In fluid catalytic cracking(FCC) unit, it is greatly important to control the coke yield, since the increase of coke yield not only leads to the reduction of total liquid yield, but also affects the heat balance and o...In fluid catalytic cracking(FCC) unit, it is greatly important to control the coke yield, since the increase of coke yield not only leads to the reduction of total liquid yield, but also affects the heat balance and operation of FCC unit. Consequently, it is significant to predict the coke yield accurately. The coke formation and burning reactions are affected by many parameters which influence each other, so it is difficult to establish a prediction model using traditional models. This paper combines the industrial production data and establishes a generalized regression neural network(GRNN) model and a back propagation(BP) neural network model to predict the coke yield respectively. The comparison and analysis results show that the accuracy and stability of the BP neural network prediction results are better than that of the GRNN. Then, the particle swarm optimization to optimize BP neural network(PSO-BP) and genetic algorithm to optimize the BP neural network(GA-BP) were further used to improve the prediction precision. The comparison of these models shows that they can improve the prediction precision. However, considering the accuracy and stability of the prediction results, the GA-BP model is better than PSO-BP model.展开更多
This paper examines the experimental study on influence of material component to non-linear relation between sediment yield and drainage network development completed in the Lab. The area of flume drainage system is 8...This paper examines the experimental study on influence of material component to non-linear relation between sediment yield and drainage network development completed in the Lab. The area of flume drainage system is 81.2 m2, the longitudinal gradient and cross section slope are from 0.0348 to 0.0775 and from 0.0115 to 0.038, respectively. Different model materials with a medium diameter of 0.021 mm, 0.076 mm and 0.066 mm cover three experiments each. An artificial rainfall equipment is a sprinkler-system composed of 7 downward nozzles, distributed by hexagon type and a given rainfall intensity is 35.56 mm/hr.cm2. Three experiments are designed by process-response principle at the beginning the ψ shaped small network is dug in the flume. Running time spans are 720 m, 1440 minutes and 540 minutes for Runs I, IV and VI, respectively. Three experiments show that the sediment yield processes are characterized by delaying with a vibration. During network development the energy of a drainage system is dissipated by two ways, of which one is increasing the number of channels (rill and gully), and the other one is enlarging the channel length. The fractal dimension of a drainage network is exactly an index of energy dissipation of a drainage morphological system. Change of this index with time is an unsymmetrical concave curve. Comparison of three experiments explains that the vibration and the delaying ratio of sediment yield processes increase with material coarsening, while the number of channel decreases. The length of channel enlarges with material fining. There exists non-linear relationship between fractal dimension and sediment yield with an unsymmetrical hyperbolic curve. The absolute value of delaying ratio of the curve reduces with time running and material fining. It is characterized by substitution of situation to time.展开更多
Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling techniq...Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.展开更多
<div style="text-align:justify;"> The fitting of water requirement and yield during the growth period of winter wheat can improve yield effectively and improve irrigation water use efficiency with a ce...<div style="text-align:justify;"> The fitting of water requirement and yield during the growth period of winter wheat can improve yield effectively and improve irrigation water use efficiency with a certain amount of resource input. This paper selects the irrigation amount, precipitation and yield of winter wheat at the Wuqiao Scientific Observation and Experimental Station. Fitting the water requirement and yield of winter wheat based on three types of artificial neural networks. This paper uses support vector machine (SVM), thought evolution algorithm to optimize BP neural network (MAE-BP) and generalized regression neural network (GRNN) to fit the water requirement and yield of two crops. The SVM is the model with the highest fitting accuracy among the three models, the RMSE, MAE, NS and R2 between predictive value and true value are 7.45 kg/hectares, 213.64 kg/hectares, 0.8086, 0.9409 respectively. </div>展开更多
The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural ...The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).展开更多
This paper focuses on the effects of precipitation and vegetation coverage on runoff and sediment yield in the Jinsha River Basin. Results of regression analysis were taken as input variables to investigate the applic...This paper focuses on the effects of precipitation and vegetation coverage on runoff and sediment yield in the Jinsha River Basin. Results of regression analysis were taken as input variables to investigate the applicability of the adaptive network-based fuzzy inference system （ANFIS） to simulating annual runoff and sediment yield. Correlation analysis indicates that runoff and sediment yield are positively correlated with the precipitation indices, while negatively correlated with the vegetation indices. Furthermore, the results of stepwise regression show that annual precipitation is the most important factor influencing the variation of runoff, followed by forest coverage, and their contributions to the variation ofrunoffare 69.8% and 17.3%, respectively. For sediment yield, rainfall erosivity is the most important factor, followed by forest coverage, and their contributions to the variation of sediment yield are 49.3% and 24.2%, respectively. The ANFIS model is of high precision in runoff forecasting, with a relative error of less than 5%, but of poor precision in sediment yield forecasting, indicating that precipitation and vegetation coverage can explain only part of the variation of sediment yield, and that other impact factors, such as human activities, should be sufficiently considered as well.展开更多
Using the artificial nerve network′s knowledge, establish the estimate′s mathematics model of the soybean′s yield, and by the model we can increase accuracy of the soybean yield forecast.
Enumerating the relative proportions of soil losses due to rill erosion processes during monsoon and post-monsoon season is a significant factor in predicting total soil losses and sediment transport and deposition. P...Enumerating the relative proportions of soil losses due to rill erosion processes during monsoon and post-monsoon season is a significant factor in predicting total soil losses and sediment transport and deposition. Present study evaluated the rill network with simulated experiment of treatments on varying slope and rainfall intensity to find out the rill erosion processes and sediment discharge in relation to slope and rainfall intensity. Results showed a significant relationship between the rainfall intensity and sediment yield (r = 0.75). Our results illustrated that due to increase in rainfall intensity represent the development of efficient rill network while, no rill was found with a slope of 20° and a rainfall intensity of 60 mm·h-1. The highest rill length was observed in plot E with 20° slope and 120 mm·h-1 rainfall intensity at 360 minutes. Positive and strong correlation (R2 = 0.734, P 0.001) was observed between the cumulative rainfall intensity and sediment discharge. A longitudinal profile was delineated and showed that the depth and numbers of depressions amplified with time and were more prominent for escalating rainfall intensity for its steeper slopes. Information derived from the study could be applied to estimate longer-term erosion stirring over larger areas possessing parallel landforms.展开更多
文摘In fluid catalytic cracking(FCC) unit, it is greatly important to control the coke yield, since the increase of coke yield not only leads to the reduction of total liquid yield, but also affects the heat balance and operation of FCC unit. Consequently, it is significant to predict the coke yield accurately. The coke formation and burning reactions are affected by many parameters which influence each other, so it is difficult to establish a prediction model using traditional models. This paper combines the industrial production data and establishes a generalized regression neural network(GRNN) model and a back propagation(BP) neural network model to predict the coke yield respectively. The comparison and analysis results show that the accuracy and stability of the BP neural network prediction results are better than that of the GRNN. Then, the particle swarm optimization to optimize BP neural network(PSO-BP) and genetic algorithm to optimize the BP neural network(GA-BP) were further used to improve the prediction precision. The comparison of these models shows that they can improve the prediction precision. However, considering the accuracy and stability of the prediction results, the GA-BP model is better than PSO-BP model.
基金Joint project by National Natural Science Foundation of China and Ministry of Water Resources of China, No.59890200 National Na
文摘This paper examines the experimental study on influence of material component to non-linear relation between sediment yield and drainage network development completed in the Lab. The area of flume drainage system is 81.2 m2, the longitudinal gradient and cross section slope are from 0.0348 to 0.0775 and from 0.0115 to 0.038, respectively. Different model materials with a medium diameter of 0.021 mm, 0.076 mm and 0.066 mm cover three experiments each. An artificial rainfall equipment is a sprinkler-system composed of 7 downward nozzles, distributed by hexagon type and a given rainfall intensity is 35.56 mm/hr.cm2. Three experiments are designed by process-response principle at the beginning the ψ shaped small network is dug in the flume. Running time spans are 720 m, 1440 minutes and 540 minutes for Runs I, IV and VI, respectively. Three experiments show that the sediment yield processes are characterized by delaying with a vibration. During network development the energy of a drainage system is dissipated by two ways, of which one is increasing the number of channels (rill and gully), and the other one is enlarging the channel length. The fractal dimension of a drainage network is exactly an index of energy dissipation of a drainage morphological system. Change of this index with time is an unsymmetrical concave curve. Comparison of three experiments explains that the vibration and the delaying ratio of sediment yield processes increase with material coarsening, while the number of channel decreases. The length of channel enlarges with material fining. There exists non-linear relationship between fractal dimension and sediment yield with an unsymmetrical hyperbolic curve. The absolute value of delaying ratio of the curve reduces with time running and material fining. It is characterized by substitution of situation to time.
文摘Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.
文摘<div style="text-align:justify;"> The fitting of water requirement and yield during the growth period of winter wheat can improve yield effectively and improve irrigation water use efficiency with a certain amount of resource input. This paper selects the irrigation amount, precipitation and yield of winter wheat at the Wuqiao Scientific Observation and Experimental Station. Fitting the water requirement and yield of winter wheat based on three types of artificial neural networks. This paper uses support vector machine (SVM), thought evolution algorithm to optimize BP neural network (MAE-BP) and generalized regression neural network (GRNN) to fit the water requirement and yield of two crops. The SVM is the model with the highest fitting accuracy among the three models, the RMSE, MAE, NS and R2 between predictive value and true value are 7.45 kg/hectares, 213.64 kg/hectares, 0.8086, 0.9409 respectively. </div>
文摘The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).
基金supported by the National Natural Science Foundation of China (Grant No. 40971012)International Science and Technology Cooperation Program of China (Grants No. 2011DFA20820 and 2011DFG93160)Tsinghua University Independent Scientific Research Program (Grant No.20121080027)
文摘This paper focuses on the effects of precipitation and vegetation coverage on runoff and sediment yield in the Jinsha River Basin. Results of regression analysis were taken as input variables to investigate the applicability of the adaptive network-based fuzzy inference system （ANFIS） to simulating annual runoff and sediment yield. Correlation analysis indicates that runoff and sediment yield are positively correlated with the precipitation indices, while negatively correlated with the vegetation indices. Furthermore, the results of stepwise regression show that annual precipitation is the most important factor influencing the variation of runoff, followed by forest coverage, and their contributions to the variation ofrunoffare 69.8% and 17.3%, respectively. For sediment yield, rainfall erosivity is the most important factor, followed by forest coverage, and their contributions to the variation of sediment yield are 49.3% and 24.2%, respectively. The ANFIS model is of high precision in runoff forecasting, with a relative error of less than 5%, but of poor precision in sediment yield forecasting, indicating that precipitation and vegetation coverage can explain only part of the variation of sediment yield, and that other impact factors, such as human activities, should be sufficiently considered as well.
文摘Using the artificial nerve network′s knowledge, establish the estimate′s mathematics model of the soybean′s yield, and by the model we can increase accuracy of the soybean yield forecast.
文摘Enumerating the relative proportions of soil losses due to rill erosion processes during monsoon and post-monsoon season is a significant factor in predicting total soil losses and sediment transport and deposition. Present study evaluated the rill network with simulated experiment of treatments on varying slope and rainfall intensity to find out the rill erosion processes and sediment discharge in relation to slope and rainfall intensity. Results showed a significant relationship between the rainfall intensity and sediment yield (r = 0.75). Our results illustrated that due to increase in rainfall intensity represent the development of efficient rill network while, no rill was found with a slope of 20° and a rainfall intensity of 60 mm·h-1. The highest rill length was observed in plot E with 20° slope and 120 mm·h-1 rainfall intensity at 360 minutes. Positive and strong correlation (R2 = 0.734, P 0.001) was observed between the cumulative rainfall intensity and sediment discharge. A longitudinal profile was delineated and showed that the depth and numbers of depressions amplified with time and were more prominent for escalating rainfall intensity for its steeper slopes. Information derived from the study could be applied to estimate longer-term erosion stirring over larger areas possessing parallel landforms.
