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Çok değişkenli proseste doğrusal olmayan GPC ve kesikli-zaman PID'nin deneysel performansı: Atık yemeklik yağdan biyodizel sentezinde tepkimeli damıtma kolonunun sıcaklık kontrolünde NARIMAX ve ARX modelleri

Yıl 2022, Cilt: 28 Sayı: 7, 977 - 986, 30.12.2022

Öz

Genelleştirilmiş Öngörülü Kontrol (GPC), çok değişkenli prosesi etkili bir şekilde kontrol edebilme avantajına sahip popüler bir Model Öngörülü Kontrol algoritmasıdır. Bu çalışmada, atık yemeklik yağdan kalsiyum-oksit katalizörlü biyodizel sentezinde tepkimeli damıtma kolonu prosesinin, NARIMAX model tabanlı doğrusal olmayan GPC ve ARX model tabanlı kesikli-zaman PID kontrol ile deneysel sıcaklık denetimi incelenmiştir. Deneyler öncesinde, tüm parametrelerin sıcaklık ve biyodizel mol kesri üzerine etkileri HYSYS simülasyonu ile analiz edilmiştir. Müteakiben, WCO akış hızı ve kazan ısı yükü ayar değişkenleri ve MATLAB'da geliştirilen algoritmalar ve kodlar yardımıyla denetim çalışmaları gerçekleştirilmiştir. SISO deneylerinde, ilgili ayar değişkeni ile kontrol edilen her bir bölgede önemli seviyede yakınsak sıcaklık cevapları elde edilmiştir. MIMO deneyler açısından ise, ayırımsız doğrusal olmayan GPC haricinde, önerilen tüm yöntemlerde en nihayetinde ayar noktalarına yakınsama olduğu tespit edilmiştir, ancak en iyi performans, daha az şiddetli etkileşime sahip, daha küçük yerleşme zamanlı, salınım göstermeyen ve daha düşük IAE ve ISE’ye sahip ayırımlı doğrusal olmayan GPC ile elde edilmiştir.

Kaynakça

  • [1] Goli J, Sahu O. “Development of heterogeneous alkali catalyst from waste chicken eggshell for biodiesel production”. Renewable Energy, 128, 142-154, 2018.
  • [2] Thokchom SS, Tikendra NV. “An assessment study of using Turel Kongreng (river mussels) as a source of heterogeneous catalyst for biofuel production”. Biocatalysis and Agricultural Biotechnology, 20, 1-5, 2019.
  • [3] Fonseca JM, Teleken JG, de Cinque Almeida V, da Silva C. “Biodiesel from waste frying oils: methods of production and purification”. Energy Conversion and Management, 184, 205-218, 2019.
  • [4] Milano J, Ong HC, Masjuki HH, Silitonga AS, Chen WH, Kusumo F, Dharma S, Sebayang AH. “Optimization of biodiesel production by microwave irradiation−assisted transesterification for waste cooking oil Calophyllum inophyllum oil via response surface methodology”. Energy Conversion and Management, 158, 400-415, 2018.
  • [5] Ruhul AM, Kalam MA, Masjuki HH, Fattah İR, Reham SS, Rashed MM. “State of the art of biodiesel production processes: a review of the heterogeneous catalyst”. RSC Advances, 5, 101023-101044, 2015.
  • [6] Boey PL, Maniam GP, Hamid SA. “Performance of calcium oxide as a heterogeneous catalyst in biodiesel production: a review”. Chemical Engineering Journal, 168, 15-22, 2011.
  • [7] Ling JSJ, Tan YH, Mubarak NM, Kansedo J, Saptoro A, Nolasco–Hipolito C. “A review of heterogeneous calcium oxide–based catalyst from waste for biodiesel synthesis”. SN Applied Sciences, 1, 810, 2019.
  • [8] Pradana YS, Hidayat A, Prasetya A, Budiman A. “Biodiesel production in a reactive distillation column catalyzed by heterogeneous potassium catalyst”. Energy Procedia, 143, 742-747, 2017.
  • [9] Talebian–Kiakalaieh A, Amin NAS, Mazaheri H. “A review on novel processes of biodiesel production from waste cooking oil”. Applied Energy, 104, 683-710, 2013.
  • [10] Wang J, Ge X, Wang Z, Jin Y. “Experimental studies on the catalytic distillation for hydrolysis of methyl acetate”. Chemical Engineering & Technology, 24, 155-159, 2001.
  • [11] Alaei HK, Salahshoor K, Alaei HK. “Model predictive control of distillation column based recursive parameter estimation method using HYSYS simulation”. International Conference on Intelligent Computing and Cognitive Informatics, Kuala Lumpur, Malaysia, 22-23 June 2010.
  • [12] Cong L, Liu X, Zhou Y, Sun Y. “Generalized generic model control of high‐purity internal thermally coupled distillation column based on nonlinear wave theory”. AIChE Journal, 59, 4133-4141, 2013.
  • [13] Liu X, Cong L, Zhou Y. “Nonlinear model predictive control based on wave model of high-purity internal thermally coupled distillation columns”. Industrial & Engineering Chemistry Research, 52, 6470-6479, 2013.
  • [14] Regalado-Méndez A, Romero R, Natividad R, Skogestad S. “Plant-wide control of a reactive distillation column on biodiesel production”. In Computer Science On−line Conference, Springer, Cham, 27-30 April 2016.
  • [15] Cheng Y, Chen Z, Sun M, Sun Q. “Active disturbance rejection generalized predictive control for a high purity distillation column process with time delay”. The Canadian Journal of Chemical Engineering, 97, 2941-2951, 2019.
  • [16] Giwa SO, Adeyi AA, Giwa A. “Application of model predictive control to renewable energy development via reactive distillation process”. International Journal of Engineering Research in Africa, 27, 95-110, 2016.
  • [17] Wu S. “Multivariable PID control using improved state space model predictive control optimization”. Industrial & Engineering Chemistry Research, 54, 5505-5513, 2015.
  • [18] Saravanakumar G, Valarmathi K, Iruthayarajan MW, Srinivasan S. “Lagrangian–based state transition algorithm for tuning multivariable decentralised controller”. International Journal of Advanced Intelligence Paradigms, 8, 303-317, 2016.
  • [19] Abraham A, Pappa N, Priya MS, Mary Hexy M. “Predictive control design for a MIMO multivariable process using order reduction techniques”. International Journal of Modelling and Simulation, 37, 199-207, 2017.
  • [20] Hadian M, Mehrshadian M, Karami M, Makvand AB. “Event–based neural network predictive controller application for a distillation column”. Asian Journal of Control, 23, 811-823, 2021.
  • [21] Shin Y, Smith R, Hwang S. “Development of model predictive control system using an artificial neural network: A case study with a distillation column”. Journal of Cleaner Production, 277, 1-14, 2020.
  • [22] Cheng Y, Chen Z, Sun M, Sun Q. “Decoupling control of high-purity heat integrated distillation column process via active disturbance rejection control and nonlinear wave theory”. Transactions of the Institute of Measurement and Control, 42, 2221-2233, 2020.
  • [23] Karacan S, Hapoǧlu H, Alpbaz M. “Application of optimal adaptive generalized predictive control to a packed distillation column”. Chemical Engineering Journal, 84, 389-396,2001.
  • [24] Hapoglu H, Karacan S, Koca ZE, Alpbaz M. “Parametric and nonparametric model−based control of a packed distillation column”. Chemical Engineering and Processing: Process Intensification, 40, 537-544, 2001.
  • [25] Karacan S. “Application of a nonlinear long range predictive control to a packed distillation column”. Chemical Engineering and Processing: Process Intensification, 42, 943-953, 2003.
  • [26] Mahfouf M, Abbod MF, Linkens DA. “Multivariable adaptive fuzzy TSK model−based predictive control with feedforward”. European Control Conference (ECC) IEEE, Porto, Portugal, 4-7 September 2001.
  • [27] Karacan S, Hapoğlu H, Alpbaz M. “Multivariable system identification and generic model control of a laboratory scale packed distillation column”. Applied Thermal Engineering, 27, 1017-1028, 2007.
  • [28] Marangoni C, Teleken JG, Werle LO, Machado RA, Bolzan A. “Multivariable control with adjustment by decoupling using a distributed action approach in a distillation column”. IFAC Proceedings Volumes, 42, 857-862, 2009.
  • [29] Liu X, Wang C, Cong L, Ding F. “Adaptive generalised predictive control of high purity internal thermally coupled distillation column”. The Canadian Journal of Chemical Engineering, 90, 420-428, 2012.
  • [30] Haßkerl D, Lindscheid C, Subramanian S, Markert S, Górak A, Engell S. “Dynamic performance optimization of a pilotscale reactive distillation process by economics optimizing control”. Industrial & Engineering Chemistry Research, 57, 12165-12181, 2018.
  • [31] Yadav ES, Indiran T, Priya SS. “System identification and conditional control for an optimal operation of a pilot plant binary distillation column”. International Journal of Computing and Digital Systems, 9, 139-146, 2020.
  • [32] Çağatay MT, Karacan S. “Multivariable generalized predictive control of reactive distillation column process for biodiesel production”. Turkish Journal of Engineering, 6, 40-53, 2022.
  • [33] Karacan S, Çağatay MT. “Transesterification of waste cooking oil into biodiesel using Aspen HYSYS”. International Journal of Scientific Research in Science and Technology, 3, 83-87, 2017.
  • [34] Karacan S, Çağatay MT. “Simulation and optimization of reactive packed distillation column for biodiesel production using heterogeneous catalyst”. International Journal of Energy Applications and Technologies, 5, 153-160, 2018.
  • [35] Çağatay MT, Çağatay Ş, Karacan S. “Optimisation of biodiesel synthesis from waste cooking oil in the reactive distillation column using Taguchi methodology”. Journal of Polytechnic, 24, 175-186, 2021.

Experimental performances of nonlinear GPC and discrete-time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil

Yıl 2022, Cilt: 28 Sayı: 7, 977 - 986, 30.12.2022

Öz

Generalized Predictive Control (GPC) is a popular Model Predictive Control algorithm that has the advantage of effectively managing the multivariate process. In this study, experimental temperature control of the reactive distillation column process in calcium−oxide catalyzed biodiesel synthesis from waste cooking oil was investigated using nonlinear GPC based on NARIMAX model and discrete time PID control based on ARX model. Before the experiments, the effects of all parameters on temperature and biodiesel mole fraction were analyzed by HYSYS simulation. Afterwards, control studies were carried out with the help of WCO flow rate and reboiler heat duty manipulating variables and algorithms and codes developed in MATLAB. In the SISO experiments, significant convergent temperature responses were obtained in each region controlled by the relevant manipulating variable. Regarding MIMO experiments, all proposed methods except the non-decoupled nonlinear GPC were found to converge ultimately to their setpoints, but the best performance was achieved in decoupled nonlinear GPC with less severe interaction, smaller settling time, no oscillations and lower IAE and ISE.

Kaynakça

  • [1] Goli J, Sahu O. “Development of heterogeneous alkali catalyst from waste chicken eggshell for biodiesel production”. Renewable Energy, 128, 142-154, 2018.
  • [2] Thokchom SS, Tikendra NV. “An assessment study of using Turel Kongreng (river mussels) as a source of heterogeneous catalyst for biofuel production”. Biocatalysis and Agricultural Biotechnology, 20, 1-5, 2019.
  • [3] Fonseca JM, Teleken JG, de Cinque Almeida V, da Silva C. “Biodiesel from waste frying oils: methods of production and purification”. Energy Conversion and Management, 184, 205-218, 2019.
  • [4] Milano J, Ong HC, Masjuki HH, Silitonga AS, Chen WH, Kusumo F, Dharma S, Sebayang AH. “Optimization of biodiesel production by microwave irradiation−assisted transesterification for waste cooking oil Calophyllum inophyllum oil via response surface methodology”. Energy Conversion and Management, 158, 400-415, 2018.
  • [5] Ruhul AM, Kalam MA, Masjuki HH, Fattah İR, Reham SS, Rashed MM. “State of the art of biodiesel production processes: a review of the heterogeneous catalyst”. RSC Advances, 5, 101023-101044, 2015.
  • [6] Boey PL, Maniam GP, Hamid SA. “Performance of calcium oxide as a heterogeneous catalyst in biodiesel production: a review”. Chemical Engineering Journal, 168, 15-22, 2011.
  • [7] Ling JSJ, Tan YH, Mubarak NM, Kansedo J, Saptoro A, Nolasco–Hipolito C. “A review of heterogeneous calcium oxide–based catalyst from waste for biodiesel synthesis”. SN Applied Sciences, 1, 810, 2019.
  • [8] Pradana YS, Hidayat A, Prasetya A, Budiman A. “Biodiesel production in a reactive distillation column catalyzed by heterogeneous potassium catalyst”. Energy Procedia, 143, 742-747, 2017.
  • [9] Talebian–Kiakalaieh A, Amin NAS, Mazaheri H. “A review on novel processes of biodiesel production from waste cooking oil”. Applied Energy, 104, 683-710, 2013.
  • [10] Wang J, Ge X, Wang Z, Jin Y. “Experimental studies on the catalytic distillation for hydrolysis of methyl acetate”. Chemical Engineering & Technology, 24, 155-159, 2001.
  • [11] Alaei HK, Salahshoor K, Alaei HK. “Model predictive control of distillation column based recursive parameter estimation method using HYSYS simulation”. International Conference on Intelligent Computing and Cognitive Informatics, Kuala Lumpur, Malaysia, 22-23 June 2010.
  • [12] Cong L, Liu X, Zhou Y, Sun Y. “Generalized generic model control of high‐purity internal thermally coupled distillation column based on nonlinear wave theory”. AIChE Journal, 59, 4133-4141, 2013.
  • [13] Liu X, Cong L, Zhou Y. “Nonlinear model predictive control based on wave model of high-purity internal thermally coupled distillation columns”. Industrial & Engineering Chemistry Research, 52, 6470-6479, 2013.
  • [14] Regalado-Méndez A, Romero R, Natividad R, Skogestad S. “Plant-wide control of a reactive distillation column on biodiesel production”. In Computer Science On−line Conference, Springer, Cham, 27-30 April 2016.
  • [15] Cheng Y, Chen Z, Sun M, Sun Q. “Active disturbance rejection generalized predictive control for a high purity distillation column process with time delay”. The Canadian Journal of Chemical Engineering, 97, 2941-2951, 2019.
  • [16] Giwa SO, Adeyi AA, Giwa A. “Application of model predictive control to renewable energy development via reactive distillation process”. International Journal of Engineering Research in Africa, 27, 95-110, 2016.
  • [17] Wu S. “Multivariable PID control using improved state space model predictive control optimization”. Industrial & Engineering Chemistry Research, 54, 5505-5513, 2015.
  • [18] Saravanakumar G, Valarmathi K, Iruthayarajan MW, Srinivasan S. “Lagrangian–based state transition algorithm for tuning multivariable decentralised controller”. International Journal of Advanced Intelligence Paradigms, 8, 303-317, 2016.
  • [19] Abraham A, Pappa N, Priya MS, Mary Hexy M. “Predictive control design for a MIMO multivariable process using order reduction techniques”. International Journal of Modelling and Simulation, 37, 199-207, 2017.
  • [20] Hadian M, Mehrshadian M, Karami M, Makvand AB. “Event–based neural network predictive controller application for a distillation column”. Asian Journal of Control, 23, 811-823, 2021.
  • [21] Shin Y, Smith R, Hwang S. “Development of model predictive control system using an artificial neural network: A case study with a distillation column”. Journal of Cleaner Production, 277, 1-14, 2020.
  • [22] Cheng Y, Chen Z, Sun M, Sun Q. “Decoupling control of high-purity heat integrated distillation column process via active disturbance rejection control and nonlinear wave theory”. Transactions of the Institute of Measurement and Control, 42, 2221-2233, 2020.
  • [23] Karacan S, Hapoǧlu H, Alpbaz M. “Application of optimal adaptive generalized predictive control to a packed distillation column”. Chemical Engineering Journal, 84, 389-396,2001.
  • [24] Hapoglu H, Karacan S, Koca ZE, Alpbaz M. “Parametric and nonparametric model−based control of a packed distillation column”. Chemical Engineering and Processing: Process Intensification, 40, 537-544, 2001.
  • [25] Karacan S. “Application of a nonlinear long range predictive control to a packed distillation column”. Chemical Engineering and Processing: Process Intensification, 42, 943-953, 2003.
  • [26] Mahfouf M, Abbod MF, Linkens DA. “Multivariable adaptive fuzzy TSK model−based predictive control with feedforward”. European Control Conference (ECC) IEEE, Porto, Portugal, 4-7 September 2001.
  • [27] Karacan S, Hapoğlu H, Alpbaz M. “Multivariable system identification and generic model control of a laboratory scale packed distillation column”. Applied Thermal Engineering, 27, 1017-1028, 2007.
  • [28] Marangoni C, Teleken JG, Werle LO, Machado RA, Bolzan A. “Multivariable control with adjustment by decoupling using a distributed action approach in a distillation column”. IFAC Proceedings Volumes, 42, 857-862, 2009.
  • [29] Liu X, Wang C, Cong L, Ding F. “Adaptive generalised predictive control of high purity internal thermally coupled distillation column”. The Canadian Journal of Chemical Engineering, 90, 420-428, 2012.
  • [30] Haßkerl D, Lindscheid C, Subramanian S, Markert S, Górak A, Engell S. “Dynamic performance optimization of a pilotscale reactive distillation process by economics optimizing control”. Industrial & Engineering Chemistry Research, 57, 12165-12181, 2018.
  • [31] Yadav ES, Indiran T, Priya SS. “System identification and conditional control for an optimal operation of a pilot plant binary distillation column”. International Journal of Computing and Digital Systems, 9, 139-146, 2020.
  • [32] Çağatay MT, Karacan S. “Multivariable generalized predictive control of reactive distillation column process for biodiesel production”. Turkish Journal of Engineering, 6, 40-53, 2022.
  • [33] Karacan S, Çağatay MT. “Transesterification of waste cooking oil into biodiesel using Aspen HYSYS”. International Journal of Scientific Research in Science and Technology, 3, 83-87, 2017.
  • [34] Karacan S, Çağatay MT. “Simulation and optimization of reactive packed distillation column for biodiesel production using heterogeneous catalyst”. International Journal of Energy Applications and Technologies, 5, 153-160, 2018.
  • [35] Çağatay MT, Çağatay Ş, Karacan S. “Optimisation of biodiesel synthesis from waste cooking oil in the reactive distillation column using Taguchi methodology”. Journal of Polytechnic, 24, 175-186, 2021.
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Kimya Müh. / Tekstil Müh. / Gıda Müh.
Yazarlar

Mehmet Tuncay Çağatay Bu kişi benim

Süleyman Karacan Bu kişi benim

Yayımlanma Tarihi 30 Aralık 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 28 Sayı: 7

Kaynak Göster

APA Çağatay, M. T., & Karacan, S. (2022). Experimental performances of nonlinear GPC and discrete-time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(7), 977-986.
AMA Çağatay MT, Karacan S. Experimental performances of nonlinear GPC and discrete-time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Aralık 2022;28(7):977-986.
Chicago Çağatay, Mehmet Tuncay, ve Süleyman Karacan. “Experimental Performances of Nonlinear GPC and Discrete-Time PID in Multivariate Process: NARIMAX and ARX Models in Temperature Control of Reactive Distillation Column in Synthesis of Biodiesel from Waste Cooking Oil”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28, sy. 7 (Aralık 2022): 977-86.
EndNote Çağatay MT, Karacan S (01 Aralık 2022) Experimental performances of nonlinear GPC and discrete-time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28 7 977–986.
IEEE M. T. Çağatay ve S. Karacan, “Experimental performances of nonlinear GPC and discrete-time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 28, sy. 7, ss. 977–986, 2022.
ISNAD Çağatay, Mehmet Tuncay - Karacan, Süleyman. “Experimental Performances of Nonlinear GPC and Discrete-Time PID in Multivariate Process: NARIMAX and ARX Models in Temperature Control of Reactive Distillation Column in Synthesis of Biodiesel from Waste Cooking Oil”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28/7 (Aralık 2022), 977-986.
JAMA Çağatay MT, Karacan S. Experimental performances of nonlinear GPC and discrete-time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2022;28:977–986.
MLA Çağatay, Mehmet Tuncay ve Süleyman Karacan. “Experimental Performances of Nonlinear GPC and Discrete-Time PID in Multivariate Process: NARIMAX and ARX Models in Temperature Control of Reactive Distillation Column in Synthesis of Biodiesel from Waste Cooking Oil”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 28, sy. 7, 2022, ss. 977-86.
Vancouver Çağatay MT, Karacan S. Experimental performances of nonlinear GPC and discrete-time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2022;28(7):977-86.





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