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Abstract The rapid increase in energy demand and the limited resources of fossil fuel as well as the environmentally damaging effects, drive the world to find new options for sustainable electricity generation, which is represented by renewable energies. Concentrating solar power (CSP) is one of the most promising technologies in the field of electricity generation to tackle this issue with a competitive cost in the future. This paper presents an investigation of the potential of implementation of CSP plants in Libya. The socio-economic context, current energy situation of the country and different types of CSP plants are discussed. Moreover, an assessment of site parameters required for CSP plants including solar resources, land use and topography, water resources and grid connections are investigated in detail. In addition, thermo-economic simulation of a 50 MW parabolic trough power plant is performed. The simulation is conducted based on meteorological data measured by the weather station installed at the Centre for Solar Energy Research and Studies (CSERS) in Tajoura city. The performance results are compared with the reference plant Andasol-1 in Spain. Even though the proposed plant is located on the North coast where solar resources are at their minimum compared with other regions of the country, the outcome of the study proves that Libya is not only suitable but it can be economically competitive in the implementation of CSP technology.

In this paper, a three-rotor turboprop engine (with a fixed-speed free turbine) is set as the studied object. Under the certain flight conditions and the transient state control law, the main influencing factors affecting the position of low-pressure rotor transient state line are studied. They are the ratio of MIL (moment of inertia of low pressure rotor) to MIH (moment of inertia of high pressure rotor), the ratio of HPC (high pressure compressor) pressure ratio to LPC (low pressure compressor) pressure ratio and the ratio of design point HPR (high pressure rotor) speed to design point LPR (low pressure rotor) speed. The influence of these three factors on the position of low-pressure rotor transient state line is obtained and the mechanism is analyzed.

The rapid fabrication of starch gels with excellent mechanical properties still presents a challenge. This study aimed to develop a simple alcohol soaking method to fabricate strong starch gels in a short time. The mechanical properties, crystallization behaviors, and structural characteristics of starch gels were investigated by rheological testing, texture profile analysis, differential scanning calorimetry (DSC), X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and low-field nuclear magnetic resonance (LF-NMR). The mechanical strength of the starch gel increased from 170.21 g to 666.16 g. The gelation time of starch gel decreased from 12 h (traditional retrogradation) to 2 h. DSC indicated that the enthalpy of starch gel increased from 0.88 J/g to 2.90 J/g. XRD analysis showed that the crystallinity of starch gel increased from 3.8% to 8.9% after alcohol soaking. Alcohol dehydrated the starch gels system, so that amylopectins were arranged to form more crystals. This new simple approach, alcohol soaking to form strong starch gels, can expand the applications of starch in food, agriculture, and textiles.

Abstract Accurate estimation of the remaining useful life of lithium-ion batteries is critically important for electronic devices. In the existing literature, the widely applied model-based approaches for remaining useful battery life estimation are limited by the complexity of the electrochemical modeling required. In addition, data-driven approaches for remaining useful battery life estimation commonly define unreliable sliding window sizes empirically and the prediction accuracy of these approaches needs to be improved. To address the above issues, use of a hybrid neural network with the false nearest neighbors method is proposed in this paper. First, the false nearest neighbors method is used to calculate the sliding window size required for prediction. Second, a hybrid neural network that combines the advantages of a convolutional neural network with those of long short-term memory is designed for model training and prediction. Remaining useful life prediction experiments for batteries with various rated capacities are performed to verify the effectiveness of the proposed approach, and the results demonstrate that the proposed approach offers wide generality and reduced errors when compared with the other state-of-the-art methods.

In this study, the hybrid power system of the diesel, as the main power source, and new energy, as the auxiliary energy is developed. An optimal energy management strategy was proposed for the hybrid ship, aiming to minimize consumption of the fuel. The model of the hybrid power system was established in the MATLAB/SIMULINK simulation environment. The optimal energy management strategy was verified by simulation according to the typical drive cycle of ship compared to original energy management strategy. The simulation results show that the proposed optimization can improve fuel economy while the state of charge of the battery is maintained at reasonable range.