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
The technique of modeling operating temperature variations of shell-and-tube heat exchanger 10-E-01 of kerosene-crude oil streams of Port Harcourt refinery crude distillation unit is presented in this research. A...The technique of modeling operating temperature variations of shell-and-tube heat exchanger 10-E-01 of kerosene-crude oil streams of Port Harcourt refinery crude distillation unit is presented in this research. Appropriate first-order model equations were developed applying principles of energy balance. The differential equations developed for the process streams which exchanged heat was evaluated numerically to predict the temperature variations as a function of time. The relevant parameters associated with typical heat exchanger works were calculated using plant data of 10-E-02. The model strives to predict the final kerosene temperature from 488 to 353.6 K. While the crude oil streams temperature rose from 313 to 353.6 K. The developed model enables the operator to predict the final temperature at the kerosene hydro-treating unit and thereby prevent regular emergency shutdowns due to excessive temperature rise.展开更多
This study deals with the high-risk shell-and-tube heat exchangers in the effluent system of hydrogenation reaction of the petrochemical industry.The process of hydroprocessing reactor effluent system is simulated in ...This study deals with the high-risk shell-and-tube heat exchangers in the effluent system of hydrogenation reaction of the petrochemical industry.The process of hydroprocessing reactor effluent system is simulated in Aspen Plus to study the distribution of corrosive medium in the three phases of oil,gas and water.The least-squares method is utilized to calculate the ammonium salt crystallization temperature.Then,the heat exchanger with risk of ammonium salt crystal corrosion is identified.Dynamic mathematical modeling of the heat exchanger is established to determine the transfer function.A temperature control system with proportional integral derivative(PID)control of the heat exchanger outlet is designed,and fuzzy logic is used to implement self-tuning of PID parameters.After MATLAB simulation,the results show the control system can achieve rapid control of the heat exchanger outlet temperature.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.
文摘The technique of modeling operating temperature variations of shell-and-tube heat exchanger 10-E-01 of kerosene-crude oil streams of Port Harcourt refinery crude distillation unit is presented in this research. Appropriate first-order model equations were developed applying principles of energy balance. The differential equations developed for the process streams which exchanged heat was evaluated numerically to predict the temperature variations as a function of time. The relevant parameters associated with typical heat exchanger works were calculated using plant data of 10-E-02. The model strives to predict the final kerosene temperature from 488 to 353.6 K. While the crude oil streams temperature rose from 313 to 353.6 K. The developed model enables the operator to predict the final temperature at the kerosene hydro-treating unit and thereby prevent regular emergency shutdowns due to excessive temperature rise.
基金supported by the National Natural Science Foundation of China(Grant No.51876194U1909216)General Research Project of Zhejiang Provincial Department of Education(Y201942785)。
文摘This study deals with the high-risk shell-and-tube heat exchangers in the effluent system of hydrogenation reaction of the petrochemical industry.The process of hydroprocessing reactor effluent system is simulated in Aspen Plus to study the distribution of corrosive medium in the three phases of oil,gas and water.The least-squares method is utilized to calculate the ammonium salt crystallization temperature.Then,the heat exchanger with risk of ammonium salt crystal corrosion is identified.Dynamic mathematical modeling of the heat exchanger is established to determine the transfer function.A temperature control system with proportional integral derivative(PID)control of the heat exchanger outlet is designed,and fuzzy logic is used to implement self-tuning of PID parameters.After MATLAB simulation,the results show the control system can achieve rapid control of the heat exchanger outlet temperature.
