The accumulation of undesirable deposits on the heat exchange surface represents a critical issue in industrial heat exchangers.Taking experimental measurements of the fouling is relatively difficult and,often,this me...The accumulation of undesirable deposits on the heat exchange surface represents a critical issue in industrial heat exchangers.Taking experimental measurements of the fouling is relatively difficult and,often,this method does not lead to precise results.To overcome these problems,in the present study,a new approach based on an Artificial Neural Network(ANN)is used to predict the fouling resistance as a function of specific measurable variables in the phosphoric acid concentration process.These include:the phosphoric acid inlet and outlet temperatures,the steam temperature,the phosphoric acid density,the phosphoric acid volume flow rate circulating in the loop.Some statistical accuracy indices are employed simultaneously to justify the interrelation between these independent variables and the fouling resistance and to select the best training algorithm allowing the determination of the optimal number of hidden neurons.In particular,the BFGS quasi-Newton back-propagation approach is found to be the most performing of the considered training algorithms.Furthermore,the best topology ANN for the shell and tube heat exchanger is obtained with a network consisting of one hidden layer with 13 neurons using a tangent sigmoid transfer function for the hidden and output layers.This model predicts the experimental values of the fouling resistance with AARD%=0.065,MSE=2.168×10^(−11),RMSE=4.656×10^(−6)and r^(2)=0.994.展开更多
Using ray-tracing simulation based on Monte Carlo method, the effects of phosphor concentration and thickness on light output and phosphor consumption of pcwLEDs were investigated in this work. The simulation was impr...Using ray-tracing simulation based on Monte Carlo method, the effects of phosphor concentration and thickness on light output and phosphor consumption of pcwLEDs were investigated in this work. The simulation was improved to comprehensively imitate the whole optical process of pc-wLEDs, including total produce of chip and phosphor light, losses in the propagation, and output.Experiments were conducted to verify the simulation.Results show that, light output changes non-monotonously over phosphor concentration and thickness, having maximum value. Experimental maximum light efficiency of158 lmáW-1was obtained at concentration of 16 wt%, 6 %higher than that of 11 wt% and 17 % higher than that of 33wt%. Phosphor consumption of pc-wLEDs increases linearly with the increase of phosphor concentration and the decrease of thickness. Experimental phosphor consumption of pc-wLEDs with concentration of 11 wt% is only 37 %of that of 33 wt%.展开更多
文摘The accumulation of undesirable deposits on the heat exchange surface represents a critical issue in industrial heat exchangers.Taking experimental measurements of the fouling is relatively difficult and,often,this method does not lead to precise results.To overcome these problems,in the present study,a new approach based on an Artificial Neural Network(ANN)is used to predict the fouling resistance as a function of specific measurable variables in the phosphoric acid concentration process.These include:the phosphoric acid inlet and outlet temperatures,the steam temperature,the phosphoric acid density,the phosphoric acid volume flow rate circulating in the loop.Some statistical accuracy indices are employed simultaneously to justify the interrelation between these independent variables and the fouling resistance and to select the best training algorithm allowing the determination of the optimal number of hidden neurons.In particular,the BFGS quasi-Newton back-propagation approach is found to be the most performing of the considered training algorithms.Furthermore,the best topology ANN for the shell and tube heat exchanger is obtained with a network consisting of one hidden layer with 13 neurons using a tangent sigmoid transfer function for the hidden and output layers.This model predicts the experimental values of the fouling resistance with AARD%=0.065,MSE=2.168×10^(−11),RMSE=4.656×10^(−6)and r^(2)=0.994.
基金financially supported by the National Natural Science Foundation of China (No. 51272027)the Ph.D. Programs Foundation of Education Ministry of China (No. 20100006110011)
文摘Using ray-tracing simulation based on Monte Carlo method, the effects of phosphor concentration and thickness on light output and phosphor consumption of pcwLEDs were investigated in this work. The simulation was improved to comprehensively imitate the whole optical process of pc-wLEDs, including total produce of chip and phosphor light, losses in the propagation, and output.Experiments were conducted to verify the simulation.Results show that, light output changes non-monotonously over phosphor concentration and thickness, having maximum value. Experimental maximum light efficiency of158 lmáW-1was obtained at concentration of 16 wt%, 6 %higher than that of 11 wt% and 17 % higher than that of 33wt%. Phosphor consumption of pc-wLEDs increases linearly with the increase of phosphor concentration and the decrease of thickness. Experimental phosphor consumption of pc-wLEDs with concentration of 11 wt% is only 37 %of that of 33 wt%.
