摘要
The time consumed in starting up the distillation unit with appreciable holdups can be an important fraction of the total distillation time,particular for catalytic distillation systems with large holdups.To optimize the whole process,the start-up period has to be considered as a part of the complete catalytic distillation process.In this paper,BP artificial neural network model was presented as a tool to estimate the start-up process for a given catalytic distillation system.It can been seen that through the examination of the case studied in this work,a good start-up policy can reduce both the energy and time requirements in the start-up phase of catalytic distillation processes.The results based on 20 start-up policies showed that the time consumed in start-up period with an average error of 4.140% and a maximum error of 10.291% for the case studied in this work.The accuracy of the model will depend upon the data available and the type of model.
The time consumed in starting up the distillation unit with appreciable holdups can be an important traction of the total distillation time, particular for catalytic distillation systems with large holdups. To optimize the whole process, the start-up period has to be considered as a part of the complete catalytic distillation process. In this paper, BP artificial neural network model was presented as a tool to estimate the start-up process for a given catalytic distillation system. It can been seen that through the examination of the case studied in this work, a good start-up policy can reduce both the energy and time requirements in the start-up phase of catalytic distillation processes. The results based on 20 start-up policies showed that the time consumed in start-up period with an average error of 4. 140% and a maximum error of 10.291% for the case studied in this work. The accuracy of the model will depend upon the data available and the type of model.
出处
《分子催化》
EI
CAS
CSCD
北大核心
2006年第4期360-362,共3页
Journal of Molecular Catalysis(China)
关键词
人工神经网络
催化精馏
开工
预测模型
Artificial neural network
Catalytic distillation
Start-up
Prediction model