Model predictive control of dividing wall distillation column
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1 Available online at Scinzer Journal of Engineering, Vol 3, Issue 1, (2017): DOI: /SJE ISSN X Model predictive control of dividing wall distillation column Shahin Seyed Aghaei 1, Mohammad Reza Jahed-Motlagh 2 1. Senior Control & Instrumentation Engineer at Tehran Refinery, Iran 2. Associate prof.at Iran university of science and technology *Corresponding author Sh.aghaei@tehranrefinery.ir Abstract: The primary research indicates that 33 percent reduction energy consumption was in the distillation column Kaibel. As well as, we can consider the other benefits besides energy consumption for Kaibel column. The most important factors for distillation column Kaibel including: 1) lower power consumption; 2) lower investment cost; 3) small physical space required. Due to the advantages mentioned in relation with distillation columns with walls of separation,the distillation column Kaibel most likely alternative to distillation columns will be present in process industries, especially oil and gas. In order to control the distillation column, we can apply the controllers such as PID, LQR, prediction models and regulatory prediction models and their performance in the present disturbance and uncertainty are investigated in the bypass steam and control inputs. Keywords: split Steam, split liquid, boil-up, control monitoring forecasting model, uncertainty Introduction Distillation columns with partition (Dividing Wall Column) can be used for alternative distillation columns current with the most efficient. The DWC of thermodynamically is equivalent arrangement Petlyuk, but there is two differences in columns and common shell[1]. As its name implies that DWC through a wall is being separated by two vertically parts. However, it will be replaced by the current fed into the column Petlyuk. One of the major mutations that occurred in DWC, including development and utilization rates in the industry increased significantly, and the introduction of the separation wall without boiling by Montez (montz), respectively[12]. distillation column (kaibel) in 1987 to enhance the efficiency of distillation was invented in process industries[2]. Operating results was obtained from the use of 30 to 35 percent reduction in energy consumption. Now more than 150 DWC are in process plants on operation [2]. The most important factors that give the distillation column Kaibel contains: 1) lower power consumption; 2) the cost of investment lower; 3) small physical space required. The present study is providing background information and technical knowledge about of particular modeling and control distillation column[3]. The used equations in the distillation The distillation is a physical process in which including mixture of boiling liquids based on their boiling point.figure1 shows a partition and Split column four products with liquid and vapor. A distillation column usually is divided to the vertical direction [14],into a number of classes and each class is contains vapor-liquid equilibrium (VLE) [4]. Figure 1: The vapor-liquid equilibrium
2 Where Nc is the number of components that must be in separate columns. y1, y2,..., y (Nc1) molar ratios are in the vapor phase for Nc. Similarly x1, x2,..., x (Nc-1) molar ratios of the components are in the liquid phase T and P respectively[5]. Figure 2 : Stages of a distillation column. Modeling distillation column The model distillation column including 64 trays and consists of seven sections, and contains a certain number of classes (which are numbered from number one to 64 [7]. feed containing butanol, ethanol, methanol and propanol. This column has eight degrees of freedom that four of which are related to the products and the rest of the fine. Liquid split (RL) will determine how much of the liquid that goes to the top of the separator from the main column and steam (Rv) the amount of steam that is separated from the back to the bottom [8]. 63
3 Figure 3. The model distillation column Kaibel The nonlinear model is used basically in three different, differential and The differential equations contain mass balance and ratio temperature in the distillation column[6].the simulation process is summarized in the following expression: Compare the flow of steam and liquid for each floor in the column and Calculate the change in mass for each class K by using a mass balance differential equation: Calculate the molar ratio of steam for each component i in each class: Where the coefficient activity γ (i, k) by using Wilson (Wilsons) is obtained and the pressure in each class k is equal to: To Calculate the molar ratio of liquid for each component I in each class k: Where μ is the speed of convergence and positively number. Temperature is simply a numerical and has no physical meaning, but ensures that pressure is calculated using raoult s law [9]. Table 1:variables and nominal values 64
4 The entries that are adjustable using the controls in the form of disturbances of the vector u and d we define the vector. IV. control column and optimal performance Skogestad(2007) considered a number of advantages of thermal loops to control temperature [5] as follows: 1-Stabilization composition profiles along column 2- Providing indirect control level These six degrees of freedom are provided by six different PID controller and also, four temperature values are selected by keeping them in reference to the steady-state performance [13]. The objective function is writable as follows: Figure 4. control structure controlled temperatures contain T17, T30, T49 and T59, After a fine stream, L is variable for T30, and so the relationship between T59 and S1 side, the side S2 and T49, liquid Split RL and T17. The model predictive controller regulatory 65
5 The model predictive controller needs to access an estimate of the current state and future disturbances. Future disturbances often estimated by value placed disturbance[10]. Figure 5 shows the difference between a typical model predictive control and monitoring: Figure 5: Model predictive controller In this case, the PID controller is also included in the dynamic model, model predictive mode is different in singlelayer model predictive controller. Linear model has 20 state variable in which the temperatures contain T17, T30, T49, and T59. Beverly and Morari ( Borrelli, & Morari) stated that the system is visible to the number of measured variables must be at least equal to the number of disturbance temperatures T6, T24 and T53 as measured variables and The measured output vector will be as follows: 66
6 Figure 6: decentralized control column Vapor bypass steam combined with high-class show in each category. However, the new model steam will be as follows: Simulink Maximum and minimum values of the control is and, and.the most important factors including weighted matrix and the forecast horizon, respectively. The model predictive controller sampling time of 10 minutes was considered. X1 = XD, X2 = XS1, X3 = XS2, X4 = XB, respectively out flows related to the concentration of S1, S2, S3, S4 and temperatures are related to the T17, T30, T49, T59, respectively. Figure 8: Changes in product concentration controller with PID 67
7 Figure 9: concentration controller with MPC monitoring products Figure 10: Changes in temperature controlled by PID controller Figure 11: The return flow (Reflux) 68
8 Figure 12: MPC controller that is controlled by monitoring temperature changes Figure 13: PID control signal controls Conclusion As part of the simulation is known to increase the bypass steam parameters (value α) causes adverse effect on the concentration and purity of the products of distillation column is the origin of this effect is the change in temperature profile column. To compensate for this adverse effect to the amount of flow in the column increases, the controller before the regulatory changes less in the amount of flow in the controller decentralized the compensation performs, while a signal control in Model Predictive control (MPC) performance is better than decentralized controllers and provides a constant temperature profile that keeps your tower stabilize the concentration and purity distillation column is output. References A.J. Brugma. Process and device for fractional distillation of liquid mixtures, more particularly petroleum.us Patent No. 2,295,256, B. Kaibel, H. Jansen, E. Zich, and Z. Olujic. Unfixed dividing wall technology for packed and tray distillation columns. IChemE Symposium Series, 152, D.A. Monro. Fractionating apparatus and method of fractionation. US Patent No. 2,134,882, F. B. Petlyuk, V. M. Platonov, and D.M. Slavinskii. Thermodynamically optimal method for separating multicomponent mixtures. International Chemical Engineering, 5(3): , G. Kaibel. Distillation columns with vertical partitions. Chem. Eng. Technol., 10:92 98, Petluyk. I. Dejanovic, L. Matijasevic, and Z. Olujic. Dividing wall column-a breakthrough towards sustainable distilling. Chemical Engineering And Processing, 49(6): , June I. Dejanovic, L. Matijasevic, Z. Olujic, I. Halvorsen, S. Skogestad, H. Jansen, and B. Kaibel. Conceptual design and comparison of four-products dividing wall columns for separation of a multicomponent aromatics mixture. Distillation & Absorption 2010, A.B. de Haan, H. Kooijman and A. Gorak (Editors),2010. I.J. Halvorsen and S. Skogestad. Optimal operation of petlyuk distillation: A steady-state behavior. J. of Process Control, Special issue:selected Papers from Symposium PSE-ESCAPE 97, Trondheim. Norway,May
9 J. Strandberg. Optimal operation of dividing wall columns, PhD Thesis, Norwegian University of Science and Technology, M.A. Schultz, D.G. Stewart, J.M. Harris, S.P. Rosenblum, M.S. Shakur, and D.E. O Brien. Reduce costs with dividing-wall columns. Chemical Engineering Progress, HS R.O. Wright. Fractionation apparatus. US Patent No. 2,471,134, The Magic of Dividing Wall Columns, Montz Z. Oluji, M. Jodecke, A. Shilkin, G. Schuch, B. Kaibel, Equipment improvement trends in distillation.chemical Engineering and Processing 48 (2009) Z. Olujic, M. Jodecke, A. Shilkin, G. Schuch, and B. Kaibel. Equipment improvement trends in distillation. Chemical Engineering And Processing, 48(6): , June
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