First Principle Modeling and Neural Network–Based Empirical Modeling with Experimental Validation of Binary Distillation Column
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Amit Kumar Singh
Amit Kumar Singh is currently working towards his Ph.D. degree in Department of Electrical Engineering at Indian Institute of Technology, Roorkee (India). His research interests include control system, process control and application of evolutionary techniques to chemical processes.
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Barjeev Tyagi
Barjeev Tyagi received B. Tech. degree in Electrical Engineering from University of Roorkee (India) in 1987 and Ph.D. degree from IIT Kanpur in 2006. Presently, he is Associate professor in Electrical Engineering Department at Indian Institute of Technology, Roorkee (India). His research interests include control system, power system deregulation, power system optimization and control.Vishal Kumar received the Ph.D. degree in power system engineering from the Indian Institute of Technology, Roorkee, India, in 2007. Currently, he is Assistant professor in the Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India. His research interests include power distribution system operation and protection, and digital design and verification.
Abstract
To get the better product quality and to decrease the energy consumption of the distillation column, an accurate and suitable nonlinear model is crucial important. In this work, two types of model have been developed for an existing experimental setup of continuous binary distillation column (BDC). First model is a theoretical tray-to-tray binary distillation model for describing the steady-state behavior of composition in response to changes in reflux flows and in reboiler duty. Another model is an artificial neural network (ANN)–based input/output data relationship model. In ANN-based model, temperature of first tray, feed flow rate, and column pressures have been taken in addition to reflux flow rate and reboiler heat duty as inputs to give the more accurate I/O relationship. The comparison of output of ANN model and the equation-based model with the real-time output of the experimental setup of BDC has been given for the validation of developed models.
About the authors
Amit Kumar Singh is currently working towards his Ph.D. degree in Department of Electrical Engineering at Indian Institute of Technology, Roorkee (India). His research interests include control system, process control and application of evolutionary techniques to chemical processes.
Barjeev Tyagi received B. Tech. degree in Electrical Engineering from University of Roorkee (India) in 1987 and Ph.D. degree from IIT Kanpur in 2006. Presently, he is Associate professor in Electrical Engineering Department at Indian Institute of Technology, Roorkee (India). His research interests include control system, power system deregulation, power system optimization and control.
Vishal Kumar received the Ph.D. degree in power system engineering from the Indian Institute of Technology, Roorkee, India, in 2007. Currently, he is Assistant professor in the Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India. His research interests include power distribution system operation and protection, and digital design and verification.
Acknowledgement
The authors wish to acknowledge the financial support of the Ministry of human resource and developments (MHRD), India under faculty initiation grant scheme with grant no. MHRD-03-29-801-108(FIG).
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Articles in the same Issue
- Masthead
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- Ionic Liquids as Green Solvents for the Extraction of Endosulfan from Aqueous Solution: A Quantum Chemical Approach
- Neuro-Fuzzy-Based Control for Parallel Cascade Control
- Modelling the Heat and Mass Transfer during Hot Pressing of Medium Density Fibreboard
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