Adequacy Testing of Some Algorithms for Feedforward Control of a Propane-propylene Distillation Process

Transcription

1 Adequacy Testing of Some Algorithms for Feedforward Control of a Propane-propylene Distillation Process NICOLAE PARASCHIV*, EMIL PRICOP* Petroleum Gas University of Ploiesti,, Control Engineering, Computers and Electronics Department, 39 Bucuresti Blvd., , Ploiesti, Romania In this paper the authors present a new approach for establishing the most suitable model for a feed forward controller for propane-propylene separation. The process is simulated in PRO/II and the results are validated against industrial data. The adequacy of two controller models is tested using a LabVIEW application. The suitability of FUG model based on limitative operation parameters for propane-propylene distillation process control is demonstrated by analyzing the simulations results. Keywords: automatic control, control design, feed forward control, distillation process, distillation process simulation, manipulated variables. The main objective of a separation process consists in satisfying the quality conditions, respectively, conformation to the imposed compositions of the separated products. Specifications for the concentration of one or more components in the separated products are used in the current practice. Apart from the quality objective, which is the most important, the separation process must comply to security and efficiency objectives [1]. The security refers to protecting human operators, the environment and the industrial equipment. The efficiency can be quantified in the effort (especially to the financial ones) to respect the quality specifications and to ensure the security, in correlation with the financial results from separated products commercialization. Automation is a key component of process operation, sine most processes are strongly affected by disturbances. According to the objectives mentioned above, the automation includes automated process control, protection and optimization. This paper addresses issues concerning the automatic control of the classic distillation process of a binary mixture, with a single feed stream and no side products streams. In order to control such a complex process, operated by using mainly the following equipment: the distillation column, the separation vessel (reflux vessel) the condenser and the reboiler, there must be automatically controlled the parameters: the separated products compositions (the overhead product and the bottom product), the pressure and the liquid level in the condenser and in the bottom of the column. The control of mass transfer and implicitly of the separated products quality can be achieved by adjusting the descending liquid stream L and/or the ascending vapor stream V which interact on each elementary separation unit (stage/tray). Shinskey [2] recommends for the distillation process control the utilization of a single internal stream (L or V) together with one of the products stream (overhead product D or bottom product B). Figure 1 exemplifies the dual control structure L B of a distillation column. In this figure the allocation of manipulated variables at control tasks (controlled variables) are those included in table 1. Table 1 and f igure 1 shows that the internal liquid stream L, and the stream product B, are controlled variables for controlling concentration of light key component in top * nparaschiv@upg-ploiesti.ro; emil.pricop@upg-ploiesti.ro Fig. 1. L-B structure of a distillation column Table 1 LOCATION OF MANIPULATED VARIABLES product (x D ) and in bottom product (x B ). The manipulated variables for the liquid level in the condenser (H VR ) and in the bottom of the column are the distillate flow rate D and the vapor flow V in the top of the column. The important disturbances are the flow rate of the feed stream F and the concentration in this stream of the most volatile component x F. The separation process of propane-propylene mixture (C 3 ) in a distillation column from a Catalytic Cracking installation will be presented and analyzed in this paper from the automation point-of-view. The propylene (C 3 ) is the most volatile component in the used mixture and, as consequence the notations x F, x D, x B refers to the propylene concentrations expressed as molar fractions in the feed, overhead product and bottom product, respectively in F, D and B streams. Feedforward control is adequate for this installation since the mass transfer process, which is the key of the separation process, is characterized by high inertia with the transient regime duration of hours and even dozens of hours. The disturbance actions are compensated for this REV.CHIM.(Bucharest) 67 No

2 kind of control algorithm, so they can not influence the controlled parameters and the set points and specifications are kept [3]. A very important characteristic of feedforward control is that the algorithm is fully dependent on the controlled process, reflecting the process behavior at considered disturbances modifications. The algorithm consists of a properly codified process model. Taking into consideration the need for real time processing and the limited resources available on embedded equipment controller, the usage of simplified models is highly required. The feedforward control system performances are significantly influenced by the control algorithm characteristics. As stated in the previous paragraph the control algorithm includes a simplified model of the separation process. An adequacy testing method for simplified models of the separation process that can be used to implement a feedforward controller is presented in the following sections of this paper. The customization of the model will be done for propane propylene (C 3 ) mixture. References [4, 5] show an industrial implementation of a feed-forward control system for C 3 mixture, but the adequacy of presented control algorithms was not tested. Proposed testing method The proposed testing method, is valid for the feedforward control of propane-propylene mixture fractionating, shown in figure 2. It is obvious that by using the proposed staging, the proposed method evaluates in two approaches L and B flow rates, which interconnect the FFC and FP entities corresponding to the feed forward structure. Two simplified models for the propane-propylene separation process are presented in the following paragraphs. Those models will be used in the second step of the proposed testing method. The model based on evaluation of limitative operation parameters Limitative operation parameters of a separation column are represented by the minimum number of theoretical trays N min and by the minimum reflux ratio R min [6]. These parameters are purely theoretical since R min is referring to a column with an infinite number of trays and N min is considering a column operating with total reflux, meaning also infinite reflux ratio. C 3 mixture is characterized by a quasi-constant relative volatility (α) of propylene and propane. Given this volatility, N min can be computed using Fenske formula [7]. R min can be determined using Underwood expression [8]: (1) (2) where θ is computing using the following equation: (3) Fig. 2. Feedforward control structure of a binary distillation process The FFC inputs, represented in informational approach, are the set points x Di along with disturbances F and x F, as observed in figure 2. Reflux flow rate L and bottom product flow rate - B represent the FFC controller output variables. The process FP has four inputs in the same informational approach: the two disturbances F and x F along with the two command variables L and B computed by the FFC controller. The process outputs are the two products concentrations x D and x B (controlled variables). The main objective of the feedforward control structure consists in maintaining the products concentrations (process outputs) at the desired set points x Di while the disturbances F and x F affect the process. Reaching this objective is conditioned by the computed L and B values. From the FP point-of-view, L and B are manipulated variables, which are adequate if the process reach the desired set points when they are applied as inputs. The adequacy testing of the algorithm implemented in the FFC controller, based on the simplified process model, is done using a method consisting in three steps: 1 process (FP) simulation in order to determine the steady state values of L and B for the desired x D and x B values, taking into consideration F and x F disturbances; 2 computing of L and B steady state values using the control algorithm, based on x Di set points and considering F and x F disturbances; 3 analyzing and interpreting the obtained results. If N, the real trays number of the column is known, then the reflux ratio R can be determined by using an analytical form of the graphical correlation Gilliland [9], which correlate the ideal and real parameters, using the following function: There were proposed some equations for the Gilliland correlation as presented in [10, 11]. The equation proposed by Eduljee [10], which was imposed by precision and simplicity is the following: This equation is valid when: Equation (5) can be also written as: Taking into consideration the total and partial material balance equations: and the reflux ratio formula: (4) (5) (6) (7) (8) (9) REV.CHIM.(Bucharest) 67 No

3 the two manipulated variables can be computed using the following relationships: The model described by relations (1) (12) will be referred in this paper as Fenske-Underwood-Gilliland (FUG) model. The model based on separation coefficient evaluation Separation coefficient, which is a quantitative indicator of fractionating efficiency, is defined by the following equation [3] The same coefficient can be obtained by the approximation determined by Douglas, Jafarey and McAvoy for the analytical solution of Smoker [12]. (14) The following equation (16) results from (14) and (15): (15) R can be determined solving equation (15). Then the values of B and L variables can be computed using (11) and (12). In the following sections of the paper, the model based on the double estimation of the separation coefficient will be referred as Douglas-Jafarey-McAvoy (DJM) model. Distillation process simulation PRO/II Simulation Software Configuration PRO/II is a steady state simulation software produces by SimSCI company, a subsidiary of Schneider Electric. The software package is currently used in industry and academia for simulation of processes chemical, petrochemical, natural gas processing industry and refineries. The software combines a large database of chemical components with a complex library of thermodynamic methods and models to offer the best (10) (11) (12) (13) fitting of the simulated process with the real, industrial data [13]. The first step for the steady state simulation of the propylene-propane separation is to select the two chemical components (propylene and propane) from the software s library. The thermodynamic model used to describe the phase equilibrium and the variation of the properties of the mixture in the operation conditions of the separation column is the Peng - Robinson method. The separation of the propane-propylene mixture in a column with 95 theoretical trays (including condenser and reboiler) was simulated in PRO/II starting from operation data of an industrial column and considering a global average efficiency of the industrial column of 90%. The simulated column operates in the same conditions as the industrial column in terms of: a) temperature and pressure profile; b) feed flowrate and composition; c) products compositions (distillate product composition x D =0,92 mol fraction propylene and bottom product composition x B =0.02 mol fraction propylene). As consequence, we expect that the reflux ratio and reflux flowrate to have similar values as the ones in the industrial column. In industry the column is fed with a multicomponent mixture with very low butane concentration. The distillation for multicomponent mixtures is done by using complex column sequences as presented in [14]. Table 2 displays the operating parameters of the column, both in simulation and industrial conditions. The results of the simulation of propane-propylene separation process in PRO/II are in good correlations with the values of the industrial parameters. Figure 3 shows the simulated column with 95 theoretical trays. The column is fed on tray 51. The main streams in this diagram are as follows: F_1 feed stream, D_1 distillate product and B_1 bottom product. PRO/II simulation results In order to determine the influence of the feed flowrate and feed composition variation on the reflux ratio (and implicitly on reflux rate) were achieved simulation in the following conditions: 1) Feed flowrate was varied from kmol/h up to kmol/h (241.49±10% kmol/h) while all others parameters (pressure, temperature, compositions) were kept constant at the values previously set. Table 3 displays the results of the simulations in these conditions; 2) Feed composition was varied from mole fraction up to mole fraction ( ±10% mole fraction). Table 2 OPERATING PARAMETERS FOR C 3 DISTILLATION COLUMN Fig. 3. The process simulation diagram of the distillation column REV.CHIM.(Bucharest) 67 No

4 Table 4 SIMULATION RESULTS IN CONDITIONS OF FEED STREAM COMPOSITION MODIFICATION Table 3 SIMULATION RESULTS IN CONDITIONS OF FEED FLOWRATE MODIFICATION Fig. 4. LabVIEW application front panel Table 4 displays the results of the simulations for the second case, when the feed stream composition ranges ±10% from the initial value of mole fraction propylene. As in the previously case, all others parameters (pressure, temperature, compositions) were kept constant at the values previously set in table 2. The simulations whose results are presented intable 3 and table 4 were aimed at determining the values for L and B flows, in order to assure x Di specification when the disturbances F and x F are modified. Determination of feedforward controller output (manipulated variables) The two presented models, FUG and DJM, was implemented in a computer program in order to determine the L and B values and to compare with the ones resulted from simulation in PRO/II. The implementation of the models was done in National Instruments LabVIEW software using the G graphical programming language and formula blocks. LabVIEW is a programming software instrument that permits the rapid design of a virtual instrument (VI). The front panel of the program is presented in figure 4. The x Bi and x Di specifications are defined when opening the application using the numeric up-down boxes in the left side of the program. In the same area the relative volatility of propylene and propane (α) and N, number of real trays of the column, can be modified. The purpose of the simulation using the software is to determine L and B values when disturbances F and x F are changed. In order to realize that scenarios, the user has to change F, respectively x F value, by using the corresponding cursors on the screen. The values for B and L are computed instantaneously and displayed in the corresponding textboxes. The block diagram of the LabVIEW program is shown in figure 5. The two formula nodes contain the program for computing L and B based on FUG and, respectively, DJM models. All the coding is done in a language similar to the ANSI C. The Write to Measurement File block in the right of the block diagram is used to write the Excel result files REV.CHIM.(Bucharest) 67 No

5 Fig. 5. LabVIEW application block diagram Fig. 6. Computing scheme for FUG model. Rel - Relation Table 6 SIMULATION RESULTS FOR FUG MODEL- SCENARIO 2 Table 5 SIMULATION RESULTS FOR FUG MODEL- SCENARIO 1 Fig. 7. Computing scheme for DJM model Feedforward algorithm based on FUG model In the first test scenario the feed flowrate F was modified The software designed for FUG algorithm from kmol/h up to kmol/h and while keeping implementation is based on the calculation scheme in constant x F at mol fraction. The results are figure 6, where the numbers between parentheses presented in table 5. represents numbers of relations from the second section In the second test scenario, the concentration x F was of this paper. modified from to mol fraction and while The value of the parameters that intervene in these keeping constant feed flowrate F at kmol/h. table 6 relations are shown in table 2. Relative volatility, α, is shows the results. computed as the equilibria constants ratio for propylene and propane, determined by simulation at pressure and Feedforward algorithm based on DJM model temperature corresponding to the feeding tray. The program for DJM algorithm is based on the calculation scheme presented in figure 7, where the REV.CHIM.(Bucharest) 67 No

6 Table 7 SIMULATION RESULTS FOR DJM MODEL- SCENARIO 1 Table 8 SIMULATION RESULTS FOR DJM MODEL - SCENARIO 2 Fig. 8. Results of adequacy testing Scenario I numbers in parentheses describe the relations from sections 2-B and 2-A of this paper. DJM model testing was realized using the same initial data as for FUG model. We also used the same testing scenarios. The results for the two scenarios are presented in tables 7 and 8. Testing results discussion and interpretation Figures 8 and 9 show a comparison between the PRO/ II simulation results and the ones obtained using the FUG and DJM models in each testing scenario. Figure 8 show the variation of reflux flowrates corresponding to the PRO/II simulation and the two tested models when the feeding flowrate F is modified. By analyzing the data upon which Figure 8 was generated we obtained the following error rates: E LF -FUG = 5.31% and E LF -DJM = %. Figure 9 presents the variation of reflux flowrates corresponding to the two tested models when the propylene concentration (x F ) in the feeding stream is modified. The following error rates were determined: E LxF - FUG = 5.40% i E LxF -DJM = %. Flow rate B is determined from the material balance equations for both the PRO/II simulation and the test models, so the error determination is not required. Fig. 9. Results of adequacy testing Scenario 2 Taking into consideration the results presented above we conclude that FUG model is adequate for feedforward control of the propane-propylene distillation process. The confirmation of FUG model suitability is given by another testing step. In this step we simulate the process in PRO/II using L and B flow-rate values determined with FUG model and we analyze the x D concentration. Table 9 contains a sample of the test results from the first scenario. This scenario imposed to maintain x F Table 9 CONCENTRATION X D CALCULATED IN PRO/II FOR GIVEN L AND B FLOWRATE VALUES FROM FUG MODEL (SCENARIO 1) REV.CHIM.(Bucharest) 67 No

7 concentration constant and to vary the F flowrate. The x D determined concentration was compared to the specification x Di =0.92 mol fraction. The resulting mean error is E xdf -FUG = 114%. Table 10 presents a sample of results from the second testing scenario. In this case the F flowrate was constant and the value of concentration x F varied. The resulting x D concentration was compared to the reference value x Di =0.92 mol fraction. The resulting mean error is E xdxf - FUG = 0.55%. Table 10 CONCENTRATION X D CALCULATED IN PRO/II FOR GIVEN L AND B FLOWRATE VALUES FROM FUG MODEL (SCENARIO 2) Conclusions The feedforward process control performance for a distillation process is dependent on the model used for implementing the controller. If there are several available models, selecting the one who is the best regarding the process control objectives is a challenging task. A strategy for testing the adequacy of a controller model for such a system is proposed in this paper. The testing procedure involves a double simulation, one for the process and one for the model associated to the feedforward controller. A case-study is done in this paper and focuses on propane-propylene separation process in a distillation column. The PRO/II simulation results are validated on data obtained from an industrial facility. The simulation aims to determine the L and B flowrates in order to ensure compliance with the specifications x Di when the disturbances F and x F change. Two models are proposed for the feed forward controller. The first model (FUG) is based on the evaluation of the limitative operation parameters. The second model (DJM) rely on a double evaluation of the separation coefficient. A LabVIEW application was developed in order to determine the values of L and B manipulated variables using these two models. The adequacy testing of these models was done by following two test scenario differentiated by the modified disturbance (F and x F ). Analyzing the simulation results and evaluating the errors we conclude that FUG model is suitable to be used in a feed forward controller for propanepropylene separation process. The error obtained with the FUG model when testing its performances was under 1.2 %. References 1.PARASCHIV, N., Achizitia si prelucrarea datelor, Editura Universitãii Petrol-Gaze din Ploiesti, Ploie ti, SHINSKEY, G.F., Distillation control for productivity and energy conservation, New York, McGraw-Hill Book Company, MARINOIU, V., PARASCHIV, N., Automatizarea proceselor chimice, vol 2, Editura Tehnicã, Bucure ti, PARASCHIV, N., CIRTOAJE, V., Rev. Chim. (Bucharest), 43, no. 7, 1992, p MARINOIU, V., PARASCHIV, N., Rev. Chim. (Bucharest), 42, no. 8-9, 1991, p STRATULA, C., Fractionarea. Principii si metode de calcul. Editura Tehnicã, Bucureºti, FENSKE, M.R., Ind.Eng. Chem., Vol. 24: 482, UNDERWOOD, A.J.V, Chem. Eng. Prog., Vol. 44, nr. 8, 1948, p GILLILAND, E. R., Ind. Eng. Chem., Vol. 32, nr. 9, 1940, p EDULJEE, H.E., Hydro. Proc., Vol. 54, nr. 9, 1975, p PARASCHIV, N., Rev. Chim. (Bucharest), 41, no. 7-8, 1990, p JAFAREY, A., DOUGLAS M.J., MC AVOY, J.T., Ind. Eng. Chem. Process Dev, Vol. 18, Nr. 2, ***, PRO/II User Manual - Schneider Electric Software, Inc. 14.NICOLAE, M. Complex systems of distillation columns used in the production of the propylene oxide, Rev. Chim.(Bucharest), in press Manuscript received: REV.CHIM.(Bucharest) 67 No