Rapid Synthesis of Piezoelectric ZnO-Nanostructures for Micro Power-Generators

In this paper, we report a rapid synthesis of piezoelectric ZnO-nanostructures and fabrication of the nanostructures- based power-generators demonstrating an energy conversion from an environmental mechanical/ultrasonic energy to an electrical energy. The ZnO nanostructures are grown on a silicon wafer by a modified chemical solution method （CSD, chemical-solution-deposition） with a two-step thermal-oxidation approach. The synthesis process can be completed within 1 h. By varying the mixture-ratio of Zn micro-particles in an oxalic acid solution with 0.75 mol/l concentration in the CSD process, the growth mechanism is well-controlled to synthesize three different types of ZnO-nanostructures （i.e., dandelion-like nanostructures, columnar nanostructures, and nanowires）. Furthermore, through oxidizing at different temperatures in the thermal-oxidation process, the featured geometry of the nanostructures （e.g., the length and diameter of a nanowire） is modified. The geometry, size, morphology, crystallization, and material phase of the modified nanostructures are characterized by scanning electron microscopy and X-ray diffraction. Finally, the nanostructures are used to fabricate several micro power-generators. Through the piezoelectric effect, a maximum current density output of 0.28 μA cm-2 generated by a power-generator under an ultrasonic wave is observed.

Fault diagnosis of a marine power-generation diesel engine based on the Gramian angular field and a convolutional neural network

Marine power-generation diesel engines operate in harsh environments.Their vibration signals are highly complex and the feature information exhibits a non-linear distribution.It is difficult to extract effective feature information from the network model,resulting in low fault-diagnosis accuracy.To address this problem,we propose a fault-diagnosis method that combines the Gramian angular field(GAF)with a convolutional neural network(CNN).Firstly,the vibration signals are transformed into 2D images by taking advantage of the GAF,which preserves the temporal correlation.The raw signals can be mapped to 2D image features such as texture and color.To integrate the feature information,the images of the Gramian angular summation field(GASF)and Gramian angular difference field(GADF)are fused by the weighted average fusion method.Secondly,the channel attention mechanism and temporal attention mechanism are introduced in the CNN model to optimize the CNN learning mechanism.Introducing the concept of residuals in the attention mechanism improves the feasibility of optimization.Finally,the weighted average fused images are fed into the CNN for feature extraction and fault diagnosis.The validity of the proposed method is verified by experiments with abnormal valve clearance.The average diagnostic accuracy is 98.40%.When−20 dB≤signal-to-noise ratio(SNR)≤20 dB,the diagnostic accuracy of the proposed method is higher than 94.00%.The proposed method has superior diagnostic performance.Moreover,it has a certain anti-noise capability and variable-load adaptive capability.

Permitted emissions of major air pollutants from coal-fired power plants in China based on best available control technology

Based on the activity level and technical information of coal-fired power-generating units(CFPGU)obtained in China from 2011 to 2015,we,1)analyzed the time and spatial distribution of SO_(2) and NOx emission performance of CFPGUs in China;2)studied the impact of installed capacity,sulfur content of coal combustion,and unit operation starting time on CFPGUs’pollutant emission performance;and 3)proposed the SO_(2) and NOx emission performance standards for coal-fired power plants based on the best available control technology.Our results show that:1)the larger the capacity of a CFPGU,the higher the control level and the faster the improvement;2)the CFPGUs in the developed eastern regions had significantly lower SO_(2) and NOx emission performance values than those in other provinces due to better economic and technological development and higher environmental management levels;3)the SO_(2) and NOx emission performance of the Chinese thermal power industry was significantly affected by the single-unit capacity,coal sulfur content,and unit operation starting time;and 4)based on the achievability analysis of best available pollution control technology,we believe that the CFPGUs’SO_(2) emission performance reference values should be 0.34 g/kWh for active units in general areas,0.8 g/kWh for active units in high-sulfur coal areas,and 0.13 g/kWh for newly built units and active units in key areas.In addition,the NOx emission performance reference values should be 0.35 g/kWh for active units in general areas and 0.175 g/kWh for new units and active units in key areas.