Startseite Estimator Based Inferential Control of an Ideal Quaternary Endothermic Reactive Distillation with Feed-Forward and Recurrent Neural Networks
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Estimator Based Inferential Control of an Ideal Quaternary Endothermic Reactive Distillation with Feed-Forward and Recurrent Neural Networks

  • Anish K Mathew und M V Pavan Kumar
Veröffentlicht/Copyright: 17. November 2017
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Abstract

A feed-forward neural network (FNN) and a layered recurrent neural network (LRNN) based two composition estimators, respectively, are designed for the purpose of tight product purity control for an ideal, quaternary, hypothetical, kinetically controlled, reactive distillation (RD) column. The output variables of the considered control structure i.e. the compositions, are estimated using the chosen tray temperatures as inputs to the estimators. The performances of the estimators in the control of the column for the servo, regulatory, feed impurity disturbances and catalyst deactivation are studied. The estimator based control is found to be effective for the on-spec product purity control. One-to-one relation between the number of tray temperature measurements and their sensitivity to the accuracy of estimation is observed. Overall, the performance of LRNN is found to be superior over the FNN for the throughput manipulations tested for the more number  of inputs to estimator.

Nomenclature

af

Pre-exponential factor, forward reaction

ab

Pre-exponential factor, backward reaction

L

[mol s−1] Reflux rate

R

Gas constant

rj

[mol s−1] Reaction rate on tray j

Tj

[K] Temperature of tray j

xC, D

[mole fraction] Mole fraction component C in distillate

xD, B

[mole fraction] Mole fraction component D in bottoms

xi,j

Liquid mole fraction of component i in tray j

zi

[mole fraction] Mole fraction of component i in feed stream

Abbreviations
MAE

[mole fraction × min] Mean absolute error

RMSE

Root Mean Square Error

TPM

Throughput Manipulation

ANN

Artificial Neural Network

References

[1] Sneesby MG, Tade MO, Datta R, Smith TN. ETBE Synthesis via Reactive Distillation. 1. Steady-State Simulation and Design Aspects. Ind Eng Chem Res. 1997;36(5):1855–69.10.1021/ie960283xSuche in Google Scholar

[2] Sneesby MG, Tade MO, Smith TN. Steady-state transitions in the reactive distillation of MTBE. Comput Chem Engg. 1998;22(7,8):879–92.10.1016/S0021-9290(97)00273-1Suche in Google Scholar

[3] Arfaj MA, Luyben WL,. Control Study of ETBE Reactive Distillation. Ind Eng Chem Res. 2002a;41:3784–96.10.1021/ie010432ySuche in Google Scholar

[4] Arfaj MA, Luyben WL,. Comparative control study of ideal and methyl acetate. Chem Eng Sci. 2002b;57:5039–50.10.1016/S0009-2509(02)00415-3Suche in Google Scholar

[5] Huss RS, Chen F, Malone MF, Doherty MF. Reactive distillation for methyl acetate production. Comput Chem Eng. 2003;27:1855–66.10.1016/S0098-1354(03)00156-XSuche in Google Scholar

[6] Ciric AR, Miao P,. Steady state multiplicities in an ethylene glycol reactive distillation column. Ind Eng Chem Res. 1994;33:2738–48.10.1021/ie00035a025Suche in Google Scholar

[7] Mohl KD, Kienle A, Gilles ED, Rapmund P, Sundmacher K, Hoffmann U,. Steady-state multiplicities in reactive distillation columns for the production of fuel ethers MTBE and TAME: theoretical analysis and experimental verification. Chem Eng Sci. 1999;54:1029–43.10.1016/S0009-2509(98)00327-3Suche in Google Scholar

[8] Chen F, Huss RS, Doherty MF, Malone MF,. Multiple steady states in reactive distillation: kinetic effects. Comput Chem Eng. 2002;26:81–93.10.1016/S0098-1354(01)00750-5Suche in Google Scholar

[9] Wang SJ, Wong DSH, Lee EK,. Effect of interaction multiplicity on control system design for a MTBE reactive distillation column. J Process Contr. 2003;13:503–15.10.1016/S0959-1524(02)00111-7Suche in Google Scholar

[10] Singh BP, Singh R, Kumar MVP, Kaistha N. Steady state analysis of reactive distillation using homotopy continuation. Chem Eng Res Des. 2005;83:959–68.10.1205/cherd.04279Suche in Google Scholar

[11] Kumar MVP, Kaistha N. Steady state multiplicity and its implications on the control of an ideal reactive distillation column. Ind Eng Chem Res. 2008a;47(8):2778–87.10.1021/ie701720rSuche in Google Scholar

[12] Arfaj MA, Luyben WL. Comparison of alternative control structures for an ideal two-product reactive distillation column. Ind Eng Chem Res. 2000;39:3298–307.10.1021/ie990886jSuche in Google Scholar

[13] Vora N, Daoutidis P,. Dynamics and control of an ethyl acetate reactive distillation column. Ind Eng Chem Res. 2001;40:833–49.10.1021/ie990633qSuche in Google Scholar

[14] Kaymak DB, Luyben WL,. Comparison of two types of two-temperature control structures for reactive distillation columns. Ind Eng Chem Res. 2005;44(13):4625–40.10.1021/ie058012mSuche in Google Scholar

[15] Lai I-K, Hung S-B, Hung W-J, Yu C-C, Lee M-J, Huang H-P. Design and control of reactive distillation for ethyl and isopropyl acetates production with azeotropic feeds. Chem Eng Sci. 2007;62:878 –98.10.1016/j.ces.2006.10.019Suche in Google Scholar

[16] Kumar MVP, Kaistha N. Decentralized control of a kinetically controlled ideal reactive distillation column. Chem Eng Sci. 2008b;63:228–43.10.1016/j.ces.2007.09.029Suche in Google Scholar

[17] Kaymak DB, Luyben WL. Evaluation of a two-temperature control structure for a two-reactant/two-product type of reactive distillation column. Chem Eng Sci. 2006;61:4432–50.10.1016/j.ces.2006.01.050Suche in Google Scholar

[18] Kienle A, Marquardt W. Nonlinear dynamics and control of reactive distillation process. In: Sundmacher K, Kienle A, editor(s). Reactive distillation: status and future directions. Weinheim: Wiley-VCH Verlag GmbH & Co. 2002.10.1002/3527600523Suche in Google Scholar

[19] Mathew AK, Kaistha N, Kumar MVP. Control of quaternary ideal endothermic reactive distillation with and without internal heat integration. Chem Eng Technol. 2016;39(4):775–85.10.1002/ceat.201400785Suche in Google Scholar

[20] Wang S-J, Wong DS, Lee EK. Control of a reactive distillation column in the kinetic regime for the synthesis of n-Butyl Acetate. Ind Eng Chem Res. 2003;42(21):5182–94.10.1021/ie0209172Suche in Google Scholar

[21] Mejdell T, Skogestad S. Estimation of distillation compositions from multiple temperature measurements using partial-least-squares regression. Ind Eng Chem Res. 1991;30:2543–55.10.1021/ie00060a007Suche in Google Scholar

[22] Mejdell T, Skogestad S,. Output estimation using multiple secondary measurements; high-purity distillation. AIChE J. 1993;39:10.10.1002/aic.690391008Suche in Google Scholar

[23] Kano M, Miyazaki K, Hasebe S, Hashimoto I. Inferential control system of distillation compositions using dynamic partial least squares regression. J Process Contr. 2000;10:157–66.10.1016/S0959-1524(99)00027-XSuche in Google Scholar

[24] Eliana Z, Barolo M, Seborg DE. Estimating product composition profiles in batch distillation via partial least squares regression. Control Eng Pract. 2004;12:917–29.10.1016/j.conengprac.2003.11.005Suche in Google Scholar

[25] Fortuna L, Graziani S, Xibilia MG. Soft sensors for product quality monitoring in debutanizer distillation columns. Control Eng Pract. 2005;13:499–508.10.1016/j.conengprac.2004.04.013Suche in Google Scholar

[26] Singh V, Gupta I, Gupta HO. ANN based estimator for distillation— inferential control. Chem Eng Process. 2005;44:785–95.10.1016/j.cep.2004.08.010Suche in Google Scholar

[27] Singh V, Gupta I, Gupta HO. ANN-based estimator for distillation using Levenberg–Marquardt approach. Eng Appl Artif Intel. 2007;20:249–59.10.1016/j.engappai.2006.06.017Suche in Google Scholar

[28] Bahar A, Kemal OZ, Leblebiciolu HU. Artificial neural network estimator design for the inferential model predictive control of an industrial distillation column. Ind Eng Chem Res. 2004;43:6102–11.10.1021/ie030585gSuche in Google Scholar

[29] Venkateswarlu C, Kumar JB,. Composition estimation of multicomponent reactive batch distillation with optimal sensor configuration. Chem Eng Sci. 2006;61:5560–74.10.1016/j.ces.2006.04.023Suche in Google Scholar

[30] Olanrewaju MJ, Arfaj MA. Estimator-based control of reactive distillation system: application of an extended Kalman filtering. Chem Eng Sci. 2006;61:3386–99.10.1016/j.ces.2005.12.009Suche in Google Scholar

[31] Bahar A, Kemal OZ, Leblebiciolu, Halc U. State estimation of a reactive batch distillation column. IFAC. 2008 July 6–11.10.3182/20080706-5-KR-1001.00561Suche in Google Scholar

[32] Raghavan SRV, Radhakrishnan TK, Srinivasan K,. Soft sensor based composition estimation and controller design for an ideal reactive distillation column. ISA Trans. 2011;50:61–70.10.1016/j.isatra.2010.09.001Suche in Google Scholar PubMed

[33] Sakhre V, Jain S, Sapkal VS, Agarwal DP,. Modified neural network based cascaded control for product composition of reactive distillation. Pol J Chem Tech. 2016;18:2.10.1515/pjct-2016-0037Suche in Google Scholar

[34] Rani A, Singh V, Gupta JRP. Development of soft sensor for neural network based control of distillation column. ISA Trans. 2013;52:438–49.10.1016/j.isatra.2012.12.009Suche in Google Scholar PubMed

[35] Ramli NM, Hussain MA, Jan BM. Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies. Neurocomputing. 2016;194:135–50.10.1016/j.neucom.2016.02.026Suche in Google Scholar

[36] Pani AK, Amin KG, Mohanta HK. Soft sensing of product quality in the debutanizer column with principal component analysis and feed-forward artificial neural network. Alexandria Eng J. 2016;55:1667–74.10.1016/j.aej.2016.02.016Suche in Google Scholar

[37] Singh AK, Tyagi B, Kumar V. Application of feed forward and recurrent neural network topologies for the modeling and identification of binary distillation column. IETE J Res. 2013;59(2):167–75.10.4103/0377-2063.113038Suche in Google Scholar

[38] Huang K, Iwakabe K, Nakaiwa M, Tsutsumi A. Towards further internal heat integration in design of reactive distillation columns—part I: the design principle. Chem Eng Sci. 2005;60(17):4901–14.10.1016/j.ces.2005.03.052Suche in Google Scholar

[39] Jhon YH, Lee T-H,. Dynamic simulation for reactive distillation with ETBE synthesis. Sep Purif Technol. 2003;31:301–17.10.1016/S1383-5866(02)00207-1Suche in Google Scholar

[40] Astrom KJ, Hagglund T,. Automatic tuning of simple controllers with specification on phase and amplitude margins. Automatica. 1984;20(5):645–51.10.1016/0005-1098(84)90014-1Suche in Google Scholar

[41] Tyreus BD, Luyben WL,. Tuning PI controllers for integrator/dead-time processes. Ind Eng Chem Res. 1992;31:2625–28.10.1021/ie00011a029Suche in Google Scholar

[42] Kadlec P, Gabrys B, Strandt S,. Data-driven Soft Sensors in the process industry. Comput Chem Eng. 2009;33:795–814.10.1016/j.compchemeng.2008.12.012Suche in Google Scholar

[43] Hagan MT, Menhaj M,. Training feed-forward networks with the Marquardt algorithm. IEEE Trans Neural Networks. 1994;6(5):989–93.10.1109/72.329697Suche in Google Scholar PubMed

[44] Hagan MT, Demuth HB, Beale, MH. Neural Network Design. Boston, MA: PWS publishing; 1996.Suche in Google Scholar

[45] Elman JL. Finding structure in time. Cogn Sci. 1990;14:179–211.10.1207/s15516709cog1402_1Suche in Google Scholar

[46] Tang W-S, Wang J. A two-layer recurrent neural network for real-time control of redundant manipulators with torque minimization. IEEE Int Conf SMC. 1998, 1998;2:1720–25.Suche in Google Scholar

[47] Medsker LR, Jain LC,. Recurrent neural network – design and applications. New York: CRC Press, 2001.Suche in Google Scholar

[48] Moore CF. Selection of controlled and manipulated variables. In: Luyben WL, editor(s). Practical distillation control. New York: Van Nostrand Reinhold, 1992Suche in Google Scholar

[49] Yu CC, Luyben WL,. Control of multicomponent distillation columns using rigorous composition estimators, distillation and absorption. Inst Chem Eng Symp Ser. 1987;104:29–69.Suche in Google Scholar

[50] Quintero-Marmol E, Luyben WL, Georgakis C,. Application of an extended Luenberger observer to the control of multicomponent batch distillation. Ind Chem Eng Res. 1991;30:1870–80.10.1021/ie00056a029Suche in Google Scholar

[51] Yildiz U, Gurkan UA, Ozgen C, Leblebicioglu K,. State estimator design for multicomponent batch distillation columns. Chem Eng Res Des. 2005;83:433–44.10.1205/cherd.03318Suche in Google Scholar

[52] Hagan MT, , Fun M-H. Levenberg-Marquardt training for modular networks. IEEE, Proceedings of International Conference on Neural Networks, Washington, DC. 1996;468–73.Suche in Google Scholar

[53] Ngia LSH, Sjöberg J. Efficient training of neural nets for nonlinear adaptive filtering using a recursive levenberg–marquardt algorithm. IEEE Trans Signal Process. 2000;48(7):1915–27.10.1109/78.847778Suche in Google Scholar

[54] Wilamowski BM, Iplikci S, Kaynak O, Efe MO. An algorithm for fast convergence in training neural networks. IEEE, Proceedings of International Joint Conference on Neural Networks, Washington, DC. 2001;1778–82.10.1109/IJCNN.2001.938431Suche in Google Scholar

[55] Lera G, Pinzolas M. Neighborhood based levenberg–marquardt algorithm for neural network training. IEEE Trans Neural Networks. 2002;13(5):1200–03.10.1109/TNN.2002.1031951Suche in Google Scholar PubMed

[56] Kermani BG, Schiffman SS, Nagle HT. Performance of the Levenberg–Marquardt neural network training method in electronic nose applications. Sensors and Actuators B. 2005;110:13–22.10.1016/j.snb.2005.01.008Suche in Google Scholar

[57] Wilamowski BM, Yu H. Improved computation for Levenberg–Marquardt. training. IEEE Trans Neural Networks. 2010;21(6):930–37.10.1109/TNN.2010.2045657Suche in Google Scholar PubMed

[58] Bascil MS, Temurtas F. A study on hepatitis disease diagnosis using multilayer neural network with Levenberg Marquardt training algorithm. J Med Syst. 2011;35:433–36.10.1007/s10916-009-9378-2Suche in Google Scholar PubMed

[59] Chan KY, Dillon TS, Singh J, Chang E,. Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm. IEEE Trans Intell Transp Syst. 2012;13(2):644–54.10.1109/TITS.2011.2174051Suche in Google Scholar

[60] Baptista D, Morgado-Dias F,. Comparing different implementations for the Levenberg-Marquardt algorithm. 10th Portuguese Conference on Automatic Control. 16–18 July 2012.Suche in Google Scholar

[61] Eğrioğlu E, Aladag CH, Gṳnay S. A new model selection strategy in artificial neural networks. Appl Math Comput. 2008;195:591–97.10.1016/j.amc.2007.05.005Suche in Google Scholar

[62] Kumar MVP, Mathew AK. Reaction-separation interaction on the control of ideal two-feed, two product exothermic reactive distillation columns. Can J Chem Eng. 2017;95:331–342.10.1002/cjce.22682Suche in Google Scholar

[63] Hu YH, Hwang J-N. Applications of artificial neural networks to time series prediction. In: Hu YH, Hwang J-N, editors. Handbook of neural network signal processing. New York: CRC Press, 2001.Suche in Google Scholar

Received: 2017-4-14
Revised: 2017-10-2
Accepted: 2017-10-2
Published Online: 2017-11-17

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