A sustainable humidity pump is proposed to manage the indoor humidity levels by directly removing the water vapor from the room air instead of supplying a dried cool air as in the conventional systems. The humidity pump uses liquid desiccant driven by a thermosyphon loop. The system is sized following a systematic sizing approach applicable to any outdoor conditions. The proposed system is simulated for different heat inputs to determine the optimal operating conditions. It was found that the system performance was affected by the outdoor RH levels, where the optimal COP decreased with the increase in the outdoor RH: a maximum COP of 1.54 was reached at low RH (35 °C, 30 %). Furthermore, a higher heat input was needed to operate the system at higher outdoor RH for the same latent load removed.
With the rapid development of big data, the scale of data center is becoming larger and larger. The heat generated by the servers in the data center is also increasing, which puts forward higher requirements for the cooling of the data center, which should have strong enough heat dissipation capacity and low enough energy consumption. This paper is committed to reducing the cooling energy consumption of the data center and reducing the PUE of the system. This paper studies a server cabinet with a power of 4.8KW. The fins indirect water-cooled radiator is used to cool the chip, and the cooling tower cold source cycle is constructed outside. In order to facilitate the research, TRNSYS software is used to model the system, and the internal modules of TRNSYS software are selected to build the system.Through TRNSYS simulation and thermodynamic calculation, the relationship between the chip temperature and the server inlet water temperature and secondary water flow is studied. The system working conditions from March to October in Tianjin are studied which all change with the change of ambient temperature and relative humidity. The results shows that the optimal cabinet inlet water temperature, primary water flow, secondary water flow, system power and PUE of the system will change accordingly with the change of ambient temperature and relative humidity. In Tianjin in each month, the range of PUE is 1.032-1.091, and the variation range of system power is 0.154 kW to 0.437kW when run at the optimal conditions.
Early warning is an important and challenging issue in governmental policy-making. This study proposes a skillful spillover network-based machine learning model to provide early warnings of critical transition in energy and stock markets. First, the critical transition of stock and energy time series can be detected using a hidden Markov model. Second, a dynamic spillover network is established, which can help to understand the characteristics of return volatility from the perspective of the time-varying structure of spillover relationships. A machine learning algorithm is employed to model the early warning of critical transition based on the topological structures of the network. The results demonstrated that the proposed model can identify the early warning of critical transition with the warning day, e.g., one day or thirty days, with a high generalizationability. Our study enriches critical transition research and can offer important warning signals for policy-makers and market investors.
This study proposed a novel coupling hydrogen liquefaction – multi-energy liquid air energy storage (M-LAES) system, aiming to reduce the energy consumption of hydrogen liquefaction while realizing the cascade utilization of cold energy in M-LAES. In the proposed coupling system, the M- LAES characterizes the delivery of the cold capacity by methanol and propane, pre-cooling hydrogen to 100 K in hydrogen liquefaction instead of conventional liquid nitrogen. A transient thermodynamic model is built to investigate the operating characteristics of the proposed system. Considering the specific energy consumption (SEC) as the objective function, the optimum flow rate and thermodynamic parameters can be determined. Compared with traditional hydrogen liquefier, the proposed system shows better performance for its lower SEC and higher exergy efficiency, about 8.745 and 32.18%, respectively. The exergy analysis shows the coupling system increases the energy efficiency of both M-LAES and hydrogen liquefaction. The proposed system outperformed the conventional LAES operation on flexibility. Energy input into M-LAES can be transformed into liquid hydrogen, instead of only electricity, opening up further possibilities for fuel cells, long-distance transport, and future clean energy management net options.
Africa’s power system faces many challenges: Half of all Africans are without access to electricity, the infrastructure needs investments to strengthen its reliability and lessen the frequent electricity disruptions, adaptation of the system for one of the world’s highest power consumption growth rates and an urge to transition the currently highly fossil-fuel dependent system to renewable energies. Based on a detailed representation of the power system, incorporating the unique technical and economic characteristics of the electric power sector, we model a transition to renewable energies in Africa. An addition of 180.2 GW of wind power and 42.1 GW of PV is necessary to cover the current demand in the modeled countries purely by renewable energy sources.
A real-time emulation of a modular multilevel converter with integrated energy storage devices has been carried out. The real-time model is detailed and implemented using MicroLabBox/dSPACE. The system is tested and compared to an experimental prototype of the converter. The main advantage of the proposed real-time system is that it gives higher fidelity for further investigations, specifically in electric vehicle applications where it can be integrated into a real-time emulated electric vehicle. The model can be extended to a higher number of levels as it has no constraints on the number of switches or batteries/cells. Also, it can be integrated in a power hardware in the loop system to decrease the testing time of a product. This is a novel proposal of a real-time emulation of the converter in an electric vehicle application using MicroLabBox/dSPACE.
Low carbon liquid fuels are needed for maritime shipping and long-haul trucking which are difficult to decarbonize by use of battery energy storage or hydrogen. Thermochemical conversion of biomass to liquid fuel (BTL) is a promising option to produce carbon-neutral liquid fuel. However, no commercial BTL plants are yet operating and one of the main reasons is the cost of produced fuel. Here we propose two processes in order to improve the economic appeal of BTL process for producing methanol. These processes employ natural gas as a swing fuel and utilize the synergy between natural gas reforming and biomass gasification. Through this integration, we can use the synergistic effects of adding H2-rich syngas (H2/CO mixture) from natural gas to carbon-rich syngas from biomass to produce the right H2/CO ratio for methanol synthesis while maintaining a high carbon utilization. The biomass syngas generation step in both designs is the same and utilize the illustrative example of an entrained flow gasifier (EFG) with subsequent cleaning of the generated syngas to remove H2S, dust, soot, etc. The differentiating feature of these processes is the syngas generation step from natural gas. In the first design, an autothermal reformer (ATR) is used to generate syngas, while the O2 required for both biomass gasification and natural gas reforming is provided by a solid oxide electrolysis cell (SOEC). The H2 stream from the SOEC is used to adjust the stoichiometry of the methanol synthesis reactor. In the second design, natural gas is sent to a gas-heated-reformer (GHR) followed by an ATR. The heat required in the GHR is provided by the exhaust stream from the ATR, which is the best method to utilize the high temperature exergy of the exhaust stream. The reformed gas has high hydrogen content, but not enough to have the correct stoichiometric number prior to the methanol synthesis. Therefore, a fraction of the reformed gas is sent to a water gas shift (WGS) reactor followed by a CO2 capture unit. The produced stream is used to adjust the stoichiometric number prior to the methanol synthesis reactor. The flexibility and economics of the two processes are compared to a stand-alone BTL process. While the produced methanol includes some fossil carbon, the synergy of this integration and added flexibility would increase the economic viability of deployment of biomass-based fuel production.
Gas-liquid thermoacoustic engine, using gas as working fluid and liquid as phase-matching element, can operate at a lower heat source temperature than the gas-only thermoacoustic engine, which is attractive for low-grade heat recovery. Rayleigh-Taylor instability, which is induced when a low-density fluid accelerates a high-density fluid, can occur in gas-liquid thermoacoustic engine. In this work, cylindrical or spherical floats with different dimensions were employed to suppress the Rayleigh-Taylor instability in a gas-liquid standing-wave thermoacoustic engine. Comparison between the onset and damping temperature differences obtained from the conditions with or without float was conducted to analyze the effects of instability on the onset and damping processes. The experimental results show that the dimension of float has marked effects on the onset and damping temperature differences, and there exists an optimal dimension for both cylindrical and spherical floats to achieve the lowest onset and damping temperature differences. After installing the float, the maximum decreases in the onset and damping temperature differences are 19.0% and 21.8%, respectively. This work demonstrates that the suppression of Rayleigh- Taylor instability by the suitably sized float can reduce the onset and damping temperature differences of a gas-liquid standing-wave thermoacoustic engine for low-grade heat recovery.
A personalized stationery air treatment unit is proposed in this work to provide acceptable breathable air quality and adequate thermal comfort in in poorly ventilated spaces. The system consists of a coaxial personalized ventilation system integrated with an antibacterial filter and a metal organic framework-coated thermoelectric cooling unit. Validated mathematical models were developed to minimize the system size while maintaining thermally comfortable conditions. The system was simulated to determine its operative conditions and its required energy consumption. The supplied air conditions at the user’s breathing zone were met by the system ensuring acceptable air quality and thermal comfort levels. The system needed a total of 60 g of Nb-OFFIVE-1-Ni adsorbent with 130 W of electrical energy to properly operate the cooling unit and the fans.
This paper proposes an intelligent battery health-aware energy management strategy (EMS) for the hybrid electric bus (HEB) with a deep reinforcement learning (DRL) method. Firstly, an EMS based on twin delayed deep deterministic policy gradient (TD3) algorithm considering battery health is innovatively designed to minimize the total operating cost of the HEB. Secondly, the superiority of the proposed EMS over the state-of-the-art deep deterministic policy gradient (DDPG) based strategy is validated. Simulation results show that the proposed EMS accelerates the convergence by 24.00% and reduces the total operating cost by 9.58% compared with the EMS based on DDPG.