Silver powder was fabricated by spray pyrolysis, using 2%–20% AgNO3 solution, 336–500 mL/h flux of AgNO3 solution, 0.28–0.32 MPa flux of carrier gas and in the 620–820 °C temperature range. The effects of furnace set temperature,
concentration of AgNO3 aqueous solution, flux of AgNO3 aqueous solution as well as carrier gas on the morphology and particle size distribution of silver powder, were investigated.
The experimental results showed that with the high concentration of AgNO3 aqueous solution, the average grain size of silver decreased with the increasing of furnace set temperature. But the gain
size distribution was not homogenous, the discontinuous grain growth occurred. With the low concentration of AgNO3 aqueous solution, the higher furnace set temperature made the nano sliver grains sintered together to grow. Nano silver powder
about 100 nm was fabricated by spray pyrolysis, using 2wt% AgNO3 solutions, 336 mL/h flux of AgNO3 aqueous solution, 0.32 MPa flux of carrier gas at 720 °C furnace set temperature. 相似文献
Rosemary (Rosemarinus officinalis L.) leaves were extracted with three different solvents, namely hexane, acetone and methanol. A reverse-phase high-performance
liquid chromatography system in combination with a mass detector was used to quantitate the content of carnosol, carnosic
acid and ursolic acid in the rosemary extracts. All rosemary extracts showed strong inhibitory effects on lipid oxidation
and soybean lipoxygenase activity. 相似文献
Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.
Journal of Applied Electrochemistry - In this work, the electrochemical corrosion behaviours of selective laser melted (SLMed) and wrought Ti6Al4V alloys in acid fluoride-containing artificial... 相似文献