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| 1 | Parameter optimization of fluted-roller meter using discrete element method显示文摘The most important performance indicator of a fertilizer metering mechanism is the evenness of fertilizer flow.In this study,a regression mathematical model between the key operating parameters of the fluted roller meter and the flow evenness was developed to simulate a fluted-roller meter for metering diammonium phosphate fertilizer using the discrete element method(DEM).The model was verified by bench test using the same equipment and parameters as the DEM model.Selected working parameters of the fluted-roller meter,including roll length(L),roll rotational speed(n),and flap angle(α)(for fertilizer discharge control),were optimized to maximize the flow evenness.Flow evenness was assessed by the coefficient of variation(CV)of the discharging mass during the operation.The simulation and experiment results showed the similar trends,in terms of effects of machine parameters on the CV.The relative errors ranged from 0.2%to 34.6%with a mean of 10.5%.This demonstrated that the DEM model was feasible to simulate the metering process of the fluted-roller meter.The machine parameters that significantly affected the values of CV in descending order wereα,L and n.Both simulation and measurement results revealed that the optimal machine parameters,represented by the minimum value of CV,were observed at L=45 mm,n=55 r/min andα=22.5°.This combination of parameters returned CV values of 10.89%and 9.55%for simulations and measurements,respectively.The study provided useful information for guiding the design and selection of machine parameters for metering devices for fertilizer applications. | Yuxiang Huang Botao Wang Yuxiang Yao Shangpeng Ding Junchang Zhang Ruixiang Zhu | 2018 | International Journal of Agricultural and Biological Engineering2018，11，6： | 1 |
| 2 | Synthesis of large area silicon nanowire arrays via self assembling nanoelectrochemistry显示文摘 | Peng Kuiqing Yan Yunjie Gao Shangpeng | | 0，，16： | 1 |
| 3 | 显示文摘 | Zhang Yingjiu Wang Nanlin Gao Shangpeng | 2002 | Chem Mater2002，14，： | 1 |
| 4 | Synthe- sis of large-area silicon nanowire arrays via self-assembling nanoelectrochemistry 显示文摘 | Peng Kuiqing Yah Yunjie Gao Shangpeng | 2002 | Adv Mater2002，14，： | 1 |
| 5 | Influence of sodium sulfate and sodium nitrite on strength of negative temperature concrete 显示文摘 | SHENG Shangpeng WANG Qicai HAN Lei | 2006 | Optimize Capital Construct2006，27，1： | 1 |
| 6 | Synthesis of large-area silicon nanowire arrays via self-assembly nano-electrochemistry显示文摘 | PENG Kuiqing YAN Yunjie GAO Shangpeng | 2002 | Adv Matter2002，14，： | 1 |
| 7 | Hybrid Network Model Based on Data Enhancement for Short-term Power Prediction of New PV Plants显示文摘This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV plants.First,a time-series gener ative adversarial network(TimeGAN)is used to learn the distri bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training set.Subsequently,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep&cross network(DCN)is con structed to effectively extract deep information from the origi nal features while enhancing the impact of important informa tion on the prediction results.Finally,the hyperparameters in the hybrid network model are optimized using the whale optimi zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction results.The simulation results show that after data enhance ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability. | Shangpeng Zhong Xiaoming Wang Bin Xu Hongbin Wu Ming Ding | 2024 | Journal of Modern Power Systems and Clean Energy2024，12，1： | 0 |
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