The utilization of spontaneous capillary driven water evaporation to generate electricity can be considered as a green and potential approach to the energy and pollution issues in very recent years. Various attempts have been made to harvest this green energy in the previous works, which is from the interactions between water molecules and solid materials of nano-structures. However, few literatures reported the capillary driven evaporation phenomena for the device design, especially the effect of geometry was not considered. Therefore, four porous carbon black films with different geometries are inhouse fabricated and tested in present work. It is found that the generated open-circuit voltages in different geometrical films are different accordingly. Through optimizing the geometry of the film, the generated voltage is increased by up to 200% in present experiment. It should be a promising way to improve the output performance of this kind of electricity generation devices. The principle behind this evaporation-induced electricity generation and the effect of geometry on volume flow inside the porous film are then revealed. The findings in this work can be used to guide the design of green evaporation-induced electricity generators.
A good flow field design is important to the proton exchange membrane fuel cell (PEMFC) performance, especially under a high current density region, which is dominated by concentration polarization. Motivated by variable cross-section channel idea, in this study, a novel flow field containing a converging-diverging (C-D) pattern is proposed. A three-dimensional multiphase model with the novel flow field is established. The results show that it outperforms the conventional straight channel and depth-variant channel. The enhanced under land cross flow in novel flow field improve the reactant transport.
The combined systems of solid oxide fuel cell-gas turbine (SOFC-GT) can operate with high efficiency and low carbon emissions. However, air compressors power consumption of SOFC-GT is big. In this study, a novel hybrid system integrated SOFC-GT with compressed air energy storage (CAES) is proposed. For the integrated system, the energy efficiency is improved in comparison to traditional SOFC-GT due to no air compressors power consumption in the discharging process. In addition, Rankine steam cycle (RSC) is applied to recover waste heat of SOFC-GT exhaust. The new system is simulated in Aspen plus software. The energy and exergy analysis is investigated. Then, the sensitivity analysis is also studied. The results show the cycle efficiency, electrical cycle efficiency and exergy cycle efficiency of new energy storage can reach 75.98%, 60.49% and 60.79%, respectively. Besides, SOFC has the largest exergy destruction of all components. In conclusion, the new system can provide theoretical guidance for efficient energy utilization
Addressing the growing need for sustainability, novel concrete solutions become increasingly popular for mitigating the negative environmental impacts found in cement production, such as high CO2 emissions output and raw materials overuse, providing conventional concrete products alternatives. The industry is lacking a common analytical framework for business models to clearly define sustainable concrete value streams present across economic, environmental, and social layers. Our research utilises the Triple-Layer Business Model Canvas (TL-BMC) to analyse a piloted sustainable concrete product (CIRCLE), describes its multi-layered value, and effectively provides the common framework for sustainable concrete business model adaptation. We conclude that the Triple-Layered Business Model Canvas (TL-BMC) is the most appropriate framework that enables the identification and establishment of successful business models focused on sustainable concrete.
Efficient maintenance management of devices is fundamental to ensuring supply reliability of natural gas pipeline system. A joint optimization model of preventive maintenance and spare parts ordering for the gas compressor in pipeline system based on supply reliability is developed aiming to maximize system gas supply reliability and minimize maintenance costs. The model consists of three parts: calculation of the maximum gas supply capacity, modeling of the joint optimization problem and using a genetic algorithm to find the optimal solution. The effectiveness of the proposed method is validated on a European gas pipeline network. The results show that the proposed joint optimization strategy outperforms others in identifying optimal maintenance strategies.
Silicon/graphite composite electrodes are promising because of their high capacities, and much research has been conducted to speed up the commercialization of lithium-ion batteries with silicon/graphite electrodes. However, most of the research focuses on electrochemical and mechanical behaviors of the composite electrodes, and thermal behavior analysis of silicon/graphite electrodes is scarce. It is necessary to study the thermal behavior because it hugely affects the performance and safe operation of lithium-ion batteries. This study for the first time develops an electrochemical-thermal model for silicon/graphite electrodes based on a multi-material framework, which can separate the electrochemical and thermal behaviors of each electrode material. Using the model, the thermal characteristics of silicon/graphite electrodes are investigated. The research reveals the relationship between heat, characteristics of active materials, and their content in the composite electrode. At the same C-rate, an electrode with a higher silicon content experiences a higher temperature rise. Thermal peaks representing the phase transition processes of graphite are observed during (de)lithiation, which can be potentially used to detect the aging of silicon-based batteries in service. We further analyze the contributions of different heat sources. The heat generation of graphite converges on the beginning stage of delithiation followed by huge heat generation from silicon. In contrast, the two active materials are lithiated simultaneously, and graphite plays a dominant role during its phase transition processes. For a composite electrode with a mass ratio of silicon to graphite of 0.2 at a moderate C rate (2C), ohmic heat generation is the major contributor to heat generation accounting for 41% of the total heat generation, followed by reversible (36.1%) and irreversible (22.9%) heat generation. This model paves the way for experimental work regarding the thermal characteristics of silicon/graphite composite electrodes and can be potentially used for the thermal analysis of large-format batteries with silicon/graphite electrodes in the electrical vehicle industry.
To attain a zero-carbon emissions energy system, we need dispatchable and flexible power production means that can respond to the variability of wind and solar. Hydrogen-fired gas turbines could have a place to fit this demand, but research and development are needed to assess the impact of the fuel change on the systems. This study focuses on a cogeneration unit composed of an aero-derivative industrial gas turbine within a range of 25-35MWe and an HRSG to generate steam for heat and industrial purposes. Setting aside existing challenges for hydrogen-fired gas turbines, we have found that hydrogen has a slightly positive impact on the thermodynamic performance of the considered system. The gas turbine cycle’s efficiency and net output power increase by about 2.69% and 4.84% respectively with the H2 ratio, while the heat utilization factor for the whole system does not show significant improvements.
The increasing share of renewable energy systems (RES) at the European scale enables the shift from a centralized to a decentralized power system with small units located close to consumption sites. Decentralized power systems enhance social acceptance, however it requires a deep change of the current grid. This study explores with the model eTIMES-EU the feasibility conditions, barriers and benefits of this change with a land use perspective.
Applying post-combustion Carbon Capture (CC) offers a solution to reduce CO2 emissions of micro Gas Turbines (mGTs). However, the conventional monoethanolamine-based (MEA) absorption-regeneration process was never found economically feasible for these small scales (5-500 kWe) due to the high energy consumption of this CC process, degrading the plant performance. Improving the CC configuration can help to reduce the CC energy penalty. To this end, two advanced configurations are investigated, the Rich Solvent Recycle (RSR) and the Rich Solvent Split (RSS) configurations, and applied to a typical mGT, namely the Turbec T100, through thermodynamic cycle modeling in Aspen Plus. The optimal operating parameters were first determined for each configuration to minimize the energy consumption. The results showed that by applying RSR and RSS, the CC reboiler duty could be reduced by 2% and 0.9%, respectively, compared to the conventional one. Moreover, the increase in mGT electrical efficiency is limited to 0.04% for both processes, demonstrating that such configurations do not provide significant benefits for mGT applications.
The global horizontal irradiance (GHI), direct normal irradiance (DNI), temperature and other meteorological data are generally used for the photovoltaic (PV) power forecasting. Due to the multi-layered and complex factors between irradiance and PV power generation, a large amount of long-term operation data is required to train the model to achieve a high prediction accuracy. In order to reduce the data requirements, a coupled model based on solar irradiance and BP Neural Network is proposed in this paper. Firstly, the difference between the received irradiance and the GHI/DNI is clearly demonstrated. Moreover, the received irradiance of fixed photovoltaic panel is calculated. On this basis, according to the geographical location of the photovoltaic power plant, the received irradiance in a whole year is modelled and used as the input to train BP neural network. The prediction results show that, compared with the conventional prediction methods, the coupled model has a higher accuracy, which can reduce the mean squared error and root mean squared error by about 34% and 16%.