In this study, Prussian blue analogue (PBA) with high content of lattice water is synthesized by a simple aqueous phase method and introduced into the sulfonated polysulfone (SPSf) polymer matrix, to prepare composite proton exchange membranes (PEMs) by solution casting method. The successful synthesis of PBA and the features of lattice water are comprehensively studied via X-ray diffraction (XRD), as well as several other characterization, reflecting considerable potential to promote Grotthuss type proton transport. In terms of membrane structure, XRD tests show that the PBA could be well dispersed in the SPSf matrix with the filler loading of 0-1.5 wt%. Mechanical strength, swelling ratio and water uptake were reduced in SPSf/PBA composite membranes with increased filler loading, within the appropriate range for PEM usage. The proton conductivity of SPSf/PBA composite membranes show evident improvements over pristine SPSf membrane, especially when relative humidity (RH) decreases. This result shows that the SPSf/PBA composite membranes are promising for use at low RH conditions, owing to the PBA filler containing abundant lattice water which helps facilitate Grotthuss type proton transport.
With the intensification of energy crisis and environmental crisis, countries have accelerated the development of new energy sources. Lithium-ion energy systems occupy an important position in the energy storage market because of their excellent performance, but temperature-related issues still hinder their further development. In order to solve this problem, researchers are committed to more accurate prediction of the temperature of lithium-ion energy system. Long and short-term memory network (LSTM) has always been considered to be able to process time series well. The emerging temporal convolution network (TCN), as a special convolutional network, has also been proven to be able to handle sequential tasks well. In this paper, a new allied temporal convolution-recurrent diagnosis network (TCRDN) is constructed by combining LSTM and TCN using an adaptive boosting algorithm. The proposed model is experimentally demonstrated to be able to predict the change of surface temperature of lithium-ion energy system more accurately.
With the ability of vertical take-off and landing, the task path of an air-ground vehicle will be significantly shortened. Accordingly, the energy consumption will be greatly reduced. Through reasonable planning of the path, such vehicle can meet the high-efficiency needs of unmanned tasks and alleviate the global energy shortage problem. To design an optimal feasible path, this paper proposes a smooth path planning learning strategy considering mode switching. A new reward function of the Q-learning algorithm is presented, considering the influence of flight obstacle crossing parameters. To avoid the redundant flight distance and energy consumption caused by frequent high flights, the flight height correction is made in the update rule. Besides that, a path smoothing modification, called double yaw correction, reduces turning points and improves the path smoothness. It further reduces the energy consumption caused by the tortuous path. This modification also points out the direction of iterative learning and accelerates the algorithm convergence speed. Finally, the proposed strategy is verified on a 40m*40m map with 0-10m obstacle height. Results show that, the proposed strategy is effective to shorten 4.08m distance and plays the role of smoothing the path. Its convergence speed is faster than the traditional algorithm.
The solid oxide fuel cell-gas turbine (SOFC-GT) hybrid systems are thought to be the most attractive energy conversion systems in future energy markets because of high efficiency 60~70%, low emission, and fuel flexibility. Which are vary suitable for mobile applications due to they could be installed in a standalone manner to supply power with a high electric efficiency. In this context, this paper designed a LNG-fueled SOFC-GT hybrid system for all-electric ship application. And proposed the control structure of power feedback and temperature feedforward for the ship used SOFC-GT system. Also, a 5000t class of river-to-sea cargo ship which is sailed from Nanjing to Shanghai along the course of the Yangtze River is conducted as a case study. The results show that the total amount of 2724kg fuel is consumed by the ship in the whole course, meanwhile, the CO2 emissions is reduced by almost 48.8% compares to the conventional propulsion technology.
The availability of solar irradiance is uncertain and time-dependent, which is influenced by several climatic factors. Therefore accurate solar irradiance prediction is required for planning, designing, and site selection to establish new solar power plants. This study utilizes eight machine learning techniques, including multivariate linear regression, ridge, lasso, elastic net, multilayer perceptron, k-nearest neighbors, decision tree, and random forest to develop hourly global horizontal irradiance prediction models. A feature selection procedure to select the most influential input features from different meteorological variables is also discussed in this study. To examine the accuracy of the developed models, this study employs the data of 21 locations of different climatic and geographic regions. The considered cities are categorized into three groups using k-means clustering in order to find out the favorable locations for solar power generation. The computational results suggest that the random forest and k-nearest neighbor are the most efficient prediction model, which outperformed other machine learning models with an average forecast skill of 37% and 35% over the smart persistence model. Overall, this study may be exercised for the selection of an efficient GHI prediction model and location for the installation, designing and planning of new solar power plants.
In this paper, a cogeneration system coupled with low concentrating photovoltaic/thermal (LCPV/T) technology and heat pump is proposed to meet the energy consumption of villa. The system is mainly composed of low concentrating photovoltaic/thermal module (LCPV/T), water source heat pump and capillary network radiation ceiling. LCPV/T generates clean electric energy and thermal energy by absorbing solar radiation, the system can achieve self-sufficiency in electricity / heat and achieve the goal of zero carbon building with appropriate operation mode. Taking the real weather data of Tianjin as the input parameters, the system performance is simulated by using the simulation software TRNSYS. The results show that the electric energy and heat energy produced by the system can fully meet the annual power consumption and the heat load in the heating season of the villa. LCPV/T system can also improve the cop of heat pump and save energy. Overall, the comprehensive utilization efficiency of solar energy of the system can reach 75%, including electric efficiency of 12% and thermal efficiency of 65%.
The curvature and bending direction of a sinusoidal channel show periodic changes, so its internal flow and heat transfer characteristics are more complex. In this paper, Direct Numerical Simulation method is used to calculate the square sinusoidal channels with different cross-sectional ratios. The results show that in the laminar flow range, the Dean vortex loses stability when the Reynolds number increases to a certain value, which is manifested as random fluctuations of the Dean vortex with time and space. This state is called Dean instability phenomenon, and the critical Reynolds number corresponding to 1:2 cross-sectional ratio of the sinusoidal channel is determined by using the helicity discrimination method. In addition, by comparing the heat transfer characteristics of the channels in the two states, it is found that after reaching the Dean instability state, the heat transfer enhancement caused by the Dean vortex is not limited to a fixed position, which has significant implications for enhancing the heat transfer capacity and reducing the wall temperature difference.
The introduction of CO2 emissions peaking and neutrality targets bring huge challenges to China’s petroleum and petrochemical industry. However, there is not enough research on the emission characteristics, mitigation technologies assessment and optimization of this industry. This paper constructs an integrated assessment and optimization model for mitigation technologies, which includes the following three parts. Firstly, the model analyzes the emission characteristics based on enterprises’ emissions data. Secondly, the model selects key mitigation technologies and conducts the integrated evaluation based on the emission characteristics. Thirdly, the combination of mitigation technologies for typical enterprises have been optimized. The results show that it is difficult to widely apply emerging mitigation technologies in petroleum and petrochemical industry at present due to safety, cost and other factors; energy saving will still be an important measure for short-term emission reduction. In the long term, the adjustment of energy structure will play an increasingly important role. In addition, the emission composition of different enterprises varies greatly and they should apply different mitigation strategies.
Waste heat is inevitable in any human endeavour. Thus, the need to develop thermal energy conversion systems. Thermoelectric generators (TEGs) are solid-state devices that convert waste heat to useful electricity. They have found various applications in converting solar energy to electricity, harvesting exhaust waste heat in automobiles and power plants and providing power for spacecrafts by converting the heat released during radioactive decay to electricity. Despite these perks, they are characterised by very low efficiencies. Thus, several efficiency enhancement strategies such as material modification and leg geometry alteration have been introduced. Pertaining the latter, the trapezoidal shaped geometry has been studied extensively. Although it offers a higher efficiency compared to the conventional rectangular leg geometry, it still exhibits higher thermal stresses and consequently, a reduced lifespan. A conical frustum shaped TE pin has not been conceived yet. The investigation of this leg geometry is important since it might provide a higher efficiency and operating lifetime compared to the current trapezoidal leg. Thus, a thoroughly validated numerical model is used in evaluating the performance of three TEGs comprising rectangular, trapezoidal and conical frustum shaped TE legs. Results indicate that the proposed conical frustum leg TEG enhances the power density and exergy efficiency of the trapezoidal device by 20% and 23%, respectively. Also, the thermal stress and thermodynamic irreversibilities of the trapezoidal leg TEG are reduced by 2% and 0.5%, respectively.
A study on the synergistic effects of high boost pressure and injection strategies on diesel engine performance were investigated. The highest gross indicated thermal efficiencies (ITEg) are at the intake pressure of 0.3 MPa. The soot and NOx emissions increase with increasing intake pressure. But once intake pressure which reaches 0.2 MPa has less effect on the diesel engine performance. Overall, intake pressure has larger effects on ITEg and emissions than injection pressure; intake pressure also has larger effects on ITEg than start of injection (SOI), but SOI has larger effects on emissions than intake pressure.