Multivariable decoupling model predictive control of reactive distillation column for the production of ethyl acetate
Başlık çevirisi mevcut değil.
- Tez No: 346830
- Danışmanlar: PROF. DR. SÜLEYMAN KARACAN
- Tez Türü: Doktora
- Konular: Kimya, Chemistry
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2011
- Dil: İngilizce
- Üniversite: Ankara Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Kimya Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 197
Özet
.
Özet (Çeviri)
This work was carried out to develop a decoupling model predictive control algorithm for a reactive distillation column for the production of ethyl acetate from the estcrification reaction between acetic acid and ethanol. The pilot scale of the column that was set up had the rectifying and stripping sections filled with rasching rings while the reaction section was filled with Amberlyst 15 solid catalyst. The system of theoretical models and the prototype of the column were developed using the first principles and IIYSYS 3.2 respectively. The I IYSYS prototype plant was simulated and optimized taking the maximization of ethyl acetate mole fraction in the condenser as the objective function. The optimum values of the input variables estimated using I IYSYS optimizer were used to simulate the theoretical models numerically with the aid of MATLAB R2010b and run the plant for steady state studies and. in all the cases, the output variables were recorded accordingly, fhe validity of the conceptually selected control configurations were verified by studying the dynamics of the plant. Also achieved from the dynamics studies of the reactive packed distillation column was the generation of plant input/output data for the development of nonlinear neural networks models. Three different nonlinear neural networks models were designed and simulated for the reactive packed distillation process with the aid of Neural Network Toolbox 7 of MATLAB R2010b using Levenberg-Marquardt algorithm as the training algorithm. The decoupling of the 3 x 3 multi-input multi-output process was carried out by estimating the process transfer function matrix with the aid of System Identification Toolbox 7 of MATLAB R2010b and formulating the interaction compensator matrix. The next line of action of this work is proceeding to the formulation of the multivariable decoupling model predictive control algorithm. The algorithm will be used to control the process theoretically first before using it to control the real pilot plant for the production of ethyl acetate. The results of the algorithm will be compared to that of an undecoupled model predictive control algorithm and a classical Proportional-lntegralDerivative (PID) control algorithm
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