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Kesikli Bir Polimerizasyon Reaktörüne Farklı Optimal Şartlarda Sıcaklık Kontrolunun Uygulanması

Yıl 2013, Cilt: 28 Sayı: 2, 383 - 392, 27.03.2014

Öz

Genelleştirilmiş öngörmeli kontrol(GPC) ile genelleştirilmiş delta kuralı(GDK) izotermal koşullar altında stirenin polimerizasyonunun gerçekleştiği ceketli kesikli bir reaktörün sıcaklığını kontrol etmede kullanılmıştır. Monomer dönüşümü, viskozite ortalama molekül ağırlığı ve zincir uzunluğu üzerine değişik optimal koşulların etkileri incelenmiştir. Reaktör sıcaklığı ve reaktöre verilen ısı arasındaki etkileşime dayanan yapay sinir ağı modeli kullanılmıştır. GPC’li GDK’nın etkinliği belirlenen sabit sıcaklıklarda GDK parametreleri kullanılarak deneysel ve benzetim ile incelenmiştir. Sonuçlar Self-Tuning PID (STPID) yöntemi ile karsılaştırılmıştır. Kontrol deneyleri sonucunda GDK-GPC kontrol yönteminin iyi bir performans gösterdiği ve istenilen özelliklerde polimer elde edildiği gözlenmiştir. Ayrıca GDK-GPC yönteminin STPID yönteminden daha iyi olduğu hem kontrol performansından hem de elde edilen polimer özelliklerinden gözlenmektedir.

Kaynakça

  • Barner-Kowollik, C., & Davis, T.P., “Using
  • Kinetics and Thermodynamics in the controlled
  • synthesis of low molecular weight polymers in
  • free-radical polymerization”, Macromol. Theory
  • Simul., 10, 255, 2001.
  • Scali, C., Morretta, M., & Semino, D., “Control of
  • the quality of polymer products in continuous
  • reactors: comparison of performance of state
  • estimators with and without updating of
  • parameters”, J. Proc. Cont., 7, 357, 1997.
  • Erdoğan, S., Alpbaz, M., & Karagöz, A.R., “The
  • effect of operational conditions on the
  • performance of batch polymerization reactor
  • control”, Chem. Eng. J., 86, 259, 2002.
  • Yang, J., Li, X., Mou, H.-G., & Jian, L., “Control-Oriented Thermal Management of Solid Oxide Fuel Cells Based on a Modified Takagi–Sugeno Fuzzy Model”, Journal of Power Sources, 188, 475-482, 2009.
  • Leite, M. S., Santos, B. F., Lona, L. M. F., Silva, F. V., & Fileti, A. M. F., “Application of Artificial Intelligence Techniques for Temperature Prediction in a Polymerization Process”, Chemical Engineering Transactions, 24, 385-
  • , 2011.
  • Richards, J.R. & Congalidis, J.P., “Measurement and control polymerization reactors”, Comput. Chem. Eng., 30, 1447, 2006.
  • Seki, H., Ogawa, M., Ooyama, S., Akamatsu, K., Ohshima, M., & Yang, W., “Industrial application of a nonlinear model predictive control to polymerization reactors”, Cont. Eng. Pract., 9, 819, 2001.
  • Xaumier, F., Lann, M-V.L., Cabassud, M., & Casamatta, G., “Experimental application of nonlinear model predictive control: temperature control of an industrial semi-batch pilot-plant reactor”, J. Proc. Cont., 12, 687, 2002.
  • Altınten, A., Erdoğan, E., Hapoğlu, H., Alıev, F., & Alpbaz, M., “Application of fuzzy control metod with genetic algorithm to a polymerization reactor at constant set point”, Chem. Eng. Res. Des. (IChemE), 84(A11), 1012, 2006.
  • Altınten, A., Erdoğan, E., Hapoğlu, H., & Alpbaz, M., “Control of a polymerization reactor by fuzzy control method with genetic algorithm”, Comp. Chem. Eng., 27, 1031, 2003.
  • Erdoğan, S., Özkan, G., & Alpbaz, M., “Self-Tuning control of batch polymerization reactor”, J. Chem. Eng. Japan, 31, 499, 1998.
  • Lewis, G.T., Nguyen, V., & Cohen, Y., “Synthesis of poly(4-vinylpyridine) by reverse atom transfer radical polymerization”, J. Polym. Sci., 45 (Part A), 5748, 2007.
  • Altınten, A., Ketevanlioğlu, F., Erdoğan, S., Hapoğlu, H., & Alpbaz, M., “Self-tuning PID control of jacketed batch polystyrene reactor using genetic algorithm”, Chem. Eng. J., 138, 490, 2008.
  • Zhang, J., Morris, A.J., Martin, E.B., & Kiparissides, C., “Prediction of polymer quality in batch polymerization reactors using robust neural network”, Chemical Eng. J., 69, 135, 1998.
  • Yang, S.H., Chung, P.W.H., & Brooks, B.W., “Multi-stage modelling of a semi-batch polymerization reactor using artificial neural networks”, Trans IChemE, 77 (Part A), 779, 1999.
  • Zeybek, Z., “Role of adaptive heuristic criticism in cascade temperature control of an industrial tubular furnace”, Applied Thermal Engineering, 26, 152, 2006.
  • Özkan, G., Hapoğlu, H., & Alpbaz, M., “Generalized predictive control of optimal temperature profiles in a polystyrene polymerization reactor”, Chemical Engineering and Processing, 37, 125, 1998.
  • Yüce (Çetinkaya), S., Nonlinear Model Predictive Control of Reactor Temperature in
  • Agitated Batch Polymerization Reactor Operating Optimal Condition, Ph.D Thesis, Department of Chemical Engineering, University of Ankara, Turkey, 2001.
  • Zeybek, Z., Çetinkaya, S., Hapoğlu, H., & Alpbaz, M., “Generalized delta rule (GDR) algorithm with generalized predictive control (GPC) for optimum temperature tracking of batch polymerization”, Chem. Eng. Sci., 61, 6691, 2006.
  • Hsu, K-Y., & Chen, S-A., “Optimal piecewise constant temperature policies for batchwise thermally initiated bulk polymerization of styrene”, Journal of the Chinese Institute of Chemical Engineers, 17(5), 315, 1986.
  • Zaheer-uddin, M., & Tudoroiu, N., “Neuro-PID tracking control of a discharge air temperature system”, Energy Conversion and Management, 45, 2405, 2004.
  • Ekpo, E.E., & Mujtaba, I.M., “Evaluation of neural networks-based controllers in batch polymerization of methyl methacrylate”, Neurocomputing, 71, 1401, 2008.
  • Valigi, P., Fravolini, M.L., & Ficola, A., “Improved temperature control of a batch reactor with actuation constraints”, Cont. Eng. Pract., 14, 783, 2006.
  • Yüce, S., Hasaltun, A., Erdoğan, S., & Alpbaz, M., “Temperature control of a batch polymerization reactor”, Trans IChemE, 77 Part A, 413, 1999.
  • Chen, S.A., & Jeng, W.F., “Minimum end time policies for batch-wise radical polymerization”, Chem Eng Sci., 33, 735, 1978.
  • Karagöz, A.R., Özkan, G., Erdoğan, S., & Alpbaz, M., “Modeling optimization and control of batch polymerization reactors”, Control and Computers, 25(1), 21, 1997.
  • Rumelhart, D.E., Hinton, G.E., & Williams, R.J., “Learning representations by backpropagation errors”, Nature, 323, 533, 1986.
  • Werbos, P., Beyond Regression: New tools for predicting and analysis in the behavioral scienses, Ph.D Thesis, Cambridge, MA: Harvard University Committee on Applied Mathematics, 1974.
  • Parker, D., “Learning-logic”, invention report S81-64, file 1, Office of Technology Licensing, Stanford University, Stanford, CA, 1982.
  • LeCun, Y., “Learning process in an asymmetric threshold network in disordered systems and biological organization”, Les Houches, France, 223, 1986.
  • Widrow, B. & Hoff, M., “Adaptive switching circuits, In Western Electronic Show and Convention”, Institute of Radio Engineers (IEEE), 4, 96, 1960.
  • Lu, Y-Z., Industrial intelligent control, fundamentals and applications, 1. Edition, 1996.
  • Çetinkaya, S., Dynamic matrix control of a batch polymerization reactor under the optimal conditions, M.Sc. Thesis, Department of Chemical Engineering, Ankara University, Turkey, 1996.

APPLICATION OF TEMPERATURE CONTROL IN A BATCH POLYMERIZATION REACTOR AT DIFFERENT OPTIMAL TEMPERATURES

Yıl 2013, Cilt: 28 Sayı: 2, 383 - 392, 27.03.2014

Öz

The generalized delta rule (GDR) algorithm with generalized predictive control (GPC) was used to control the
temperature of a jacketed batch reactor in which styrene polymerization occurs under isothermal conditions. The
effects of different optimal conditions were examined on monomer conversion, average viscosity molecular
weight and chain length. The neural network model based on the relation between the reactor temperature and
heat input to the reactor was used. The efficiency of the GDR with GPC was examined by simulation and
experimentally using GDR parameters specified at constant temperatures, and compared with Self-Tuning PID
(STPID). It was observed that the control experiments provided a good performance in maintaining the reactor
temperature at its set point and yielded polymer product with desired properties. 

Kaynakça

  • Barner-Kowollik, C., & Davis, T.P., “Using
  • Kinetics and Thermodynamics in the controlled
  • synthesis of low molecular weight polymers in
  • free-radical polymerization”, Macromol. Theory
  • Simul., 10, 255, 2001.
  • Scali, C., Morretta, M., & Semino, D., “Control of
  • the quality of polymer products in continuous
  • reactors: comparison of performance of state
  • estimators with and without updating of
  • parameters”, J. Proc. Cont., 7, 357, 1997.
  • Erdoğan, S., Alpbaz, M., & Karagöz, A.R., “The
  • effect of operational conditions on the
  • performance of batch polymerization reactor
  • control”, Chem. Eng. J., 86, 259, 2002.
  • Yang, J., Li, X., Mou, H.-G., & Jian, L., “Control-Oriented Thermal Management of Solid Oxide Fuel Cells Based on a Modified Takagi–Sugeno Fuzzy Model”, Journal of Power Sources, 188, 475-482, 2009.
  • Leite, M. S., Santos, B. F., Lona, L. M. F., Silva, F. V., & Fileti, A. M. F., “Application of Artificial Intelligence Techniques for Temperature Prediction in a Polymerization Process”, Chemical Engineering Transactions, 24, 385-
  • , 2011.
  • Richards, J.R. & Congalidis, J.P., “Measurement and control polymerization reactors”, Comput. Chem. Eng., 30, 1447, 2006.
  • Seki, H., Ogawa, M., Ooyama, S., Akamatsu, K., Ohshima, M., & Yang, W., “Industrial application of a nonlinear model predictive control to polymerization reactors”, Cont. Eng. Pract., 9, 819, 2001.
  • Xaumier, F., Lann, M-V.L., Cabassud, M., & Casamatta, G., “Experimental application of nonlinear model predictive control: temperature control of an industrial semi-batch pilot-plant reactor”, J. Proc. Cont., 12, 687, 2002.
  • Altınten, A., Erdoğan, E., Hapoğlu, H., Alıev, F., & Alpbaz, M., “Application of fuzzy control metod with genetic algorithm to a polymerization reactor at constant set point”, Chem. Eng. Res. Des. (IChemE), 84(A11), 1012, 2006.
  • Altınten, A., Erdoğan, E., Hapoğlu, H., & Alpbaz, M., “Control of a polymerization reactor by fuzzy control method with genetic algorithm”, Comp. Chem. Eng., 27, 1031, 2003.
  • Erdoğan, S., Özkan, G., & Alpbaz, M., “Self-Tuning control of batch polymerization reactor”, J. Chem. Eng. Japan, 31, 499, 1998.
  • Lewis, G.T., Nguyen, V., & Cohen, Y., “Synthesis of poly(4-vinylpyridine) by reverse atom transfer radical polymerization”, J. Polym. Sci., 45 (Part A), 5748, 2007.
  • Altınten, A., Ketevanlioğlu, F., Erdoğan, S., Hapoğlu, H., & Alpbaz, M., “Self-tuning PID control of jacketed batch polystyrene reactor using genetic algorithm”, Chem. Eng. J., 138, 490, 2008.
  • Zhang, J., Morris, A.J., Martin, E.B., & Kiparissides, C., “Prediction of polymer quality in batch polymerization reactors using robust neural network”, Chemical Eng. J., 69, 135, 1998.
  • Yang, S.H., Chung, P.W.H., & Brooks, B.W., “Multi-stage modelling of a semi-batch polymerization reactor using artificial neural networks”, Trans IChemE, 77 (Part A), 779, 1999.
  • Zeybek, Z., “Role of adaptive heuristic criticism in cascade temperature control of an industrial tubular furnace”, Applied Thermal Engineering, 26, 152, 2006.
  • Özkan, G., Hapoğlu, H., & Alpbaz, M., “Generalized predictive control of optimal temperature profiles in a polystyrene polymerization reactor”, Chemical Engineering and Processing, 37, 125, 1998.
  • Yüce (Çetinkaya), S., Nonlinear Model Predictive Control of Reactor Temperature in
  • Agitated Batch Polymerization Reactor Operating Optimal Condition, Ph.D Thesis, Department of Chemical Engineering, University of Ankara, Turkey, 2001.
  • Zeybek, Z., Çetinkaya, S., Hapoğlu, H., & Alpbaz, M., “Generalized delta rule (GDR) algorithm with generalized predictive control (GPC) for optimum temperature tracking of batch polymerization”, Chem. Eng. Sci., 61, 6691, 2006.
  • Hsu, K-Y., & Chen, S-A., “Optimal piecewise constant temperature policies for batchwise thermally initiated bulk polymerization of styrene”, Journal of the Chinese Institute of Chemical Engineers, 17(5), 315, 1986.
  • Zaheer-uddin, M., & Tudoroiu, N., “Neuro-PID tracking control of a discharge air temperature system”, Energy Conversion and Management, 45, 2405, 2004.
  • Ekpo, E.E., & Mujtaba, I.M., “Evaluation of neural networks-based controllers in batch polymerization of methyl methacrylate”, Neurocomputing, 71, 1401, 2008.
  • Valigi, P., Fravolini, M.L., & Ficola, A., “Improved temperature control of a batch reactor with actuation constraints”, Cont. Eng. Pract., 14, 783, 2006.
  • Yüce, S., Hasaltun, A., Erdoğan, S., & Alpbaz, M., “Temperature control of a batch polymerization reactor”, Trans IChemE, 77 Part A, 413, 1999.
  • Chen, S.A., & Jeng, W.F., “Minimum end time policies for batch-wise radical polymerization”, Chem Eng Sci., 33, 735, 1978.
  • Karagöz, A.R., Özkan, G., Erdoğan, S., & Alpbaz, M., “Modeling optimization and control of batch polymerization reactors”, Control and Computers, 25(1), 21, 1997.
  • Rumelhart, D.E., Hinton, G.E., & Williams, R.J., “Learning representations by backpropagation errors”, Nature, 323, 533, 1986.
  • Werbos, P., Beyond Regression: New tools for predicting and analysis in the behavioral scienses, Ph.D Thesis, Cambridge, MA: Harvard University Committee on Applied Mathematics, 1974.
  • Parker, D., “Learning-logic”, invention report S81-64, file 1, Office of Technology Licensing, Stanford University, Stanford, CA, 1982.
  • LeCun, Y., “Learning process in an asymmetric threshold network in disordered systems and biological organization”, Les Houches, France, 223, 1986.
  • Widrow, B. & Hoff, M., “Adaptive switching circuits, In Western Electronic Show and Convention”, Institute of Radio Engineers (IEEE), 4, 96, 1960.
  • Lu, Y-Z., Industrial intelligent control, fundamentals and applications, 1. Edition, 1996.
  • Çetinkaya, S., Dynamic matrix control of a batch polymerization reactor under the optimal conditions, M.Sc. Thesis, Department of Chemical Engineering, Ankara University, Turkey, 1996.
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mimarlık
Bölüm Makaleler
Yazarlar

Sevil Çetinkaya Bu kişi benim

Hasan H. Durmazuçar Bu kişi benim

Zehra Zeybek Bu kişi benim

Hale Hapoğlu Bu kişi benim

Mustafa Alpbaz Bu kişi benim

Yayımlanma Tarihi 27 Mart 2014
Gönderilme Tarihi 27 Mart 2014
Yayımlandığı Sayı Yıl 2013 Cilt: 28 Sayı: 2

Kaynak Göster

APA Çetinkaya, S., Durmazuçar, H. H., Zeybek, Z., Hapoğlu, H., vd. (2014). Kesikli Bir Polimerizasyon Reaktörüne Farklı Optimal Şartlarda Sıcaklık Kontrolunun Uygulanması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 28(2), 383-392.
AMA Çetinkaya S, Durmazuçar HH, Zeybek Z, Hapoğlu H, Alpbaz M. Kesikli Bir Polimerizasyon Reaktörüne Farklı Optimal Şartlarda Sıcaklık Kontrolunun Uygulanması. GUMMFD. Şubat 2014;28(2):383-392.
Chicago Çetinkaya, Sevil, Hasan H. Durmazuçar, Zehra Zeybek, Hale Hapoğlu, ve Mustafa Alpbaz. “Kesikli Bir Polimerizasyon Reaktörüne Farklı Optimal Şartlarda Sıcaklık Kontrolunun Uygulanması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 28, sy. 2 (Şubat 2014): 383-92.
EndNote Çetinkaya S, Durmazuçar HH, Zeybek Z, Hapoğlu H, Alpbaz M (01 Şubat 2014) Kesikli Bir Polimerizasyon Reaktörüne Farklı Optimal Şartlarda Sıcaklık Kontrolunun Uygulanması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 28 2 383–392.
IEEE S. Çetinkaya, H. H. Durmazuçar, Z. Zeybek, H. Hapoğlu, ve M. Alpbaz, “Kesikli Bir Polimerizasyon Reaktörüne Farklı Optimal Şartlarda Sıcaklık Kontrolunun Uygulanması”, GUMMFD, c. 28, sy. 2, ss. 383–392, 2014.
ISNAD Çetinkaya, Sevil vd. “Kesikli Bir Polimerizasyon Reaktörüne Farklı Optimal Şartlarda Sıcaklık Kontrolunun Uygulanması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 28/2 (Şubat 2014), 383-392.
JAMA Çetinkaya S, Durmazuçar HH, Zeybek Z, Hapoğlu H, Alpbaz M. Kesikli Bir Polimerizasyon Reaktörüne Farklı Optimal Şartlarda Sıcaklık Kontrolunun Uygulanması. GUMMFD. 2014;28:383–392.
MLA Çetinkaya, Sevil vd. “Kesikli Bir Polimerizasyon Reaktörüne Farklı Optimal Şartlarda Sıcaklık Kontrolunun Uygulanması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 28, sy. 2, 2014, ss. 383-92.
Vancouver Çetinkaya S, Durmazuçar HH, Zeybek Z, Hapoğlu H, Alpbaz M. Kesikli Bir Polimerizasyon Reaktörüne Farklı Optimal Şartlarda Sıcaklık Kontrolunun Uygulanması. GUMMFD. 2014;28(2):383-92.