToU-F scenario. The Dyn-F scenario further avoids drastic fluctuations in load. (3) In terms of benefit distribution, only under the Reg-F scenario have both the aggregator and V2G adopters gained higher profits.

Battery energy storage systems (BESSs) play an important role in increasing the use of renewable energy sources. Owing to the temperature sensitivity of lithium-ion batteries (LIBs), battery thermal management systems (BTMSs) are crucial to ensuring the safe and efficient operation of BESSs. Previous works mainly focused on evaluating the performance of BTMS; however, little attention has been paid to the minimum cooling requirements of BESSs, which are important for optimizing the design and operation of BTMSs. To bridge the knowledge gap, this work investigated the performance of air cooling for a battery cabin under different charge/discharge (C) rates by using a computational fluid dynamics (CFD) model, which is coupled with a battery model. Simulation results show that the inlet airflow rate has the strongest influence. For the studied cases, when the battery operates at C-rates lower than 3, the inlet temperature should be controlled below 35 °C, and the gap between the batteries should be greater than 3 mm to meet the minimum heat dissipation requirement. At a C-rate of 0.5C, natural convection is sufficient to meet the cooling need, whereas at 1C or higher C-rates, forced convection has to be used. Increasing the number of batteries, for example, from 6 to 8, has negligible impact on the inlet flow required to assure the heat dissipation.

Layered nickel-rich oxide LiNixCoyMn1-x-yO2 (0.6 ≤x<1) is a highly promising positive electrode material. However, the cycling stability of nickel-rich positive electrode materials is limited by particle fracture and a series of side reactions. A comprehensive understanding of particle cracking mechanisms is paramount for material optimization, but crack initiation and propagation have received limited research attention. This paper uses a quasi in-situ SEM observation method and an in-situ optical microscopy observation method to observe crack evolution in real time. The results show rapid cracking behavior under hazardous operating conditions and cracking during cycling under mild conditions. Center cracks and surface cracks are observed during cycling. The observation methods and these insights into the crack behavior offer theoretical guidance for the structural engineering of NCM cathode particles.

Self-powered smart buoys are widely used in sustainable sea, such as marine environmental monitoring. The article designs a self-powered and self-sensing point-absorber wave energy converter based on the two-arm mechanism. The system consists of the wave energy capture module, the power take-off module, the generator module and the energy storage module. As the core component of the wave energy converter, the power take-off module is mainly composed of a two-arm mechanism, which can convert the oscillation heave motion into unidirectional rotary motion. To evaluate the power generation performance of the system, the kinematic and dynamic models of the wave energy converter with the flywheel are established, and the disengagement and engagement phenomena of the flywheel are analyzed. The effectiveness of the prototype in capturing wave energy is verified through dry experiments in lab and field tests. The dry experiment reveals that the maximum output power of the system is 5.67 W, and the maximum and average mechanical efficiency are 66.63 % and 48.35 %, respectively. Additionally, the field test demonstrates that the peak output power can reach 92 W. Meanwhile, the generated electrical signals can be processed by deep learning algorithms to accurately identify different wave states. This high performance confirms that the proposed wave energy converter can meet its own energy needs by capturing wave energy in the marine environment, while also achieving self-sensing for wave condition monitoring. The system has great potential for promoting the development of intelligent sustainable sea in the future.

Fluctuations in electricity price create arbitrage opportunities for compressed CO2 energy storage (CCES) systems. However, previous studies often neglected the dynamic characteristics of CCES systems, leading to inaccurate assessments. This paper addresses this gap by evaluating the CCES system arbitrage considering its dynamic characteristics. We introduce a novel indicator, state of charge (SOC), into a mixed-integer linear programming (MILP) optimization model to capture the dynamics. Utilizing real electricity prices, the model optimizes the CCES operation strategy for a maximum profit. The results demonstrate that a CCES system with a 267 MWh capacity could achieve a total income of 22.5 MEUR in 2022, with a net present value (NPV) of 258.1 MEUR over 35 years, a payback time of 2 years, and an average round-trip efficiency (ARTE) of 77.0 %. Sensitivity analysis reveals that the sizes of the compressor, the expander, and the high-pressure gas tank significantly impact the arbitrage potential. In contrast, the steady-state model-based results demonstrate that the CCES system could yield a higher NPV of 573.7 MEUR, a shorter payback time of 1 year, and a higher ARTE of 87.0 %. This emphasizes the pivotal importance of integrating dynamic characteristics into the design and assessment of CCES systems for arbitrage assessment.

The integration of artificial intelligence (AI) with radiology has marked a transformative era in medical diagnostics. Vision foundation models have been adopted to enhance radiologic imaging analysis. However, the distinct complexities of radiological imaging, including the interpretation of 2D and 3D radiological data, pose unique challenges that existing models, trained on general non-medical images, fail to address adequately. To bridge this gap and capitalize on the diagnostic precision required in medical imaging, we introduce RadCLIP: a pioneering cross-modal foundational model that harnesses Contrastive Language-Image Pre-training (CLIP) to refine radiologic image analysis. RadCLIP incorporates a novel 3D slice pooling mechanism tailored for volumetric image analysis and is trained using a comprehensive and diverse dataset of radiologic image-text pairs. Our evaluations demonstrate that RadCLIP effectively aligns radiological images with their corresponding textual annotations, and in the meantime, offers a robust vision backbone for radiologic imagery with significant promise.

Unraveling the mechanism of water's glass transition and the interconnection between amorphous ices and liquid water plays an important role in our overall understanding of water. X-ray photon correlation spectroscopy (XPCS) experiments were conducted to study the dynamics and the complex interplay between the hypothesized glass transition in high-density amorphous ice (HDA) and the subsequent transition to low-density amorphous ice (LDA). Our XPCS experiments demonstrate that a heterodyne signal appears in the correlation function. Such a signal is known to originate from the interplay of a static component and a dynamic component. Quantitative analysis was performed on this heterodyne signal to extract the intrinsic dynamics of amorphous ice during the HDA-LDA transition. An angular dependence indicates non-isotropic, heterogeneous dynamics in the sample. Using the Stokes-Einstein relation to extract diffusion coefficients, the data are consistent with the scenario of static LDA islands floating within a diffusive matrix of high-density liquid water., QC 20240110

The integration of CO2 capture with biomass-fired power plants has attracted much attention due to its ability to achieve negative emissions. Waste-fired combined heat and power (CHP) plants with CO2 capture, on the other hand, has received little attention, and their potential remains unclear. This study aims to identify the possible range of the amount of captured CO2 and investigate the impact of CO2 capture on the performance of waste-fired CHP plants. Since heat is the primary product of CHP plants, it is important to maintain heat production unchanged when CO2 capture is integrated. Based on this prerequisite, two operating strategies (OS) were investigated, which correspond to the upper and lower boundaries of CO2 capture: OS1 was to maximize the amount of captured CO2 while keeping the heat supplied to the district heating (DH) network unchanged; and OS2 was to maximize CO2 capture while keeping both supplied heat and generated electricity unchanged. To obtain more accurate results regarding the CO2 capture, a dynamic model developed in Aspen Hysys™ was utilized to simulate monoethanolamine (MEA) based chemical absorption for CO2 capture. By using real dynamic data from a waste-fired CHP plant, dynamic simulation results showed that the highest amount of captured CO2, which was achieved in OS1, was 401 kton/year, corresponding to a CO2 capture ratio of 82%; while the lowest amount of captured CO2, which was achieved in OS2, was 99 kton/year, corresponding to a CO2 capture ratio of 20%. For OS1, the electricity generation was substantially decreased by 61%. When determining the negative emission, the emission resulted from the share of fossil fuel in the waste needs to be excluded. For the studied CHP plant, the fossil share was around 45%. As a result, only OS1 can achieve the negative emission, which was 181 kton/year; while OS2 still led to positive emissions. Compared to the plant without CO2 capture, the carbon intensity of heat was reduced from 0.405 to, QC 20231215

Tunable dynamic networks of cellulose nanofibrils (CNFs) are utilized to prepare high-performance polymer gel electrolytes. By swelling an anisotropically dewatered, but never dried, CNF gel in acidic salt solutions, a highly sparse network is constructed with a fraction of CNFs as low as 0.9%, taking advantage of the very high aspect ratio and the ultra-thin thickness of the CNFs (micrometers long and 2–4 nm thick). These CNF networks expose high interfacial areas and can accommodate massive amounts of the ionic conductive liquid polyethylene glycol-based electrolyte into strong homogeneous gel electrolytes. In addition to the reinforced mechanical properties, the presence of the CNFs simultaneously enhances the ionic conductivity due to their excellent strong water-binding capacity according to computational simulations. This strategy renders the electrolyte a room-temperature ionic conductivity of 0.61 ± 0.12 mS cm−1 which is one of the highest among polymer gel electrolytes. The electrolyte shows superior performances as a separator for lithium iron phosphate half-cells in high specific capacity (161 mAh g−1 at 0.1C), excellent rate capability (5C), and cycling stability (94% capacity retention after 300 cycles at 1C) at 60 °C, as well as stable room temperature cycling performance and considerably improved safety compared with commercial liquid electrolyte systems., QC 20231115

Tunable dynamic networks of cellulose nanofibrils (CNFs) are utilized to prepare high-performance polymer gel electrolytes. By swelling an anisotropically dewatered, but never dried, CNF gel in acidic salt solutions, a highly sparse network is constructed with a fraction of CNFs as low as 0.9%, taking advantage of the very high aspect ratio and the ultra-thin thickness of the CNFs (micrometers long and 2-4 nm thick). These CNF networks expose high interfacial areas and can accommodate massive amounts of the ionic conductive liquid polyethylene glycol-based electrolyte into strong homogeneous gel electrolytes. In addition to the reinforced mechanical properties, the presence of the CNFs simultaneously enhances the ionic conductivity due to their excellent strong water-binding capacity according to computational simulations. This strategy renders the electrolyte a room-temperature ionic conductivity of 0.61 +/- 0.12 mS cm(-1) which is one of the highest among polymer gel electrolytes. The electrolyte shows superior performances as a separator for lithium iron phosphate half-cells in high specific capacity (161 mAh g(-1) at 0.1C), excellent rate capability (5C), and cycling stability (94% capacity retention after 300 cycles at 1C) at 60 degrees C, as well as stable room temperature cycling performance and considerably improved safety compared with commercial liquid electrolyte systems., Funding Agencies|Digital Cellulose Centre, a Vinnova (Swedish Innovation Agency) [2016-05193]; Knut and Alice Wallenbergs Research Foundation; Wallenberg Wood Science Centre at KTH [KAW 2018.0452-WWSC 2.0]; A-forsk foundation [19-320]

Unraveling the mechanism of water’s glass transition and the interconnection between amorphous ices and liquid water plays an important role in our overall understanding of water. X-ray photon correlation spectroscopy (XPCS) experiments were conducted to study the dynamics and the complex interplay between the hypothesized glass transition in high-density amorphous ice (HDA) and the subsequent transition to low-density amorphous ice (LDA). Our XPCS experiments demonstrate that a heterodyne signal appears in the correlation function. Such a signal is known to originate from the interplay of a static component and a dynamic component. Quantitative analysis was performed on this heterodyne signal to extract the intrinsic dynamics of amorphous ice during the HDA–LDA transition. An angular dependence indicates non-isotropic, heterogeneous dynamics in the sample. Using the Stokes–Einstein relation to extract diffusion coefficients, the data are consistent with the scenario of static LDA islands floating within a diffusive matrix of high-density liquid water.

Tunable dynamic networks of cellulose nanofibrils (CNFs) are utilized to prepare high-performance polymer gel electrolytes. By swelling an anisotropically dewatered, but never dried, CNF gel in acidic salt solutions, a highly sparse network is constructed with a fraction of CNFs as low as 0.9%, taking advantage of the very high aspect ratio and the ultra-thin thickness of the CNFs (micrometers long and 2-4 nm thick). These CNF networks expose high interfacial areas and can accommodate massive amounts of the ionic conductive liquid polyethylene glycol-based electrolyte into strong homogeneous gel electrolytes. In addition to the reinforced mechanical properties, the presence of the CNFs simultaneously enhances the ionic conductivity due to their excellent strong water-binding capacity according to computational simulations. This strategy renders the electrolyte a room-temperature ionic conductivity of 0.61 +/- 0.12 mS cm(-1) which is one of the highest among polymer gel electrolytes. The electrolyte shows superior performances as a separator for lithium iron phosphate half-cells in high specific capacity (161 mAh g(-1) at 0.1C), excellent rate capability (5C), and cycling stability (94% capacity retention after 300 cycles at 1C) at 60 degrees C, as well as stable room temperature cycling performance and considerably improved safety compared with commercial liquid electrolyte systems., Funding Agencies|Digital Cellulose Centre, a Vinnova (Swedish Innovation Agency) [2016-05193]; Knut and Alice Wallenbergs Research Foundation; Wallenberg Wood Science Centre at KTH [KAW 2018.0452-WWSC 2.0]; A-forsk foundation [19-320]

Studying protein interactions at low temperatures hasimportantimplications for optimizing cryostorage processes of biological tissue,food, and protein-based drugs. One of the major issues is relatedto the formation of ice nanocrystals, which can occur even in thepresence of cryoprotectants and can lead to protein denaturation.The presence of ice nanocrystals in protein solutions poses severalchallenges since, contrary to microscopic ice crystals, they can bedifficult to resolve and can complicate the interpretation of experimentaldata. Here, using a combination of small- and wide-angle X-ray scattering(SAXS and WAXS), we investigate the structural evolution of concentratedlysozyme solutions in a cryoprotected glycerol-water mixturefrom room temperature (T = 300 K) down to cryogenictemperatures (T = 195 K). Upon cooling, we observea transition near the melting temperature of the solution (T & AP; 245 K), which manifests both in the temperaturedependence of the scattering intensity peak position reflecting protein-proteinlength scales (SAXS) and the interatomic distances within the solvent(WAXS). Upon thermal cycling, a hysteresis is observed in the scatteringintensity, which is attributed to the formation of nanocrystallitesin the order of 10 nm. The experimental data are well described bythe two-Yukawa model, which indicates temperature-dependent changesin the short-range attraction of the protein-protein interactionpotential. Our results demonstrate that the nanocrystal growth yieldseffectively stronger protein-protein attraction and influencesthe protein pair distribution function beyond the first coordinationshell.

Tunable dynamic networks of cellulose nanofibrils (CNFs) are utilized to prepare high-performance polymer gel electrolytes. By swelling an anisotropically dewatered, but never dried, CNF gel in acidic salt solutions, a highly sparse network is constructed with a fraction of CNFs as low as 0.9%, taking advantage of the very high aspect ratio and the ultra-thin thickness of the CNFs (micrometers long and 2–4 nm thick). These CNF networks expose high interfacial areas and can accommodate massive amounts of the ionic conductive liquid polyethylene glycol-based electrolyte into strong homogeneous gel electrolytes. In addition to the reinforced mechanical properties, the presence of the CNFs simultaneously enhances the ionic conductivity due to their excellent strong water-binding capacity according to computational simulations. This strategy renders the electrolyte a room-temperature ionic conductivity of 0.61 ± 0.12 mS cm−1 which is one of the highest among polymer gel electrolytes. The electrolyte shows superior performances as a separator for lithium iron phosphate half-cells in high specific capacity (161 mAh g−1 at 0.1C), excellent rate capability (5C), and cycling stability (94% capacity retention after 300 cycles at 1C) at 60 °C, as well as stable room temperature cycling performance and considerably improved safety compared with commercial liquid electrolyte systems.

Tunable dynamic networks of cellulose nanofibrils (CNFs) are utilized to prepare high-performance polymer gel electrolytes. By swelling an anisotropically dewatered, but never dried, CNF gel in acidic salt solutions, a highly sparse network is constructed with a fraction of CNFs as low as 0.9%, taking advantage of the very high aspect ratio and the ultra-thin thickness of the CNFs (micrometers long and 2-4 nm thick). These CNF networks expose high interfacial areas and can accommodate massive amounts of the ionic conductive liquid polyethylene glycol-based electrolyte into strong homogeneous gel electrolytes. In addition to the reinforced mechanical properties, the presence of the CNFs simultaneously enhances the ionic conductivity due to their excellent strong water-binding capacity according to computational simulations. This strategy renders the electrolyte a room-temperature ionic conductivity of 0.61 +/- 0.12 mS cm(-1) which is one of the highest among polymer gel electrolytes. The electrolyte shows superior performances as a separator for lithium iron phosphate half-cells in high specific capacity (161 mAh g(-1) at 0.1C), excellent rate capability (5C), and cycling stability (94% capacity retention after 300 cycles at 1C) at 60 degrees C, as well as stable room temperature cycling performance and considerably improved safety compared with commercial liquid electrolyte systems., Funding Agencies|Digital Cellulose Centre, a Vinnova (Swedish Innovation Agency) [2016-05193]; Knut and Alice Wallenbergs Research Foundation; Wallenberg Wood Science Centre at KTH [KAW 2018.0452-WWSC 2.0]; A-forsk foundation [19-320]

Integrating CO2 capture with biomass/waste fired combined heat and power plants (CHPs) is a promising method to achieve negative emission. However, the use of versatile biomass/waste and dynamic operation of CHPs result in big fluctuations in the flue gas (FG) and heat input to CO2 capture. Dynamic modelling is essential to investigate the interactions between key process parameters in producing the dynamic response of the CO2 capture process. In order to facilitate developing robust control strategies for flexible operation in CO2 capture plants and optimizing the operation of CO2 capture plants, artificial intelligence (AI) models are superior to mechanical models due to the easy implementation into the control and optimization. This paper aims to develop an AI model, Informer, to predict the dynamic responses of MEA based CO2 capture performance from waste-fired CHP plants. Dynamic modelling was first developed in Aspen HYSYS software and validated against the reference. The operation data from the simulated CO2 capture process was then used to develop and verify Informer. The following variables were employed as inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, lean solvent flow rate, heat input to CO2 capture. It was found that Informer could predict CO2 capture rate and energy consumption with the mean absolute percentage error of 6.2% and 2.7% respectively.

Global climate change imposes significant challenges on the ecological environment and human sustainability. Industrial parks, in line with the national climate change mitigation strategy, are key targets for low-carbon revolution within the industrial sector. To predict the carbon emission of industrial parks and formulate the strategic path of emission reduction, this paper amalgamates the benefits of the “top-down” and “bottom-up” prediction methodologies, incorporating the logarithmic mean divisia index (LMDI) decomposition method and long-range energy alternatives planning (LEAP) model, and integrates the Tapio decoupling theory to predict the carbon emissions of an industrial park cluster of an economic development zone in Yancheng from 2020 to 2035 under baseline (BAS) and low-carbon scenarios (LC1, LC2, and LC3). The findings suggest that, in comparison to the BAS scenario, the carbon emissions in the LC1, LC2, and LC3 scenarios decreased by 30.4%, 38.4%, and 46.2%, respectively, with LC3 being the most suitable pathway for the park’s development. Finally, the paper explores carbon emission sources, and analyzes emission reduction potential and optimization measures of the energy structure, thus providing a reference for the formulation of emission reduction strategies for industrial parks.

The integration of CO2 capture with biomass-fired power plants has attracted much attention due to its ability to achieve negative emissions. Waste-fired combined heat and power (CHP) plants with CO2 capture, on the other hand, has received little attention, and their potential remains unclear. This study aims to identify the possible range of the amount of captured CO2 and investigate the impact of CO2 capture on the performance of waste-fired CHP plants. Since heat is the primary product of CHP plants, it is important to maintain heat production unchanged when CO2 capture is integrated. Based on this prerequisite, two operating strategies (OS) were investigated, which correspond to the upper and lower boundaries of CO2 capture: OS1 was to maximize the amount of captured CO2 while keeping the heat supplied to the district heating (DH) network unchanged; and OS2 was to maximize CO2 capture while keeping both supplied heat and generated electricity unchanged. To obtain more accurate results regarding the CO2 capture, a dynamic model developed in Aspen Hysys™ was utilized to simulate monoethanolamine (MEA) based chemical absorption for CO2 capture. By using real dynamic data from a waste-fired CHP plant, dynamic simulation results showed that the highest amount of captured CO2, which was achieved in OS1, was 401 kton/year, corresponding to a CO2 capture ratio of 82%; while the lowest amount of captured CO2, which was achieved in OS2, was 99 kton/year, corresponding to a CO2 capture ratio of 20%. For OS1, the electricity generation was substantially decreased by 61%. When determining the negative emission, the emission resulted from the share of fossil fuel in the waste needs to be excluded. For the studied CHP plant, the fossil share was around 45%. As a result, only OS1 can achieve the negative emission, which was 181 kton/year; while OS2 still led to positive emissions. Compared to the plant without CO2 capture, the carbon intensity of heat was reduced from 0.405 to

Integrating CO2 capture with biomass/waste fired combined heat and power (CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass/waste and the dynamic operation of CHP plants result in bigger fluctuations in the properties of flue gas (FG), e.g. CO2 concentration (CO2vol%) and flowrates, and the heat that can be used for CO2 capture, when comparing with coal fired power plants. To address such a challenge, dynamic modelling is essential to accurately estimate the amount of captured CO2 and optimize the operation of CO2 capture. This paper compares three dynamic approaches commonly used in literature, namely using the ideal static model (IST) and using dynamic models without control (Dw/oC) and with control (DwC), for MEA based chemical absorption CO2 capture. The performance of approaches is assessed under the variations of key factors, including the flowrate and CO2vol% of FG, and the available heat for CO2 capture. Simulation results show clear differences. For example, when the CO2vol% drops from 15.7 % to 9.7 % (about 38 %) within 4 hours, DwC gives the highest amount of captured CO2, which is 7.3 % and 22.3 % higher than IST and Dw/oC, respectively. It is also found that the time step size has a clear impact on the CO2 capture amount, especially for DwC. Based on the results, suggestions are also provided regarding the selection of dynamic modelling approaches for different purposes of simulations.

The impact of the enhanced heat transfer performance of longitudinal fins on the entire process of melting and solidification in a phase change heat storage unit is investigated through experiments and numerical simulations in this paper. The position and structure of the longitudinal fins are optimized by using the response surface method while ensuring that the total volume of the heat storage medium remains unchanged. Studies have identified that some challenging zones in melting and solidification significantly impact the heat transfer of the entire heat storage cycle. Specifically, the challenging zone in melting primarily exists in the lower part of the unit during the charing process, while the challenging zone in solidification is mainly distributed around the unit during the discharging process. Through optimization, the optimized structure (fin spacing is 22.5 mm, fin width is 6.05 mm) can reduce the charging and discharging time by 16.94 % and 45.90 %, respectively. Additionally, the round trip time is significantly reduced by 39.19 %, and the mean heat absorption rate during the melting process is enhanced by 20.28 %. Moreover, the mean heat release rate during solidification is enhanced by 80.23 %.

Tunable dynamic networks of cellulose nanofibrils (CNFs) are utilized to prepare high-performance polymer gel electrolytes. By swelling an anisotropically dewatered, but never dried, CNF gel in acidic salt solutions, a highly sparse network is constructed with a fraction of CNFs as low as 0.9%, taking advantage of the very high aspect ratio and the ultra-thin thickness of the CNFs (micrometers long and 2-4 nm thick). These CNF networks expose high interfacial areas and can accommodate massive amounts of the ionic conductive liquid polyethylene glycol-based electrolyte into strong homogeneous gel electrolytes. In addition to the reinforced mechanical properties, the presence of the CNFs simultaneously enhances the ionic conductivity due to their excellent strong water-binding capacity according to computational simulations. This strategy renders the electrolyte a room-temperature ionic conductivity of 0.61 +/- 0.12 mS cm(-1) which is one of the highest among polymer gel electrolytes. The electrolyte shows superior performances as a separator for lithium iron phosphate half-cells in high specific capacity (161 mAh g(-1) at 0.1C), excellent rate capability (5C), and cycling stability (94% capacity retention after 300 cycles at 1C) at 60 degrees C, as well as stable room temperature cycling performance and considerably improved safety compared with commercial liquid electrolyte systems., Funding Agencies|Digital Cellulose Centre, a Vinnova (Swedish Innovation Agency) [2016-05193]; Knut and Alice Wallenbergs Research Foundation; Wallenberg Wood Science Centre at KTH [KAW 2018.0452-WWSC 2.0]; A-forsk foundation [19-320]

Tunable dynamic networks of cellulose nanofibrils (CNFs) are utilized to prepare high-performance polymer gel electrolytes. By swelling an anisotropically dewatered, but never dried, CNF gel in acidic salt solutions, a highly sparse network is constructed with a fraction of CNFs as low as 0.9%, taking advantage of the very high aspect ratio and the ultra-thin thickness of the CNFs (micrometers long and 2-4 nm thick). These CNF networks expose high interfacial areas and can accommodate massive amounts of the ionic conductive liquid polyethylene glycol-based electrolyte into strong homogeneous gel electrolytes. In addition to the reinforced mechanical properties, the presence of the CNFs simultaneously enhances the ionic conductivity due to their excellent strong water-binding capacity according to computational simulations. This strategy renders the electrolyte a room-temperature ionic conductivity of 0.61 +/- 0.12 mS cm(-1) which is one of the highest among polymer gel electrolytes. The electrolyte shows superior performances as a separator for lithium iron phosphate half-cells in high specific capacity (161 mAh g(-1) at 0.1C), excellent rate capability (5C), and cycling stability (94% capacity retention after 300 cycles at 1C) at 60 degrees C, as well as stable room temperature cycling performance and considerably improved safety compared with commercial liquid electrolyte systems., Funding Agencies|Digital Cellulose Centre, a Vinnova (Swedish Innovation Agency) [2016-05193]; Knut and Alice Wallenbergs Research Foundation; Wallenberg Wood Science Centre at KTH [KAW 2018.0452-WWSC 2.0]; A-forsk foundation [19-320]

As the electric capacity of wind turbine increases, heat dissipation in pitch cabinets becomes challenging owing to the limited space and rotating conditions. To cool down the pitch cabinet more effectively and allow heat dissipation, we designed and implemented a pulsating heat pipe (PHP) in this study. We designed PHP parameters and conducted performance tests to compare the cooling performance of the fabricated PHP with that of an air-based cooling system in a 1.5 MW wind turbine. The results demonstrated steady PHP operation under rotating conditions (17.3 rpm). At a heat load of 1000 W, the evaporator outlet temperature was only 76.1 °C. However, increasing the ambient temperature adversely affected PHP operation, resulting in higher temperature and thermal resistance. The heat-pipe-based cooling system lowered the insulated gate bipolar transistor (IGBT) temperature by about 20.4 °C in relation to the air-based cooling system, while being suitable under varied conditions. Additionally, the system could successfully operate when the heat load of IGBT was 2350 W, corresponding to a 7 MW electric capacity of the wind turbine. Reducing the manufacturing cost of the heat pipe would further enhance the applicability of this system for pitch cabinet IGBT cooling, such as decreasing payback period.

The growing share of renewable energy sources drives the need for increased flexibility in the energy systems. The flexibility provision from thermal plants is limited by the boiler’s thermal inertia as a bottleneck. Advanced controllers, such as model predictive control (MPC), have been identified as potential flexibility enablers. Fuel properties are crucial input for controllers. This work investigated the feasibility of using the properties obtained online by using near infrared spectroscopy based soft sensor to further improve the control performance. The performance of the existing proportional integral (PI) controller is compared with those of 2 feed forward (FF) MPC controllers. Both FF MPCs have significant improvement compared to PI controller and the FF MPC based on the full elemental composition shows the best performance due to more complete fuel information. There is a potential for revenues improvement with advanced control up to 1050 euros for one operation day.

Due to the growing share of intermittent renewable energy sources (RES), the requirement for flexibility in the energy system is increasing to balance the generation and demand of electricity. It has been well recognized that Combined heat and power plants (CHPs) can contribute towards improved flexibility in the energy system. Thermal energy storage (TES), using hot water as working fluid, is a commonly integrated in CHPs, which allows for decoupling of heat and electricity generation. It has been verified that proper control of the operation of TES can improve the flexibility provided by CHP. The development of advanced control system relies on accurate dynamic modeling of TES. In this work, a one-dimension (1D) dynamic model for large scale TES is developed in Dymola, based on mass and energy balances. It is validated against the operational data from a real CHP plant. Results show that the model can capture the dynamic variation in the operation of the TES energy content with maximum deviations of 6.5% from the maximum value.

Brain-computer interface (BCI) technology can realize dynamic room temperature adjustment based on individual real-time thermal sensation, which can provide the basis for future intelligent buildings. However, the generalization ability of previous thermal sensation discrimination model (TSDM) is limited, which is a serious obstacle to the application. In this paper, a general TSDM was developed by using convolutional neural network (CNN), which can be well applied to new subjects. In the study, the CNN-TSDM was established and evaluated based on the offline experimental data, and then the BCI closed-loop online room temperature control experiment was carried out based on this CNN-TSDM to further verify. The offline analysis results show that the recognition performance of CNN-TSDM in new subjects is significantly higher than that of typical shallow learning algorithms, and its area under the ROC curve (AUC) value reaches 0.789. In the online experiments of the two simulated environments, BCI using the CNN-TSDM dynamically controlled the air conditioning to improve the room temperature to the comfortable level according to the subjects' thermal sensation. The subjective score of subjects decreased from 3.1 to 3.0 for the hot uncomfortable to 1.1 and 1.2 for the cool comfortable (p < 0.001, p < 0.001). Moreover, in a hotter simulated experimental environment, BCI automatically controlled the air conditioner for longer cooling to obtain a same degree of thermal comfort. The total cooling time (p < 0.05) and the single cooling time (p < 0.05) of the air conditioner were significantly increased. This further confirmed the effectiveness and robustness of the general CNN-TSDM.

For the first time, the study investigated the dynamic performances of a compressed CO2 energy storage (CCES) system based on a dynamic model, which was validated using experimental data. The dynamic round-trip efficiency (RTE) of a scaled-up CCES system in two typical operation modes was studied, including Mode 1: the basic operation mode, where the inlet pressure of the compressor or the expander is not controlled, and Mode 2: the controlled operation mode, where the capacity for compression or expansion is regulated to remain constant. Dynamic RTE varies in the range 16.7%-56.7% in various operation modes. A new key performance indicator, utilization of tank (UOT), was defined to reflect how much working gas is used in a charging or a discharging process. In general, high charging and high discharging capacities lead to low UOTs and low RTEs. Moreover, the comparison between the dynamic model and its equivalent steady-state model showed a significant difference in system performances. The RTE of the steady-state model reaches 68.5%, which is much higher than the corresponding dynamic RTE of 55.3%. © 2023

The multi-stream heat exchanger (MS-HEX) is an important equipment in CO2 capture processes, especially for CO2 condensation. By now, little attention has been paid to thoroughly investigating its performance. To provide insights for optimizing the design and operation of the MS-HEX, this paper evaluated the impacts of some key factors, such as the flow arrangement and the content of non-condensable (NC) gases, on the performances of the MS-HEX, including the exit vapor fraction, the pressure drop, the heat transfer coefficient (HTC), and the sub-cooling degree. A 3D CFD model was developed and validated. Based on simulations, it was found that the flow arrangement can clearly affect the performance of the MS-HEX, and there existed an optimal one, which can give the highest sub-cooling degree. Moreover, increasing the volume fraction of NC gases can lead to a decrease of the HTC and the sub-cooling degree; and Ar showed the biggest impact, followed by O2 and N2. For instance, compared to condensing a stream containing 5 vol % N2, condensing a stream containing 5 vol % Ar led to a lower HTC and a lower sub-cooling degree, which can be up to 27.3% and 4 K, respectively.

The capacity fading of lithium iron phosphate batteries is related to its internal temperature and the growth of solid electrolyte (SEI). It is an effective way by controlling its internal temperature to mitigate capacity fading. This paper discusses the impact of pre-cooling and resting time on capacity fading and the growth of SEI. Results showed that the battery capacity increased and the thickness of SEI decreased if the pre-cooling was employed. Compared to 25 °C of ambient temperature, the thickness of SEI under 5 °C of pre-cooling temperature decreased by 404 nm, 386 nm, and 502 nm for 2C, 3C, and 5C discharge rate, respectively. The internal temperature of battery could be better cooled and therefore capacity increased with the increase of resting time. At 15 °C of pre-cooling temperature, the capacity increased by 3.8% if the resting time increased from 600 s to 2400 s. Therefore, the pre-cooling method could effectively mitigate capacity fading. The conclusion obtained in this paper could provide guidance for battery thermal management.

Among many technical options to improve the flexibility in combined heat and power (CHP) plants, thermal energy storage (TES) has attracted the most attention with its high applicability and benefit. Previous studies normally adopted a simplified approach for modelling a TES system, which assumes the optimized charged or discharged rate of heat can always be realized within the rated capacity. However, this may yield unfeasible results as the charging and discharging rates are dependent on the dynamic status of a TES, such as the state of charge (SOC) and water temperature, and the water flowrate for charging and discharging. In order to consider the transient operation of a TES, a 1D dynamic model was developed and validated against measured data from a real CHP plant. To analyze the dynamic performance of a TES, two key performance indicators (KPI), the maximum charging/discharging rate (C/D-ratemax) and the constant maximum charging/discharging rate that can be maintained constantly for one hour (CC/CD-ratemax) were employed. By doing simulations, it has been found that the CC/CD-ratemax was lower than the C/D-ratemax for most given SOCs of the studied TES. The developed model was also used to examine the optimized operation of a TES for providing flexibility. Some unfeasible results have been identified, as the optimized hourly charging/discharging rates were constrained by the CC/CD-ratemax. Therefore, it is of great importance to integrate a detailed dynamic model when optimizing the dispatch of electricity and heat for a CHP plant.

Data-driven methods have been widely used for estimating the state of health (SOH) of lithium-ion batteries (LiBs). The aging process can be characterized by degrading features. To achieve high accuracy, a novel method combining four algorithms, i.e. the correlation coefficient, least absolute shrinkage and selection operator regression, neighborhood component analysis, and ReliefF algorithm, is proposed to select the most important features, which are derived from the measured and calculated parameters. To demonstrate the effectiveness of the proposed method, it is adopted to estimate the SOH of two types of LiBs: i.e. NCA and LFP batteries. Compared to the case using all features, using the selected features can improve the accuracy of SOH estimation by 63.5% and 71.1% for the NCA and LFP batteries, respectively. The method can also enable the use of data obtained in partial voltage ranges, based on which the minimum root mean square errors on SOH estimation are 1.2% and 1.6% for the studied NCA and LFP batteries, respectively. It demonstrates the capability for onboard applications.

The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on annotating radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method shows the potential advantage in automatic disease diagnosis with large-scale unlabeled data available., Comment: 14 pages, 7 figures

This study conducts a review of the current practices of bioeconomy education and training in the EU; as well as the associated methodologies; techniques and approaches. In recent years; considerable efforts have been made towards developing appropriate bioeconomy education and training programs in order to support a transition towards a circular bioeconomy. This review separates bioeconomy education approaches along: higher education and academic approaches, vocational education and training (VET) and practical approaches, short-term training and education approaches, and other approaches. A range of training methodologies and techniques and pedagogical approaches are identified. The main commonalities found amongst these approaches are that they are generally problem based and interdisciplinary, and combine academic and experiential. Higher education approaches are generally based on traditional lecture/campus-based formats with some experiential approaches integrated. In contrast, VET approaches often combine academic and practical learning methods while focusing on developing practical skills. A range of short-term courses and other approaches to bioeconomy education are also reviewed.

Latent heat thermal storage (LHTS) system is vital to reduce environment pollution. In the shell-and-tube heat accumulator, the position of the inner heating tube plays a vital role in the thermal storage. To analyze the effect of the inner tube position on the phase transition, a two-dimensional numerical model is developed. The structure has the minimum full melting time of 3480 s when the inner tube is 12 mm (L = 12 mm) from the center. Compared with L = 0 mm, the full melting time at L = 12 mm can be reduced by 13.4%.

Electrification poses a promising low-carbon or even zero-carbon transportation solution, serving as a strategic approach to reducing carbon emissions and promoting carbon neutrality in the transportation sector. Along the transportation electrification pathway, the goal of carbon neutrality can be further accelerated with an increasing amount of electricity being generated from renewable energies. The past decade observed the rapid development of battery technologies and deployment of electricity infrastructure worldwide, fostering transportation electrification to expand from railways to light and then heavy vehicles on roadways. In China, a massive number of electric buses have been employed and operated in dozens of metropolises. An important daily operations issue with these urban electric buses is how to coordinate their charging activities in a cost-effective manner, considering various physical, financial, institutional, and managerial constraints. This paper addresses a general charging scheduling problem for an electric bus fleet operated across multiple bus lines and charging depots and terminals, aiming at finding an optimal set of charging location and time decisions given the available charging windows. The charging windows for each bus are predetermined in terms of its layovers at depots and terminals and each of them is discretized into a number of charging slots with the same time duration. A mixed linear integer programming model with binary charging slot choice and continuous state-of-charge (SOC) variables is constructed for minimizing the total charging cost of the bus fleet subject to individual electricity consumption rates, electricity charging rates, time-based charging windows, battery SOC bounds, time-of-use (TOU) charging tariffs, and station-specific electricity load capacities. A Lagrangian relaxation framework is employed to decouple the joint charging schedule of a bus fleet into a number of independent single-bus charging schedules

Integrating power-to-heat (P2H) assets in combined heat and power plants (CHPs) is an attractive option, which can improve the flexibility in CHPs. This paper compares the potential benefits of integrating an electrical boiler (EB) and a heat pump (HP) in a CHP from providing flexibility services in both the day-ahead market and the frequency regulation market. An optimization model is developed for the operation of P2H assets and the CHP to maximize the profit. A case study is carried out using the data of a real CHP and electricity prices of Nord Pool. It is found that when an EB or a HP is integrated, the annual profit of the studied CHP from providing frequency regulation can be increased by 3.1 % (EB) or 27.7 % (HP) respectively compared to the CHP without P2H. Despite the high capital cost, a HP can increase the net present value up to 21.8 %, and achieve a payback period of 3 year, which are better than an EB (0.8 % and 5 year). Sensitivity analysis shows that prices of fuel and electricity have significant impacts on the net present value and payback period for the integration of P2H assets. Even though the increase of the fuel price decreases the NPV, it can lead to a decline in the payback period. Meanwhile, the increase of the electricity price results in a large growth in the profit and NPV, but a big reduction in payback period.

Polyethylene terephthalate (PET) plastic-waste-derived activated carbons have recently been developed and exhibit excellent CO(2 )adsorption uptake. However, the CO2-adsorption performance of such recycled materials has only been considered on a basic characterization level and has not yet been evaluated in carbon capture cycles, thereby making biased analyses inevitable. Consequently, a whole chain including the material, process, and cycle is essential for comprehensively analyzing and evaluating novel CO2 adsorbents. Therefore, in this study, various CO2-capture cycles using PET plastic-waste-derived activated carbon adsorbents were numerically simulated, the cyclic CO2-adsorption performances were evaluated, and the application scenario was optimized. A methodology for evaluating the cyclic CO2-adsorption performance of PET plastic-waste-derived activated carbon was proposed for CO(2 )capture. The results suggested that the temperature/vacuum swing adsorption cycle was superior and that its maximum exergy efficiency reached 32.90%.