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Study of Energy Efficient Distillation Columns Usage for Multicomponent Separations through Process Simulation and Statistical Methods
Jiří Jaromír Klemeš, Petar Sabev Varbanov and Peng Yen Liew (Editors) Proceedings of the 24th European Symposium on Computer Aided Process Engineering – ESCAPE 24 June 15-18, 2014, Budapest, Hungary. Copyright © 2014 Elsevier B.V. All rights reserved.
Study of Energy Efficient Distillation Columns Usage for Multicomponent Separations through Process Simulation and Statistical Methods Sandra S. Florindo, Isabel M. João, João M. Silva* Chemical Engineering Department, Instituto Superior de Engenharia de Lisboa-ISEL, Instituto Politécnico de Lisboa, R. Cons. Emídio Navarro 1, 1959-007 Lisboa Portugal. [email protected]
Abstract This paper studies the optimal design conditions for the fully thermally coupled distillation columns, FTCDC, through process simulation with Aspen HYSYS and statistical methods. A fractional factorial design was used in order to screen the main operational and structural factors that minimize the total cost. Following the process characterization a steepest descent method was iteratively performed in the direction of total cost optimization. The best combination of levels of structural and operational variables obtained by the designed experiments allowed to a reduction of 9.6 % in the total costs. The factors’ levels obtained in steady state simulation were then tested in dynamic simulation. It is important to carry out the dynamic simulation to test the conditions obtained by steady state simulation in a more realistic way because it is well known that FTCDC systems are difficult to control and operate. Keywords: FTCDC, factorial design, Aspen HYSYS, Dynamic Simulation
1. Introduction Most chemical processes require the separation of mixtures of chemical components. Distillation is mainly used for liquid separations, driving nearly all other separation techniques out of the process industry. The distillation processes have a huge impact on both operation and investment costs in chemical plants and this has motivated the development of several types of fully thermally coupled distillation columns (FTCDC) that can lead to savings in energy and capital costs (Agrawal and Fidkowski, 1998). Despite the high potential of the FTCDC economic benefits, a lack of reliable design methods has contributed for the low number of commercial solutions (Caballero and Grossmann, 2006). Therefore, it is still a challenging task for engineers to define near optimal design conditions for the FTCDC in a simple and efficient manner in the initial stage of the design procedure. Several distinct configurations of the FTCDC can be implemented in commercial process simulators, but the challenge is to find optimal or near optimal solutions for the problem due to the large number of design variables of the FTCDC system which lead to tedious iterative simulations in order to find a proper structure. Additionally the trial and error simulation can lead to an inadequate structure unable to converge in the process simulation for design (Kim, 2002). Statistical methods like factorial and fractional factorial designs can be used to investigate the effects of many different factors by varying them simultaneously instead of changing one factor at time (OFAT). This is a great advantage of experimental statistical design over the OFAT traditional experimentation, because it provides a full
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comprehension of the interactions between the design factors allowing to reach better design solutions with lower number of experiments when compared with OFAT (Montgomery, 2009).There are few studies concerning the use of statistical designed experiments in the optimization of energy efficient columns usage for multicomponent separations through process simulation in steady state (Long and Lee, 2012). A more recent study from Sangal et al. (2013) shows the use of Box–Behnken statistical design coupled with simulation for the optimization of the main process parameters in divided wall distillation columns. Simulation was coupled with statistical designed experiments in the first place to factor screening, that is to identify the main variables that affect the total cost followed by process optimization in order to find the variables’ conditions that result in a lower total cost. Knowing that FTCDC systems are difficult to control and operate (Wolff and Skogestad, 1995) it was also an aim of this work to verify if the conditions obtained by steady state simulation could be run using Aspen HYSYS in a dynamic simulation mode which translates in a more realistic way the process performance.
2. The problem– Separation of a ternary mixture In this work, we start the design using the methodology proposed by Triantafyllou and Smith (1992) by means of the preliminary design equations based on short-cut FenskeUnderwood-Gilliland-Kirkbride method (FUGK) to find the initial configuration for the Petlyuk column. It was chosen a mixture of 2-methylpropan-1-ol, butan-1-ol and butan2-ol. The design was implemented in Aspen HYSYS v7.3 and for the computation of the thermodynamic properties it was used the UNIQUAC model with the binary parameters from the Aspen HYSYS database. The Petlyuk configuration for the separation of a ternary mixture is a special case of the FTCDC. A prefractionator followed by a product column characterizes this system. For the first step of the design procedure, it was applied the short-cut distillation method (FUGK) to obtain a first approximation for the Petlyuk structure (Figure 1). In this step it was possible identify the values for the main variables of this system and it was implemented a new structure consisting on an absorber and a distillation column. The absorber corresponds to the prefractionator.The main design factors identified can be aggregated into two types: six structural related with the number of stages in the prefractionator and main column and position of the feed and draw of the midle product; and two operational related with the flow rates of liquid and vapor transferred between
Figure 1. Implementation of the FUGK method in Aspen HYSYS and identification of the main variables of the Petlyuk system.
Study of Energy Efficient Distillation Columns Usage for Multicomponent Separations 147 through Process Simulation and Statistical Methods the prefractionator and the product column. The main variables are identified and the starting values for the optimization process by design of experiments are displayedin Table 1.
3. Process optimization with designed experiments Some special types of factorial designs are very useful in process development and improvement. One of such kinds are factorials of the type 2 k with k factors, each at two levels usually referred as low level (-1) and high level (+1) of the factor. As the number of factors in a factorial experiment grows the number of effects to estimate also grows. In the present case we have a total of 8 factors and so we would need a total of 256 simulations (experiments) with no replication in order to perform all the combinations. In order to reduce the number of simulations and assuming the sparsity of effects principle a fractional factorial design can be used to obtain information on the main effects and low order interactions. The fractional factorial chosen was 2 IV8-4. In this design no main effect is aliased with any other main effect or two factor interaction being a good design to use in a screening experiment due to its high resolution. This design only requires 16 experiments reducing considerably the number of runs required for a full factorial experiment. For the fractional factorial design four generators were used, E=BCD, F=ADC, G=ABC and H=ABD. In order to interpret the results of fractional factorial designs it is necessary to take into account the alias relationships (Montgomery, 2009). For the design of experiments simulations, a variation of r1 stage was used for the structural factors and a variation of r 5 kgmol/h for the operational factors in relation to the starting values. The response variable selected was the total cost obtained with the Aspen Economic Evaluator using the default definition. With this tool, it is possible to obtain a rapid estimation of the capital and operational cost of each run. After performing the 16 experiments, the effects were estimated and a normal probability plot of the effects was built in order to graphically judge the relevance of the factors and interactions. The estimates that behave like a random sample drawn from a normal distribution have zero mean and the plotted effects will lie approximately along a straight line. Those effects that do not plot on the line are probably the significant effects as we can observe in Figure 2a).The analysis of variance (ANOVA) was performed in order to test which factors and interactions are significant. The mean Table 1. Description of the main variables and starting values Structural factors A – Position of the feed stream in the prefractionator, NF=8
D – Number of stages in the middle section of the main column, NTS2=51
B – Number of stages of the prefractionator, NTP=19
E – Position of the extraction of B product (middle), NSD=42
C – Number of stages in the top section of the main column, NTS1=15
F – Number of stages in the bottom section of the main column, NTS3=8
Operational factors G – Vapor Molar flow of the interconnection stream in the bottom, V3=280 kgmol/h
H – Liquid molar flow of the interconnection stream in the top, L1=240 kgmol/h
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square of each of the factors and interactions that did not plot on the line (i.e. a total of seven) were calculated and divided by the mean square error. Each of these ratios follows an F distribution, with the numerator degrees of freedom equal to the number of levels minus one (i.e 1 degree of freedom) an the denominator degrees of freedom equal to 8 (i.e 15-7). The computed F should be compared with the tabular value (i.e. F5%,1,8 = 5.32) and the null hypothesis is rejected if the computed F exceed the tabular value for the significance level of 5 %. After ANOVA computation we were able to conclude that all the seven, factors and interactions, affect significantly the total cost (response variable), a result already observed graphically (Figure 2a) and tested with ANOVA. We concluded that factors A, B, G and H are significant as well as the interactions AB, BG and GH. Figure 2(b) represents the plots of the AB, BG and GH interactions and A, B, G and H main effects. The plot of the interaction GH shows that the interaction is very strong, and the effect of changing H from the lower level to the higher level is dependent of the level in which factor G is settled (the interaction hide the main effects). Looking at Figure 2(b) it was easy to conclude that it is better to work with factors A and B in their higher levels in order to minimize the cost. In relation to factors G and H it is better to work with factor H in the lower level an also factor G in the lower level due to the effect of the interaction GH that is stronger than the effect of the individual factors. After performing the fractional factorial design for process characterization and once the appropriate set of structural and operational factors as well as their levels is identified, the next step was the process optimization in order to find the set of conditions that result in the lowest total cost. In order to optimize we used the method of steepest descent, which is a procedure for moving sequentially along the path of steepest descent that is in the direction of the minimization of the response. A second cycle of simulations were performed varying the factors considered significant. The experiments were conducted along the path of steepest descent with a full factorial design24 with the factors A, B, G, and H varying in the direction of the better level in which the total cost reduces. The results of ANOVA for a level of significance of 5 % allowed to the conclusion that the factors A, B, G and H still affect the response. After that, a third designed experiment was performed in order to further move along the path of steepest descent. After the whole process of optimization, the better conditions of the eight factors (structural and operational variables) in order to minimize the total cost are:
Figure 2. Factorial fractional experiments results: a) Probability plot to identify the significant factors and interactions; b) Influence of the factors and interactions level in the total cost.
Study of Energy Efficient Distillation Columns Usage for Multicomponent Separations 149 through Process Simulation and Statistical Methods A –NF=13; B –NTP=32; C –NTS1=15; D –NTS2=51; E –NSD=42; F –NTS3=8; G – V3=265kgmol/h; H – L1 = 225 kgmol/h. The use of these conditions in the Petlyuk column allows a reduction of the total cost of 9.6 % relative to the starting values.
4. Dynamic simulation After the statistical design experiments performed, the optimal solution obtained was tested by dynamic simulation using the Aspen HYSYS in dynamic mode. This type of system is more complex than a traditional distillation column with more degrees of freedom. Taking in consideration the control objective and previous works done by Wolf and Skogestad (1995) and Hwang et al. (2011) was chosen a LV configuration with SISO feedback control loops, adapted from the LV configuration used in traditional distillation columns. The control structure implemented present eight control loops. The control loop for pressure with the pressure in the top stage of the main column is the variable to control and the manipulated variable is the flow of utility used in the condenser. The two levels control loops in the condenser and reboiler use the flow rates of the distillate and bottom products as manipulated variables respectively. The composition of the distillate is controlled by manipulating the reflux flow rate. The composition of the bottom product is adjusted by manipulating the flow rate of the utility used in the reboiler. For the control of the composition of the middle product a cascade control loop was adopted were the set point for the side draw flow rate was obtained by the control of the composition of this stream. The two remaining control loops concern the control of the flow rates of interconnection streams between the prefractionator and the main column. One is for the liquid stream leaving the main column and the other is for the vapor stream exit from the main column. Figure 3 presents the simulation of the control loops. All the controllers are configured as PI and the parameters were obtained by the internal auto-tuning function. In the auto-tuning, it is possible to adjust the relay hysteresis to take into account the non-linearity observed in the distillation columns. As an example of the performance obtained with this control configuration, it is showed in Figure 4 the response of the composition control loops in the bottom and middle products to a change in the set-point. The control of the FTCDC system is more complex than in a traditional distillation column and the results obtained showed that the response time of the system is higher when compared with a system of two columns for the same separation, but even so possible to control. The FTCDC control can be improved using model predictive control strategies.
Figure 3. Implementation of FTCDC in Aspen HYSYS with control loops.
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Figure 4. Response of the composition control loops for the middle and bottom products.
5. Conclusions The combination of statistical tools like design of experiments proved to be useful in the simulation of multicomponent separation reducing the number of simulation runs to achieve an optimized solution for a particular problem. In the case of this Petlyuk column a reduction of the total cost estimation of almost 10 % was obtained with a reduced number of runs. The dynamic simulation of the optimized Petlyuk column shows the possibility of operation of this system, but it is necessary to implement new control strategies to overcome the high response time observed. The use of model predictive control can be a strategy to improve the performance of these systems.
Acknowledgements I.M. João is also with CEG-IST and J.M. Silva is also with CRERG-IBB, research units of Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
References R. Agrawal, Z.T. Fidkowski, 1998, Are thermally coupled distillation columns always thermodynamically more efficient for ternary distillations?, Ind. Eng. Chem. Res., 37, 8, 3444–3454. J.A. Caballero, I.E. Grossmann, 2006, Structural considerations and modeling in the synthesis of heat-integrated−thermally coupled distillation sequences, Ind. Eng. Chem. Res., 45, 25, 8454– 8474. K.S. Hwang, B.C. Kim, Y.H. Kim, 2011, Design and Control of a Fully Thermally Coupled Distillation Column Modified from a Conventional System, Chem. Eng. Technol., 34, 2, 273– 281. Y.H. Kim, 2002, Structural design and operation of a fully thermally coupled distillation column, Chem. Eng. J., 85, 2–3, 289–301. N.V.D. Long, M. Lee, 2012, Design and optimization of a dividing wall column by factorial design, Korean J. Chem. Eng., 29, 5, 567-573. D.C. Montgomery, 2009, Introduction to Statistical Quality Control, 6th ed. John Wiley & Sons, Inc., Hoboken, NJ, USA. V.K. Sangal, V. Kumar, I.M. Mishra, 2013, Optimization of a divided wall columnfor the separation of c4-c6 normalparaffin mixture using box-behnkendesign, Chem. Ind. Chem. Eng. Q., 19, 1, 107−119. C. Triantafyllou, R. Smith, 1992, The design and optimization of fully thermally coupled distillation, Trans IChemE, 70, Part A, 118–132. E.A. Wolff, S. Skogestad S., 1995, Operation of Integrated Three-Product (Petlyuk) DistillationColumns, Ind. Eng. Chem. Res., 34,2094-2103.
Study of Energy Efficient Distillation Columns Usage for Multicomponent Separations through Process Simulation and Statistical Methods Sandra S. Florindo, Isabel M. João, João M. Silva* Chemical Engineering Department, Instituto Superior de Engenharia de Lisboa-ISEL, Instituto Politécnico de Lisboa, R. Cons. Emídio Navarro 1, 1959-007 Lisboa Portugal. [email protected]
Abstract This paper studies the optimal design conditions for the fully thermally coupled distillation columns, FTCDC, through process simulation with Aspen HYSYS and statistical methods. A fractional factorial design was used in order to screen the main operational and structural factors that minimize the total cost. Following the process characterization a steepest descent method was iteratively performed in the direction of total cost optimization. The best combination of levels of structural and operational variables obtained by the designed experiments allowed to a reduction of 9.6 % in the total costs. The factors’ levels obtained in steady state simulation were then tested in dynamic simulation. It is important to carry out the dynamic simulation to test the conditions obtained by steady state simulation in a more realistic way because it is well known that FTCDC systems are difficult to control and operate. Keywords: FTCDC, factorial design, Aspen HYSYS, Dynamic Simulation
1. Introduction Most chemical processes require the separation of mixtures of chemical components. Distillation is mainly used for liquid separations, driving nearly all other separation techniques out of the process industry. The distillation processes have a huge impact on both operation and investment costs in chemical plants and this has motivated the development of several types of fully thermally coupled distillation columns (FTCDC) that can lead to savings in energy and capital costs (Agrawal and Fidkowski, 1998). Despite the high potential of the FTCDC economic benefits, a lack of reliable design methods has contributed for the low number of commercial solutions (Caballero and Grossmann, 2006). Therefore, it is still a challenging task for engineers to define near optimal design conditions for the FTCDC in a simple and efficient manner in the initial stage of the design procedure. Several distinct configurations of the FTCDC can be implemented in commercial process simulators, but the challenge is to find optimal or near optimal solutions for the problem due to the large number of design variables of the FTCDC system which lead to tedious iterative simulations in order to find a proper structure. Additionally the trial and error simulation can lead to an inadequate structure unable to converge in the process simulation for design (Kim, 2002). Statistical methods like factorial and fractional factorial designs can be used to investigate the effects of many different factors by varying them simultaneously instead of changing one factor at time (OFAT). This is a great advantage of experimental statistical design over the OFAT traditional experimentation, because it provides a full
146
S.S. Florindo et al.
comprehension of the interactions between the design factors allowing to reach better design solutions with lower number of experiments when compared with OFAT (Montgomery, 2009).There are few studies concerning the use of statistical designed experiments in the optimization of energy efficient columns usage for multicomponent separations through process simulation in steady state (Long and Lee, 2012). A more recent study from Sangal et al. (2013) shows the use of Box–Behnken statistical design coupled with simulation for the optimization of the main process parameters in divided wall distillation columns. Simulation was coupled with statistical designed experiments in the first place to factor screening, that is to identify the main variables that affect the total cost followed by process optimization in order to find the variables’ conditions that result in a lower total cost. Knowing that FTCDC systems are difficult to control and operate (Wolff and Skogestad, 1995) it was also an aim of this work to verify if the conditions obtained by steady state simulation could be run using Aspen HYSYS in a dynamic simulation mode which translates in a more realistic way the process performance.
2. The problem– Separation of a ternary mixture In this work, we start the design using the methodology proposed by Triantafyllou and Smith (1992) by means of the preliminary design equations based on short-cut FenskeUnderwood-Gilliland-Kirkbride method (FUGK) to find the initial configuration for the Petlyuk column. It was chosen a mixture of 2-methylpropan-1-ol, butan-1-ol and butan2-ol. The design was implemented in Aspen HYSYS v7.3 and for the computation of the thermodynamic properties it was used the UNIQUAC model with the binary parameters from the Aspen HYSYS database. The Petlyuk configuration for the separation of a ternary mixture is a special case of the FTCDC. A prefractionator followed by a product column characterizes this system. For the first step of the design procedure, it was applied the short-cut distillation method (FUGK) to obtain a first approximation for the Petlyuk structure (Figure 1). In this step it was possible identify the values for the main variables of this system and it was implemented a new structure consisting on an absorber and a distillation column. The absorber corresponds to the prefractionator.The main design factors identified can be aggregated into two types: six structural related with the number of stages in the prefractionator and main column and position of the feed and draw of the midle product; and two operational related with the flow rates of liquid and vapor transferred between
Figure 1. Implementation of the FUGK method in Aspen HYSYS and identification of the main variables of the Petlyuk system.
Study of Energy Efficient Distillation Columns Usage for Multicomponent Separations 147 through Process Simulation and Statistical Methods the prefractionator and the product column. The main variables are identified and the starting values for the optimization process by design of experiments are displayedin Table 1.
3. Process optimization with designed experiments Some special types of factorial designs are very useful in process development and improvement. One of such kinds are factorials of the type 2 k with k factors, each at two levels usually referred as low level (-1) and high level (+1) of the factor. As the number of factors in a factorial experiment grows the number of effects to estimate also grows. In the present case we have a total of 8 factors and so we would need a total of 256 simulations (experiments) with no replication in order to perform all the combinations. In order to reduce the number of simulations and assuming the sparsity of effects principle a fractional factorial design can be used to obtain information on the main effects and low order interactions. The fractional factorial chosen was 2 IV8-4. In this design no main effect is aliased with any other main effect or two factor interaction being a good design to use in a screening experiment due to its high resolution. This design only requires 16 experiments reducing considerably the number of runs required for a full factorial experiment. For the fractional factorial design four generators were used, E=BCD, F=ADC, G=ABC and H=ABD. In order to interpret the results of fractional factorial designs it is necessary to take into account the alias relationships (Montgomery, 2009). For the design of experiments simulations, a variation of r1 stage was used for the structural factors and a variation of r 5 kgmol/h for the operational factors in relation to the starting values. The response variable selected was the total cost obtained with the Aspen Economic Evaluator using the default definition. With this tool, it is possible to obtain a rapid estimation of the capital and operational cost of each run. After performing the 16 experiments, the effects were estimated and a normal probability plot of the effects was built in order to graphically judge the relevance of the factors and interactions. The estimates that behave like a random sample drawn from a normal distribution have zero mean and the plotted effects will lie approximately along a straight line. Those effects that do not plot on the line are probably the significant effects as we can observe in Figure 2a).The analysis of variance (ANOVA) was performed in order to test which factors and interactions are significant. The mean Table 1. Description of the main variables and starting values Structural factors A – Position of the feed stream in the prefractionator, NF=8
D – Number of stages in the middle section of the main column, NTS2=51
B – Number of stages of the prefractionator, NTP=19
E – Position of the extraction of B product (middle), NSD=42
C – Number of stages in the top section of the main column, NTS1=15
F – Number of stages in the bottom section of the main column, NTS3=8
Operational factors G – Vapor Molar flow of the interconnection stream in the bottom, V3=280 kgmol/h
H – Liquid molar flow of the interconnection stream in the top, L1=240 kgmol/h
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square of each of the factors and interactions that did not plot on the line (i.e. a total of seven) were calculated and divided by the mean square error. Each of these ratios follows an F distribution, with the numerator degrees of freedom equal to the number of levels minus one (i.e 1 degree of freedom) an the denominator degrees of freedom equal to 8 (i.e 15-7). The computed F should be compared with the tabular value (i.e. F5%,1,8 = 5.32) and the null hypothesis is rejected if the computed F exceed the tabular value for the significance level of 5 %. After ANOVA computation we were able to conclude that all the seven, factors and interactions, affect significantly the total cost (response variable), a result already observed graphically (Figure 2a) and tested with ANOVA. We concluded that factors A, B, G and H are significant as well as the interactions AB, BG and GH. Figure 2(b) represents the plots of the AB, BG and GH interactions and A, B, G and H main effects. The plot of the interaction GH shows that the interaction is very strong, and the effect of changing H from the lower level to the higher level is dependent of the level in which factor G is settled (the interaction hide the main effects). Looking at Figure 2(b) it was easy to conclude that it is better to work with factors A and B in their higher levels in order to minimize the cost. In relation to factors G and H it is better to work with factor H in the lower level an also factor G in the lower level due to the effect of the interaction GH that is stronger than the effect of the individual factors. After performing the fractional factorial design for process characterization and once the appropriate set of structural and operational factors as well as their levels is identified, the next step was the process optimization in order to find the set of conditions that result in the lowest total cost. In order to optimize we used the method of steepest descent, which is a procedure for moving sequentially along the path of steepest descent that is in the direction of the minimization of the response. A second cycle of simulations were performed varying the factors considered significant. The experiments were conducted along the path of steepest descent with a full factorial design24 with the factors A, B, G, and H varying in the direction of the better level in which the total cost reduces. The results of ANOVA for a level of significance of 5 % allowed to the conclusion that the factors A, B, G and H still affect the response. After that, a third designed experiment was performed in order to further move along the path of steepest descent. After the whole process of optimization, the better conditions of the eight factors (structural and operational variables) in order to minimize the total cost are:
Figure 2. Factorial fractional experiments results: a) Probability plot to identify the significant factors and interactions; b) Influence of the factors and interactions level in the total cost.
Study of Energy Efficient Distillation Columns Usage for Multicomponent Separations 149 through Process Simulation and Statistical Methods A –NF=13; B –NTP=32; C –NTS1=15; D –NTS2=51; E –NSD=42; F –NTS3=8; G – V3=265kgmol/h; H – L1 = 225 kgmol/h. The use of these conditions in the Petlyuk column allows a reduction of the total cost of 9.6 % relative to the starting values.
4. Dynamic simulation After the statistical design experiments performed, the optimal solution obtained was tested by dynamic simulation using the Aspen HYSYS in dynamic mode. This type of system is more complex than a traditional distillation column with more degrees of freedom. Taking in consideration the control objective and previous works done by Wolf and Skogestad (1995) and Hwang et al. (2011) was chosen a LV configuration with SISO feedback control loops, adapted from the LV configuration used in traditional distillation columns. The control structure implemented present eight control loops. The control loop for pressure with the pressure in the top stage of the main column is the variable to control and the manipulated variable is the flow of utility used in the condenser. The two levels control loops in the condenser and reboiler use the flow rates of the distillate and bottom products as manipulated variables respectively. The composition of the distillate is controlled by manipulating the reflux flow rate. The composition of the bottom product is adjusted by manipulating the flow rate of the utility used in the reboiler. For the control of the composition of the middle product a cascade control loop was adopted were the set point for the side draw flow rate was obtained by the control of the composition of this stream. The two remaining control loops concern the control of the flow rates of interconnection streams between the prefractionator and the main column. One is for the liquid stream leaving the main column and the other is for the vapor stream exit from the main column. Figure 3 presents the simulation of the control loops. All the controllers are configured as PI and the parameters were obtained by the internal auto-tuning function. In the auto-tuning, it is possible to adjust the relay hysteresis to take into account the non-linearity observed in the distillation columns. As an example of the performance obtained with this control configuration, it is showed in Figure 4 the response of the composition control loops in the bottom and middle products to a change in the set-point. The control of the FTCDC system is more complex than in a traditional distillation column and the results obtained showed that the response time of the system is higher when compared with a system of two columns for the same separation, but even so possible to control. The FTCDC control can be improved using model predictive control strategies.
Figure 3. Implementation of FTCDC in Aspen HYSYS with control loops.
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Figure 4. Response of the composition control loops for the middle and bottom products.
5. Conclusions The combination of statistical tools like design of experiments proved to be useful in the simulation of multicomponent separation reducing the number of simulation runs to achieve an optimized solution for a particular problem. In the case of this Petlyuk column a reduction of the total cost estimation of almost 10 % was obtained with a reduced number of runs. The dynamic simulation of the optimized Petlyuk column shows the possibility of operation of this system, but it is necessary to implement new control strategies to overcome the high response time observed. The use of model predictive control can be a strategy to improve the performance of these systems.
Acknowledgements I.M. João is also with CEG-IST and J.M. Silva is also with CRERG-IBB, research units of Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
References R. Agrawal, Z.T. Fidkowski, 1998, Are thermally coupled distillation columns always thermodynamically more efficient for ternary distillations?, Ind. Eng. Chem. Res., 37, 8, 3444–3454. J.A. Caballero, I.E. Grossmann, 2006, Structural considerations and modeling in the synthesis of heat-integrated−thermally coupled distillation sequences, Ind. Eng. Chem. Res., 45, 25, 8454– 8474. K.S. Hwang, B.C. Kim, Y.H. Kim, 2011, Design and Control of a Fully Thermally Coupled Distillation Column Modified from a Conventional System, Chem. Eng. Technol., 34, 2, 273– 281. Y.H. Kim, 2002, Structural design and operation of a fully thermally coupled distillation column, Chem. Eng. J., 85, 2–3, 289–301. N.V.D. Long, M. Lee, 2012, Design and optimization of a dividing wall column by factorial design, Korean J. Chem. Eng., 29, 5, 567-573. D.C. Montgomery, 2009, Introduction to Statistical Quality Control, 6th ed. John Wiley & Sons, Inc., Hoboken, NJ, USA. V.K. Sangal, V. Kumar, I.M. Mishra, 2013, Optimization of a divided wall columnfor the separation of c4-c6 normalparaffin mixture using box-behnkendesign, Chem. Ind. Chem. Eng. Q., 19, 1, 107−119. C. Triantafyllou, R. Smith, 1992, The design and optimization of fully thermally coupled distillation, Trans IChemE, 70, Part A, 118–132. E.A. Wolff, S. Skogestad S., 1995, Operation of Integrated Three-Product (Petlyuk) DistillationColumns, Ind. Eng. Chem. Res., 34,2094-2103.