naa a22 4500 510820085 CHVBK 20180411083527.0 cr unu---uuuuu 180411e20130301xx s 000 0 eng 10.1007/s11814-012-0163-4 doi (NATIONALLICENCE)springer-10.1007/s11814-012-0163-4 Simulation and analysis of dehydration distillation column based on distillation mechanism integrated with neural network [Elektronische Daten] [Qingtao Sun, Chunjian Pan, Xuefeng Yan] In an industrial solvent dehydration distillation column (SDDC) model, the Murphree efficiency represents the separation ability of a distillation tray and the SDDC model's performance depends on the value accuracy of the Murphree efficiency. Because there are many operation conditions having nonlinear effect on Murphree efficiency, it is difficult to determine its value. To develop a precise and robust SDDC model, a novel hybrid model combining distillation mechanism with neural network is proposed. In the SDDC hybrid model, the neural network is employed to model the nonlinear relationship between the operation conditions and Murphree efficiency, which is embedded into the SDDC mechanistic model. The results showed a good predicting and robust performance of the hybrid model under different operation conditions. Based on the hybrid model, the effect of the operation conditions on SDDC was analyzed to obtain some useful guiding rules for the SDDC operation. Korean Institute of Chemical Engineers, Seoul, Korea, 2013 Hybrid Model nationallicence Murphree Efficiency nationallicence Neural Networks nationallicence Dehydratic Distillation nationallicence Sun Qingtao Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, 200237, Shanghai, P. R. China aut Pan Chunjian Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, 200237, Shanghai, P. R. China aut Yan Xuefeng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, 200237, Shanghai, P. R. China aut Korean Journal of Chemical Engineering Springer US; http://www.springer-ny.com 30/3(2013-03-01), 518-527 0256-1115 30:3<518 2013 30 11814 https://doi.org/10.1007/s11814-012-0163-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11814-012-0163-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sun Qingtao Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, 200237, Shanghai, P. R. China aut NATIONALLICENCE 700 1- Pan Chunjian Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, 200237, Shanghai, P. R. China aut NATIONALLICENCE 700 1- Yan Xuefeng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, 200237, Shanghai, P. R. China aut NATIONALLICENCE 773 0- Korean Journal of Chemical Engineering Springer US; http://www.springer-ny.com 30/3(2013-03-01), 518-527 0256-1115 30:3<518 2013 30 11814 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer