MULTI-OBJECTIVE OPTIMIZATION OF A FLUIDIZED BED CATALYTIC CRACKER UNIT TO MINIMIZE CO 2 EMISSIONS

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1 MULTI-OBJECTIVE OPTIMIZATION OF A FLUIDIZED BED CATALYTIC CRACKER UNIT TO MINIMIZE CO 2 EMISSIONS Mohmmad A. Al-Mayyahi* 1, Andrew F. A. Hoadley 1, Nicholas E. Smith 1, and G. P. Rangaiah 2 1 Department of Chemical Engineering, Monash University, Clayton, VIC 3800 Australia 2 Department of Chemical & Biomolecular Engineering National University of Singapore, Singapore * mohmmad.mayyahi@eng.monash.edu.au Keywords: FCC; Multi-objective optimization; NSGA-II; CO 2 emissions. ABSTRACT Fluidized bed catalytic cracker (FCC) is one of the major CO 2 emitters in oil refineries due to the large amount of CO 2 produced from the catalyst regenerating system. Minimizing the CO 2 emissions from FCC has direct impacts on many other economical and operational objectives such as total profit, total conversion, and product yields. In order to investigate the trade-off between the CO 2 emissions and other conflicting objectives, this paper presents the results of a multi-objective optimization (MOO) of a rigorous model of the FCC unit. The FCC model includes, in addition to the reactor/regenerator section, the feed preheat train, main fractionator and flue gas heat and power recovery sections. A binary coded elitist non-dominated sorting genetic algorithm (NSGA-II) has been used to generate a set of optimal non-dominated solutions (Pareto front). The pinch analysis represented by the Grand Composite Curve (GCC) has been integrated with the FCC model to minimize the external hot utilities required by maximizing the energy recovery in the FCC unit. The resulted optimal solutions in terms of Pareto curves for the FCC model are presented and their significant features are discussed. Implementing the multi-objective optimization to the FCC unit provides important physical insights into the decision making process regarding economic targets and associated emissions limits. INTRODUCTION The objective of Fluidized bed Catalytic Cracking (FCC) is to convert heavy petroleum cuts such as vacuum gas oils and residues into more valuable, lower molecular-weight products such as gasoline and light products. Due to its significant effects on refinery profitability, FCC has been one of the key operations in modern integrated refineries, and therefore the profit optimization of FCC processes is of great importance. The FCC is also one of the major sources of emissions of greenhouse gases in oil refineries. The majority of these emissions are CO 2 from catalyst regeneration where coke on the spent catalyst is burnt off to restore the catalyst activity and provide the heat required to reactor/riser reactions. However, only a quarter of the heat available from coke combustion is used for the endothermic reactions, whilst a significant amount of heat is lost to atmosphere mainly via flue gas (Mertens, 2010). Furthermore, FCC indirectly contributes to the global CO 2 emissions due to the consumption of utilities such as steam and electric power. All these different sources of emissions should be

2 considered to accurately assess CO 2 emissions and to identify potential reduction through optimization of FCC processes. Mertens (2010) suggested different areas for potential reduction in CO 2 emissions associated with FCC and found that significant reduction in CO 2 emissions can be achieved by improving energy efficiency of the FCC unit. The minimization of CO 2 emissions from FCC processes is influenced by many operating and design variables, and conflicts may arise with other objectives such as profit and product yields. Multi-objective optimization (MOO), therefore, needs to be carried out in order to optimize such conflicting and non-commensurate objectives. Several studies have been published in the open literature regarding optimization of FCC units. However, most of these studies were limited to single objective optimization to optimize a sole objective such as profit, total conversion and a product yield. A detailed review of studies on the single objective optimization of FCC processes is provided by Han et al (2004). The first attempt to optimize the FCC unit using MOO was by Kasat et al (2002). The elitist non-dominated sorting genetic algorithm (NSGA-II) was used to optimize simultaneously three different objectives of FCC unit, which was represented by a fivelump empirical model. The gasoline yield, the air flow rate, and the percent carbon monoxide, CO in the flue gas were simultaneously optimized in different two and threeobjective problems. Kasat and Gupta (2003) compared optimal solutions of MOO of a five-lump FCC model obtained by two different versions of NSGA-II. Two objectives were used to simultaneously maximize gasoline yield and minimize coke formed on the catalyst during the cracking process. The NSGA along with two different kinetic lump models for FCC were used by Dave and Zhang (2003) to maximize gasoline production and minimize CO emissions from the regenerator. Later, a modified multi-objective simulated annealing, MOSA algorithm was used by Sankararao and Gupta (2007) to optimize gasoline yield, CO in the flue gas and the air flow rate in different MOO problems. Although these past studies represent important contributions that demonstrate the value of MOO approach for the FCC process, many important operational and environmental objectives have not been studied. Minimization of CO 2 emissions is one of the most important objectives to be considered in the present study. Furthermore, FCC models used in the previous studies were limited to the reactor/riser and regenerator, and ignored the associated sections such as flue gas heat recovery system. In the present study, the MOO is implemented for a rigorous model of an FCC unit, which includes, in addition to the reactor/regenerator section, the feed preheat train, main fractionator and flue gas heat and power recovery sections. The total CO 2 emissions are minimized simultaneously with naphtha yield maintained as an economic objective. Consideration of the wider process should yield a more valid optimization of economic and environmental objectives for the FCC unit. FCC PROCESS OVERVIEW Figure 1 shows a simplified process flow diagram of a typical FCC unit. A steady-state FCC model has been simulated using Aspen HYSYS Petroleum Refining software (AspenTech, 2010). After being heated by the pre-heat train and a fired heater, the FCC feedstock is injected into the bottom of the riser. Once it contacts the hot reactivated 1

3 catalyst flowing from the regenerator, the liquid feed is vaporized and then cracked inside the vertical riser pipe to produce lighter products. The reaction products and the catalyst leave the riser to the disengagement vessel where they are separated. The products continue to the main fractionator for separation while the spent catalyst is returned to the regenerator for restoring its catalytic activity. In the regenerator, preheated air from a main blower is mixed with the spent catalyst to burn off the coke from the catalyst. As a result of this combustion, pressurized hot flue gas consisting mainly of CO 2, CO, N 2 and water vapor is produced. An expansion turbine is used to reduce the high pressure of the flue gas generating electric power to run the main air blower, whilst a waste heat boiler is used to generate high pressure steam from the hot, depressurized flue gas. In the main fractionator, the reaction products from the reactor are separated into various products, namely, light gases, naphtha, Light Cycle Oil (LCO), Heavy Cycle Oil (HCO), and slurry oil. The 13-stage fractionator is equipped with four pump-arounds, two side strippers for LCO and HCO products, and an overhead reflux system (Figure1). OPTIMIZATION ALGORITHM Fig. 1: Simplified process flow diagram of the FCC Different evolutionary algorithms such as the genetic algorithm (GA) and differential evolution have been used to solve multi-objective problems (Deb, 2001). These techniques are population-based, stochastic search techniques. The elitist nondominated sorting genetic algorithm (NSGA-II) developed by Deb et al (2002) is one of these techniques. The NSGA-II has many advantages over the previous multi-objective GAs including its ability to maintain a better spread of solutions and achieve faster convergence to non-dominated solutions (Deb et al, 2002). In the present study, an Excel version of a binary coded NSGA-II is used to solve the MOO problem and obtain Pareto-optimal solutions (Sharma et al, 2011). After linking this program to Aspen Hysys, preliminary experimentation was undertaken to obtain suitable values of the computational parameters in the NSGA-II. These values are: population size = 50, maximum generations = 35, random number seed = 0.857, crossover probability = 0.8 and mutation probability = The typical computational 2

4 time for the MOO of FC unit in this study was about 3 hr on a Compaq Pentium 4 (3.16 GHz) machine. OBJECTIVE FUNCTIONS A key role of the FCC unit in an oil refinery is to upgrade heavier products and to increase the yield of naphtha. FCC naphtha is often the main component in refinery gasoline (Gary and Handwerk, 2001). Thus the naphtha yield has a direct influence on the economic status of the FCC unit. Therefore, in this study, Naphtha Yield is the economic objective to be maximized, whilst the Total CO 2 Emission is the second objective function in the MOO optimization and is an environmental objective to be simultaneously minimized. As mentioned earlier, there are two main sources for the emissions from FCC processes: flue gas from the regenerator and utilities consumption. Emissions associated with utilities consumptions are based on pinch analysis after maximizing energy recovery at the FCC unit. A Grand Composite Curve (GCC) is formulated for the FCC model to provide information about heating and cooling requirements (Al-Mayyahi et al, 2011). Using GCC, the amount of heat to be supplied by the fired heater and the consequential CO 2 emissions are calculated. Global CO 2 emissions associated with electric power consumption/generation are also included based on black coal emissions intensity (0.92 tons CO 2 /MWh) (Golonka, 1996). DECISION VARIABLES AND CONSTRAINTS Among operational variables which determine the economical and operational performance of FCC processes, four important variables have been chosen. These variables are the feed preheat temperature, the riser temperature, the combustion air temperature, and the air mass flowrate. The feed preheat temperature is affected by energy recovery and fired heater duty, the latter of which determines fuel consumption which contributes to CO 2 emissions. The riser temperature is important as it affects the regenerator temperature, catalyst circulation rate, total conversion and products yields (Meyers, 2000). The flow rate and temperature of combustion air have direct impact on regenerator performance and the energy balance of the FCC. Each of the four variables is allowed to vary within specified ranges whilst also keeping four operational constraints within acceptable limits. The four constraints are coke on the regenerator catalyst, temperature of the regenerator, CO% in the flue gas, and 95% cut point of naphtha product. Decision variables, constraints and their limits used in the FCC optimization are listed in Table 1. RESULTS Sensitivity Analysis A sensitivity analysis of the objective functions with respect to each decision variable has been performed to compare the influence of decision variables. Each decision variable was varied one at a time within a specified range, whilst the other variables were held constant at the value which gave the minimum CO 2 emissions for their allowed ranges. In Figure 2, both objective functions are plotted on the vertical axis against each variable. Figure 2a shows that an increase in feed (preheat) temperature simultaneously decreases the total CO 2 emissions and the naphtha yield. The riser temperature has the greatest impact on both objectives as shown in Figure 2b; both 3

5 objectives increase up to a riser temperature of 520 o C. Above this, naphtha yield decreases whilst CO 2 emissions continue to increase linearly. Air T and F have relatively less effect on the two objectives considered. Tab. 1: Decision variables and constraints used for FCC optimization Units Lower Limit Upper Limit Decision Variables Feed T o C Riser T o C Air T o C Air F kg/h 1.5 x x10 5 Constraints Coke on Regenerator Catalyst % < 0.1 Temperature of Regenerator o C < 717 CO% in Flue gas % < 0.2 Naphtha cut point o C > 210 In order to quantify the influence of each operational parameter on both objectives, three values are calculated based on slopes of linear trend lines that can be drawn for each set of data in Figure 2. The first value, S1, represents the influence of a decision variable on the first objective, CO 2 emissions, the second value S2 is the influence of a decision variable on the second objective, naphtha yield, and the third value, S3, represents the trade-off influence on both objectives (see equations 1 to 3). Here, Objmax and Objmin are the maximum and minimum values of objective corresponding to values, Vi 1 and Vi 2 of variable i respectively. Based on calculated values of S1 and S2, Table 2 shows that riser temperature has the most significant influences on both objectives whilst air temperature shows the weakest individual influences. In order to maximize naphtha yield, S3 values of the four decision variables show that decreasing air temperature results in minimum consequential CO 2 emissions compared with increasing air flow rate which gives the highest emissions penalty. Tab. 2: Influence factors of decision variables Influence Factors Feed T Riser T * Air T Air F S S S * The values has been calculated based on the linear section in Figure 2b 4

6 Multi-objective Optimization Fig. 2: Effect of decision variables on objectives Figure 3 shows results of MOO which simultaneously maximizes naphtha yield and minimizes CO 2 emissions. The non-dominated solutions (i.e., optimal Pareto front) are presented in Figure 3a. A contradictory behavior between the two objectives can be observed, i.e., increasing naphtha yield results in increasing CO 2 emissions. The nondominated solutions in Figure 3a show that an increase of 7.7% in the naphtha yield results in 20.4% increase in CO 2 emissions. The corresponding results of decision variables are shown in Figures 3b to 3e. Plots of feed preheat temperature and riser temperature in Figures 3b and 3c, respectively, show clearer trends compared to either the air flow rate or air temperature (Figure 3d and 3e). Starting from the minimum CO 2 emissions point of the Pareto front in Figure 3b, the feed temperature drops sharply from its upper bound to its lower bound at an emissions value of about 29t/h; then it remains close to the lower bound until the end of Pareto range. By contrast, riser temperature in Figure 3c increases from its lower bound of 480 o C to a maximum value of around 520 o C at emissions of about 33t/h. Air temperature and flow rate show almost the same behaviour (Figures 3d and 3e); both these values are scattered close to the respective lower bound for almost the whole range of optimal solutions. However, more scattering is observed at minimum emissions for air temperature and at maximum naphtha for the air flow rate. Different components of CO 2 emissions representing the two main emissions sources are individually plotted against the total CO 2 emissions in Figure 4. Two main segments can be observed from these plots of the two emissions components, at below and above the emissions value of 29t/h. For the first section, CO 2 emissions associated with the fired heater decreases while regenerator emissions sharply increase. Above the 5

7 transition value, the utilities-related emissions stayed constant at about 2t/h whilst emissions from the regenerator increase less sharply. Fig. 3: MOO results of FCC unit Fig. 4: Components of CO 2 emissions in the optimal solutions Pareto-optimal Solutions for Extended Ranges of Decision Variables The Pareto-optimal solutions are produced for extended ranges of decision variables in order to investigate the effects of changing the limits of operating variables on the final solutions. Firstly, the lower limit of feed temperature is reduced from 320 to 270 o C. 6

8 Then, the lower limit of riser temperature is reduced from 480 o C to 450 o C. The optimal solutions for these two cases are separately generated and shown in Figure 5 along with the Pareto of base case for comparison. The results show that better and wider optimal solutions are produced by extending the lower limits of the two variables. Reducing the lower limit of feed temperature has improved the solutions in the upper part, higher than 29t/h CO 2 emissions, of the Pareto of the base case, whereby higher naphtha yields can be achieved at the same amount of emissions. Furthermore, maximum naphtha yield of 47.5% can be achieved compared to the base case which has no solution having naphtha yield higher than 47%. On the other hand, decreasing the lower limit of riser temperature significantly extended the lower part, less than 29t/h CO 2 emissions, of the original Pareto, whereby minimum CO 2 emissions of 24.6t/h were achieved compared to 27.4t/h in the base case. DISCUSSION Fig. 5: Optimal Paretos for extended ranges of decision variables. A smooth and well distributed optimal Pareto front was obtained as shown in Figure 3a. This reveals the contradictory nature of the two objectives, i.e., when naphtha yield increases, more CO 2 emissions are produced. The MOO solutions in Figure 3a show that high naphtha yield can only be achieved with a much higher emissions penalty. More interestingly, the optimal values of decision variables change in according to descending order of magnitude of S3 as naphtha yield increases (Figure 3); starting from minimum CO 2 emissions, decision variables having higher S3 change first keeping the other decision variables with lower S3 at constant values which give minimum emissions. Thus, the non-dominated front shows that air and feed temperatures are the controlling variables for the lower part of the Pareto whilst the upper part of the Pareto solutions are due to riser temperature and air flow rate (Figures 3a-e). In terms of S3, sensitivity analysis of the four decision variables shows that the air temperature has the highest value among the four decision variables (Table 2). However, its individual influences on each objective, S1 and S2, are the lowest compared to the remaining variables helping to explain the scatter exhibited in MOO 7

9 solutions for air temperature at low emissions (Figure 3d) is not reflected in the Pareto front of Figure 3a. Similarly, air flow rate also shows relatively weak individual influence on both objectives (Figure 2d and S1, S2 values in Table 2), with consequent scatter in its MOO results (Figure 3e). On the other hand, optimal values of feed preheat temperature and riser temperature show smoother trends compared to air temperature and flow rate (Figures 3b and 3c). Sensitivity analysis results show that riser temperature is more significant than feed temperature in individual influences on both objectives (Table 2). However, feed temperature has a higher value of S3 which results in lower incremental CO 2 emissions per incremental increase in naphtha yield when compared to increasing riser temperature and leads to it being varied ahead of riser temperature in MOO solutions (Figure 3b and 3c). Decreasing the FCC feed temperature increases the catalyst/feed ratio required to achieve the specified riser outlet temperature (Meyers, 2000). Consequently, the increased catalyst circulation rate causes increased naphtha yield. However, a simultaneous increase in coke yield in terms of CO 2 emissions is noticed as can be seen in Figure 4b. On the other hand, a lower feed temperature reduces the required heat from the fired heater (Figure 4a). Compared with decreasing the FCC feed temperature, increasing the riser temperature sharply increases both the naphtha yield and CO 2 emissions. However, above riser temperature of about 520 o C, naphtha yield gradually stops increasing and ultimately decreases whilst CO 2 emissions keep linearly increasing (Figure 2b). This ends the contradiction between the two objectives, naphtha yield and CO 2 emissions, for riser temperature values above 520 o C which can be clearly seen in Figure 3c, where maximum riser temperature plateaus around 520 o C, below its upper constraint of 540 o C. Beyond a certain riser temperature, naphtha yield is negatively impacted in favour of increasing dry gas and C3 yields (Meyers, 2000). The corresponding plots of optimal values of decision variables in Figures 3b-e show agreement with the results of sensitivity analysis. However, MOO results show more clearly that the feed temperature and riser temperature are the major controlling variables for a limited part of the entire Pareto range whilst other variables remain relatively constant or are scattered around certain value. Starting close to the minimum CO 2 emissions until an emissions value of 29t/h, feed temperature is the major controlling decision variable where it decreases from the upper limit of 400 o C to the lower limit of 320 o C, and then it remains at this value until the end of the Pareto range (Figure 3b). The riser temperature is changed next where it increases from the lower bound to the maximum value of about 520 o C during the Pareto range between 29t/h and 32t/h CO 2 emissions (Figure 3c). Despite the scattered behaviour of air temperature and flow rate, which can be attributed to their weak individual influences on both objectives or due to compensatory effects among decision variables (Tarafder et al., 2005), the corresponding MOO results (Figures 3d and e) show that the majority of points is scattered close to the lower limit. Maximum air flow rate, however, is ultimately required at maximum naphtha yield and maximum air temperature is necessary for minimum CO 2 emissions. In general, MOO results show that decision variables having significant individual influences on both objectives control wider part of Pareto solutions than decision variables with less significant individual influences. Extending the limits of decision 8

10 variables with higher S1 and S2, therefore, results in a wider spread of the Paretooptimal solutions, as can be seen in Figure 5. Figure 4 shows that the contribution of the fired heater to the total CO 2 emissions is as high as 5t/h. Further reduction in the furnace related emissions can be achieved by improving energy efficiency of the FCC. The later objective can be met by optimizing heat recovery from the pump around circuits by increasing their number and/or optimizing their positions (Golden and Fulton, 2000). CONCLUSION Multi-objective optimization was successfully implemented on a rigorous and comprehensive model of a fluidized bed catalytic cracker unit. Two objectives have been optimized considering four decision variables and four constraints, using a binary coded genetic algorithm, NSGA-II. Naphtha yield as an economic objective was maximized against minimizing total CO 2 emissions as the environmental objective. The total CO 2 emissions have been estimated by including the direct emissions from the regenerator, the associated emissions of hot utilities consumption and electric power generation. The results showed that including the indirect CO 2 emissions by integrating the pinch analysis approach with the MOO is beneficial especially for decision variables having opposite impact on different components of the total CO 2 emissions. It has been shown that sensitivity analysis is quite helpful in predicting, interpreting and explaining the trends of optimal Pareto curve and decisions variables. Furthermore, the results indicate that the relative impacts on both objectives provide important indications for the trade-off of decision variables in the simultaneous optimization of two objectives. However, interactions among decision variables in more complicated cases with a larger number of decision variables would make the interpretation of the optimization results more difficult. REFERENCES Al-Mayyahi M.A., Hoadley A.F.A., Smith N.E., and Rangaiah G.P. (2011) Investigating the trade-off between operating revenue and CO 2 emissions from crude oil distillation using a blend of two crudes. Fuel, In Press. Aspen Tech, (2010). AspenONE V7.0. Cambridge, MA: Aspen Technology Inc., Dave D. and Zhang N. (2003) Multiobjective Optimization of Fluid Catalytic Cracker Unit Using Genetic Algorithms. Computer Aided Chemical Engineereing 14: Deb K. (2001) Multiobjective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., Chichester, UK. Deb K., Pratap A., Agarwal S., and Meyarivan T. (2002) Fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolutionary Comp.;6: Gary J.H., and Handwerk G.E. (2001) Petroleum Refining: Technology and Economics, 4th ed. Marcel Dekker, New York 9

11 Golden, S.W., and Fulton S. (2000) Low-cost methods to improve FCCU energy efficiency. Petroleum Technical Quarterly: Golonka K.A, Brennan D.J. (1996) Application of life cycle assesment to process selection for pollutant treatment: a case study of sulfur dioxide emissions from Australia metallurgical smelters. Trans Inst Chem Eng;74(B): Han I., Riggs J.B., and Chung C. (2004) Modeling and optimization of a fluidized catalytic cracking process under full and partial combustion modes. Chemical Engineering and Processing 43: Kasat R.B. and Gupta S.K. (2003) Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator. Computers & Chemical Engineering 27(12): Kasat R.B., Kunzru D., Saraf D. N., and Gupta S.K. (2002) Multiobjective Optimization of Industrial FCC Units Using Elitist Nondominated Sorting Genetic Algorithm. Industrial & Engineering Chemistry Research 41(19): Mertens, J. (2010) Rising to the CO 2 challenge. Part 3: CO 2 emissions reduction options in refineries. Hydrocarbon Engineering. Meyers R. A. (2000) Handbook of Petroleum Refining, McGraw-Hill. Sankararao B., and Gupta S.K. (2007) Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using two jumping gene adaptations of simulated annealing. Computers & Chemical Engineering 31 (11): Sharama S., Rangaiah G.P., and Cheah K.S. (2011) Multi-Objective Optimization Using MS Excel with an Application to Design of a Falling-Film Evaporator System. Food and Bioproducts Processing, in press. Tarafder A., Lee B.C.S., Ray A.K., and Rangaiah G.P. (2005) Multiobjective Optimization of an Industrial Ethylene Reactor Using a Nondominated Sorting Genetic Algorithm. Industrial & Engineering Chemistry Research 44: BRIEF BIOGRAGHY OF PRESENTER Mohmmad Al-Mayyahi is a PhD student in Chemical Engineering at the Monash University. He is working on multi-objective optimization of petroleum refining processes. He has Masters degree in chemical engineering from Basrah University. He worked for four years in a petrochemical company in Iraq. He also holds a lecturing position at Basrah Technical College, Iraq. 10