Modified neural network based cascaded control for product composition of reactive distillation

• Autorzy: Sakhre V. , Jain S. , Sapkal V. S. , Agarwal D. P.

Identyfikatory

DOI

10.1515/pjct-2016-0037

• Języki publikacji: EN

Abstrakty

EN

In this research work, neural network based single loop and cascaded control strategies, based on Feed Forward Neural Network trained with Back Propagation (FBPNN) algorithm is carried out to control the product composition of reactive distillation. The FBPNN is modified using the steepest descent method. This modification is suggested for optimization of error function. The weights connecting the input and hidden layer, hidden and output layer is optimized using steepest descent method which causes minimization of mean square error and hence improves the response of the system. FBPNN, as the inferential soft sensor is used for composition estimation of reactive distillation using temperature as a secondary process variable. The optimized temperature profile of the reactive distillation is selected as input to the neural network. Reboiler heat duty is selected as a manipulating variable in case of single loop control strategy while the bottom stage temperature T9 is selected as a manipulating variable for cascaded control strategy. It has been observed that modified FBPNN gives minimum mean square error. It has also been observed from the results that cascaded control structure gives improved dynamic response as compared to the single loop control strategy.

Słowa kluczowe

EN

composition; inferential control; neural network; reactive distillation; soft sensor

• Wydawca: West Pomeranian University of Technology. Publishing House
• Czasopismo: Polish Journal of Chemical Technology
• Rocznik: 2016
• Tom: Vol. 18, nr 2
• Strony: 111--121
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Sakhre V.

• Madhav Institute of Technology & Science, Gwalior-474005, India

autor

Jain S.

• Madhav Institute of Technology & Science, Gwalior-474005, India

autor

Sapkal V. S.

• SGB Amravati University, Amravati-444062, India

autor

Agarwal D. P.

• IIT, New Delhi-110001, India

Bibliografia

• 1. Sorensen, E., Macchieto, S., Stuart, G. & Skogestad, S. (1996). Optimal Control and Online Operation of Reactive Batch Distillation, Comput. & Chem. Eng. 20, 1491–1498. DOI: 10.1016/0098-1354(95)00234.
• 2. Al-Arfaj, M. & Luyben, W.L. (2000). Comparison of Alternative Control Structures for an Ideal Two-Product Reactive Distillation Column. Ind. Eng. Chem. Res. 39, 3298–3307. DOI: 10.1021/ie 990886j.
• 3. Kano, M., Miyazaki, K., Hasebe, S. & Hashimoto, I. (2000). Inferential Control System of Distillation Compositions using Dynamic Partial Least Squares Regression. J. Proc. Cont. 10, 157–166. DOI: 10.1016/50959-1524(99)00027-x.
• 4. Balasubramhanya, L.S. & Doyle III, F.J. (2000). Nonlinear Model Based Control of a Batch Reactive Distillation Column, J. Proc. Cont. 10, 209–218. DOI: 10.1016/50959-1524 (99) 00024-4.
• 5. Tian, Yu-Chu, Zhao, F., Bisowarno, B.H. & Tade, M.O. (2003). Pattern-Based Predictive Control for ETBE Reactive Distillation. J. Proc. Cont. 13, 57–67. DOI: 10.106/50959-1524(02)00011-2.
• 6. Hung, Shih-Bo & Lee, M.J. (2005). Control of Different Reactive Distillation Configuration. Wiley Inter. Sci. 52, 1423–1440. DOI: 10.1002/aic.10743.
• 7. Lai, K., Hung, S.B., Hung, W.J., Yu, C.C., Lee, M.J. & Huang, H.P. (2007). Design and control of reactive distillation for ethyl and isopropyl acetates production with azeotropic feeds. Chem. Engineer. Sci. 62, 878–898. DOI: 10.1016/j.ces.2006.10.019.
• 8. Chien, I-Lung., Chen, K. & Kuo, C.L. (2008). Overall control strategy of a coupled reactor/columns process for the production of ethyl acrylate. J. Proc. Cont. 18, 215–231. DOI: 10.1016/j.jprocont.2007.02.006.
• 9. Wang, San-Jang., Yu, C.C. & Huang H.P. (2010). Steady-state design of thermally coupled reactive distillation process for the synthesis of Diphenyl carbonate, Computers and Chemical Engineering, 34, 361–373. DOI:10.1016/j.compchemeng.2013.02.001.
• 10. Wei, Hon-Yu., Rokhmah, A., Handogo, R. & Chien, I.H. (2011). Design and control of reactive- distillation process for the production of diethyl carbonate via two consecutive trans-esterification reactions, Journal of Process Control, 21, 1193–1207. DOI: 10.1016/2011.06.006.
• 11. Wu, Yi-Chang., Lee, H.Y., Tsai, C.Y., Huang, H.P. & Chien, I.H. (2013). Design and control of a reactive-distillation process for esterification of an alcohol mixture containing ethanol and n-butanol. Comput. Chem. Engine. 57, 63–67. DOI: 10.1016/2013.01.002.
• 12. Mahindrakar, V. & Hahn, J. (2014). Dynamics and control of benzene hydrogenation via reactive distillation. J. Proc.Cont. 24, 113–124. DOI: 10.1016/2014.01.005.
• 13. Brasio, A.S.R., Romanenko, A., Leal, J., Santos, L.O. & Fernandes, N.C.P. (2013). Nonlinear model predictive control of biodiesel production via transesterifi cation of used vegetable oils. J. Proc. Cont. 23, 1471–1479. DOI: 10.1016/2013.09.023.
• 14. Li, W., Hung, K., Zhang, L., Chen, H. & Wang, S.J. (2012). Dynamics and control of totally refluxed reactive distillation columns. J. Proc. Cont. 22, 1182–1197. DOI: 10.1016/2012.05.007.
• 15. Sumana, C.& Venkateswarlu, C. (2007). Development of a software sensor for compositions in continuous reactive distillation, J. Sci. & Indust. Res. 66, 898–904.
• 16. Kathel, P. & Jana, A.K. (2010), Dynamic simulation and nonlinear control of a rigorous batch reactive distillation. ISA Transact. 49, 130–137. DOI: 10.1016/2009.09.007.
• 17. Prakash, K.J.J., Patle, D.S. & Jana, A.K. (2011). Neuro-estimation based GMC control of a batch reactive distillation, ISA Transact. 50, 357–363. DOI: 10.1016/2011.01.010.
• 18. Raghavan, S.R.V., Radhakrishnan, T.K. & Srinivasan, K. (2011). Soft sensor based composition estimation and controller design for an ideal reactive distillation column. ISA Transact. 50, 61–70. DOI: 10.1016/2010.09.001.
• 19. Rani, A., Singh, V. & Gupta, J.R.P. (2011). Soft Sensor based Adaptive Linear Network for Distillation Process. Inter. J. Comput. Applicat. 36, 39–45. DOI: 10.5120/4458-6244.
• 20. Canete, J.F. de, Orozco, P. del S., Gonzalez, S. & Moral, I.G. (2012). Dual composition control and soft estimation for a pilot distillation column using a neuro-genetic design. Comput. Chem. Engine. 40, 157–170. DOI: 10.1016/j.compchemeng.2012.01.003.
• 21. Sharma, N. & Singh, K. (2012). Model predictive control and neural network predictive control of TAME reactive distillation column. Chem. Engine. Proces. 1–13. DOI: 10.1016/j.cep.2012.05.003.
• 22. Rani, A., Singh, V. & Gupta J.R.P. (2013). Development of soft sensor for neural network based control of distillation column. ISA Transact. 52, 438–449. DOI: 10.1016/2012.12.009.
• 23. Duarte, C.F.M. (2006). Production of TAME and n-Propyl Propionate by Reactive Distillation, Doctoral Dissertation, Faculty of Engineering of the University of Porto, Porto, Portugal.
• 24. Buchaly, C., Kreis, P. & Gorak, A. (2007). Hybrid Separation Process- Combination of Reactive Distillation with Membrane Separation, Proceedings of European congress of Chemical Engineering (ECCE-6), 4–5.
• 25. Sivanandam, S.N. & Deepa, S.N. (2011). Principles of Soft Computing, second edition, Wiley India.
• 26. Astrom, K.J. & Hagglund, T. (1995). PID Controllers: Theory, Design and Tuning, second edition, ISA publicati on.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.