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In order to reduce the consumption of a large amount of externally supplied hydrogen, methanol was used as the hydrogen donor solvent. Phenol, a model compound of coconut clothes pyrolysis oil, was...

An environmentally acidic ionic liquid was prepared with 1-methyl-imidazole, 1,3-propyl sultone and H2SO4, and successfully loaded B acidic ionic liquid into three zirconium-based MOFs to synthesize the catalyst used to prepare biodiesel from Jatropha oil by impregnation method. The structure of the catalyst was characterized by XRD, SEM, FTIR and BET. Compare the activities of the three catalysts (through PSH/UiO-66, PSH/UiO-66NO2, PSH/UiO-66-NH2) experiments, the catalytic activity of PSH/UiO-66-NO2 was found to be the highest, and the conversion rates of Jatropha oil was 96.69%. The effect of methanol oil mole ratio, catalyst dosage, reaction temperature, and reaction time on the conversion of Jatropha oil to biodiesel was investigated, an orthogonal test determined the optimum reaction conditions of using PSH/UiO-66-NO2 catalyst. The optimal reaction conditions were obtained: reaction temperature was 343 K, catalyst dosage to oil was 4 wt%, methanol to oil ratio was 25:1, reaction time was 4 h, and the average conversion rates of Jatropha oil was 97.57%, the methyl ester rate was over 99.98%. The results showed that the four reaction factors had significant effects on the Jatropha oil conversion rates: reaction time > catalyst dosage > reaction temperature > methanol to oil ratio.

Traditional ground-penetrating radar (GPR) data inversion leverages iterative algorithms which suffer from high computation costs and low accuracy when applied to complex subsurface scenarios. Existing deep learning-based methods focus on the ideal homogeneous subsurface environments and ignore the interference due to clutters and noise in real-world heterogeneous environments. To address these issues, a two-stage deep neural network (DNN), called DMRF-UNet, is proposed to reconstruct the permittivity distributions of subsurface objects from GPR B-scans under heterogeneous soil conditions. In the first stage, a U-shape DNN with multi-receptive-field convolutions (MRF-UNet1) is built to remove the clutters due to inhomogeneity of the heterogeneous soil. Then the denoised B-scan from the MRF-UNet1 is combined with the noisy B-scan to be inputted to the DNN in the second stage (MRF-UNet2). The MRF-UNet2 learns the inverse mapping relationship and reconstructs the permittivity distribution of subsurface objects. To avoid information loss, an end-to-end training method combining the loss functions of two stages is introduced. A wide range of subsurface heterogeneous scenarios and B-scans are generated to evaluate the inversion performance. The test results in the numerical experiment and the real measurement show that the proposed network reconstructs the permittivities, shapes, sizes, and locations of subsurface objects with high accuracy. The comparison with existing methods demonstrates the superiority of the proposed methodology for the inversion under heterogeneous soil conditions.

Threshold voltage (V TH ) shift caused by bias temperature instability (BTI) has become the most serious reliability problems in silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET). BTI-induced V TH shift is divided into recoverable shift and permanent shift. The traditional V TH shift measurement method for silicon (Si) devices is difficult to monitor the recoverable shift of SiC MOSFET. To solve this problem, firstly, based on the physical structure and working principle of SiC MOSFET, this paper establishes an analytical model of forward voltage of body diode (V SD ) under body effect based on the reverse conduction resistance (R SD-ON ). Secondly, the influence mechanism of BTI on V SD is studied. Finally, a method is proposed to monitor V TH of SiC MOSFET utilizing the forward voltage of body diode under body effect at small current. The experimental results prove that this method can real-time extract V TH , and the monitoring method is simple.

Magnetic localization provides a feasible way to estimate the position and orientation of the wireless capsule endoscopy embedded with a permanent magnet. However, the magnetic localization suffers from the drifting effect from magnetic noises and the uncertain initial guess during the optimization. In this paper, a localization system with noise cancellation is proposed to reduce the drifting effect of the geomagnetic noise and obtain the initial guess in a simple and effective way. Compared to the prior arts, the proposed system is able to achieve a lower positioning and orientation error under the same system configuration and makes it possible for the patient to move around while wearing the localization system. Besides, the proposed algorithm improves the consistency of positioning error within the localization region. Within a volume of 380 mm by 270 mm by 240 mm covered by 16 digital magnetic sensors, the average positioning error obtained from the proposed algorithm is around 10 mm and the average orientation error is around 12°.

A novel IL/Zr-SBA-15 (n(Si)/n(Zr)= 2,10,40) catalysts were synthesized, and characterized by XRD, FTIR, SEM, TEM, N2 adsorption–desorption measurement and DSC/TG. The results showed that ionic liquid (IL) has been successfully immobilized on the surface of Zr-SBA-15, and the ordered mesoporous structure can also be well preserved, even after chemical grafting reaction. Using water as a solvent, Raney Ni and IL/Zr-SBA-15 composite catalysts were investigated by hydrodeoxygenation (HDO) of phenol. The synergy of acid and metal catalysts can effectively catalyze phenol as a model compound for pyrolysis oil by tandem reactions. Under optimum reaction condition, the mean selectivity and yield of cyclohexane was 81.08% and 76.26%, respectively. The catalyst still provided satisfactory results after 5 runs. With reference to the study of phenol, HDO was carried out on the coconut clothes pyrolysis oil. The process conditions were optimized by response surface methodology. In addition, it is concluded that reaction conditions affect the order of naphthenes component content as reaction temperature>reaction time>hydrogen pressure. As a result, the content of naphthenes components in the bio-fuel reaches 34.43% and the HHV of 37.58 MJ/kg, indicating that this catalyst also has a good effect on HDO of the pyrolysis oil.

In this paper, an electric vehicle insulation detection method with single side switching fixed resistance is designed, and the hardware and software design of the system are given. The experiment proves that the insulation detection system can detect the insulation resistance in a wide range of resistance values, and accurately report the fault level. This system can effectively monitor the insulation fault between the car body and the high voltage line and avoid the passengers from being injured.

Through the pulse hammer excitation test, the influence of rail vibration characteristics for fastener loose was researched, for five different buckle pressure conditions the vibration modes of rail were tested. Experimental analysis shows that: in less than 2000Hz frequency, rail vibration response is decreasing with the increase of tighten twist torque of fastener. In 2000-3000Hz frequency, rail vibration response is increasing with the increase of tighten twist torque of fastener. When fastener tighten torque less than 80N ∙ m, vibration of the rail head and base slope under the rail increase suddenly. So from the perspective of rail vibration, we suggested that the fastener tighten twist torque should be more than 80N ∙ m in construction and maintenance. With the increases of fastener tighten twist torque, the order frequency of rail increase.