System Identification and Analysis to Optimize Plant Design of Aniline

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System Identification and Analysis to Optimize Plant Design of Aniline Afifah bt Dzulkifli 1, Ahmmed Saadi Ibrahem1* Article Info Received:15 th February 2012 Accepted: 3 th March 2012 Published

my ISSN: 2232-1179 2012 Design for Scientific Renaissance All rights reserved ABSTRACT In this work, system identification method is used to capture the reactor characteristics of production rate of

The identification method is used to measure the percentage effect on the production rate of Aniline by measuring the effect of input factors of reaction temperature, Ammonia concentration, and

1 System Identification and Analysis to Optimize Plant Design of Aniline Afifah bt Dzulkifli 1, Ahmmed Saadi Ibrahem1* Article Info Received:15 th February 2012 Accepted: 3 th March 2012 Published online: 6 th April 2012 Universiti Teknologi MARA (UiTM) Chemical Engineering Department -University40000-Shah Alam-Mlayasia ahmmed_ibrahem@salam.uitm.edu.my ISSN: Design for Scientific Renaissance All rights reserved ABSTRACT In this work, system identification method is used to capture the reactor characteristics of production rate of Aniline based on mathematical model by using hysys software. The identification method is used to measure the percentage effect on the production rate of Aniline by measuring the effect of input factors of reaction temperature, Ammonia concentration, and phenol concentration. Reaction temperature and ammonia concentration both have a large effects on the output of the system. However, eventhough phenol concentration has a large effect on the output of the system but the effect is less than the reaction temperature and ammonia concentration. All these results depend on model of hysys software and these results are very important in industrial plants. Keywords: System identification, reaction, temperature, ammonia concentration 1. Introduction Aniline is an oily liquid poisonous amine C 6 H 5 NH 2 obtained especially by the reduction of nitrobenzene and used briefly in organic synthesis as of dyes. (Merriam Webster, 2011). Aniline was first obtained from the destructive distillation of indigo in 1826 by O.Unverdorben, who named it crystalline. Since 1854 aniline has become an important commercial intermediate for isocyanates, rubber chemicals, agricultural chemicals, pharmaceuticals and dyes (Lawrence, 1996). Currently, the largest market for aniline is the preparation of methylene diphenyl diisocyanate (MDI), which consumed 85% of the aniline market. MDI based polyurethanes

2 products are used primarily in the construction, appliance, housing and automotive industries. The second largest end used of aniline is as an intermediate for rubber processing chemical such as vulcanization accelerators, antioxidant and stabilizer. In agriculture industry, aniline is also being used in the chemical application such as herbicides, fungicides and defoliants.(wong E.W & Birkhoff R, 2005). The United States, Asia-Pacific, and Europe dominate the global demand for aniline, with a combined share of more than 90%, as stated by the new market research report on aniline market. The recovery of the global economy is expected to boost demand for various downstream chemical products including aniline. Aniline witnessed the highest rate of growth among aromatic compounds during the period due to booming automotive, construction and electronics industries, particularly in Asia-Pacific markets. (Global Industry Analysts, 2010). Global Industry Analysis has announces that the global aniline market is projected to reach 6.2 million tons by the year 2015, led by the increasing demand from various end-user markets. Due to the 7% increment per year of the aniline production in Asia-Pacific, it is expected that the production of aniline in Asia-Pacific in 2011 is reaching 1.41million tonnes/year and the Asia-Pacific consumption estimated reaching 1million tonnes/year. (ICIS, 2008). There are many ways of producing Aniline for industrial purposes such as by the reduction of mononitrobenzene (Wong E.W & Birkhoff R, 2005), hydrogenation of nitrobenzene, aminolysis of phenol, amination of phenol and many more. However, this study will be focused on the aminolysis of phenol since it is favorable because of the low cost of phenol is available and high purity of aniline is desired. Aminolysis of phenol usually operated at an operating temperature of 180 to 220 o C and at pressure greater than the vapor pressure of the reactant. This reaction is carried out in the liquid phase with copper compounds as catalyst. C 6 H 5 OH + NH 3 C 6 H 5 NH 2 + H 2 O Aminolysis of phenol started with preheated the mixed vapor feed of phenol and ammonia and passed it to the fixed catalyst bed. A unique HALCON developed catalyst is used. The reactor effluent is partially condensed then it being sent to an ammonia recovery still. The ammonia is then being recycled where the ammonia recovery still bottoms are dried by distillation and the aniline is removed overhead in purification still. Capital cost is low because the nitration of benzene is avoided and water disposal problems are minimal. (Kent, 2003). 62

3 2. Methodology The system identification is purposely done to build a dynamic model from an input or output data. The system did not use any laws concerning the fundamental nature and properties of the nonlinear system. (Ahmmed S.Ibrehem, 2011). The first step for the system identification is to model the Aminolysis of Phenol process in the simulation software. For the purpose of the study, the mass and energy balance for Aminolysis of Phenol must be calculated before the aminolysis of phenol can be designed in the Aspen Hysis software. The process flow diagram as below Figure 1 is obtained. M-1 R-1 E-1 E-2 V-1 V-2 Mixer CSTR Cooler Heater Separator Separator 2 WASTE 1 P-1 1 E-1 4 M-1 WASTE 2 R-1 V-1 Waste 5 7 V-2 E-2 ANILINE Fig. 1: Process Flow Diagram of the Aminolysis of Phenol 3. Mass Balance Law of conversion of mass stated that mass can neither be created nor destroyed. This statement can be conveyed as total mass input = total mass output. In this research, mass balance for all equipments must be calculated. Then the value obtained from the mass balance calculation need to be compared with value of the simulation process which is inside the Aspen Hysis. For aniline production process, basis 100 kmol/hr has been used for the calculation. In mathematically, the general mass balance or material balance can be written as below: 63 Input + Generation Output Consumption = Accumulation

4 The assumptions for the mass balance are as followed: All streams are in steady state condition Reaction only take place inside the reactor No side reaction or impurities inside the reactor Uniform physical properties along each system 4. Calculation of Mass Balance Aniline Production = kg/hr Molecular Weight of Aniline = kg/kmol Production Rate of Aniline = kmol/hr In the real design, the reactor used is fluidized bed reactor (PBR). However, there are no PBR available in the Aspen Hysis software. The function of PBR in the process have the same behavior as three to five CSTR that are arranged simultaneously. (H. Hatzantonis et al, 1998). For the sake of simulation purposes, the PBR is being replaced with continuous stirred tank reactor (CSTR). Figure 2 shows the CSTR with stream 3 as inlet and stream 4 as the oulet. It is assumed that the conversion of the reaction is 100%. 64

5 S3 S4 n 3 =100 kmol/hr a 3 =0.5 mol P/mol b 3 =0.5 mol N/mol Fig. 2: Continuous Stirred Tank Reactor c 4 n 4 = 1 x a3 x n3 = 1 x 0.5 x 100 c 4 n 4 = 50 kmol A/hr Phenol will produce same amount of water as aniline Therefore; d 4 n 4 = 50 kmol H/hr a 4 n 4 = (1-1) x a3 x n3 = 0 x 0.5 x 100 a 4 n 4 = 0 kmol P/hr b 4 n 4 = (1-1) x b3 x n3 = 0 x 0.5 x 100 b 4 n 4 = 0 kmol N/hr Total Molar Flow: n 4 = n 4 = 100 kmol/hr 65

6 5. Composition: c 4 d 4 Table 1 shows the summary of the mass balance around the reactor. As assume it is 100% converted therefore there are none of the phenol and ammonia left in the outlet stream. Phenol and ammonia are converted into 0.5 mol of aniline and 0.5 mol of water. Table 1: Summary of Mass Balance around the Reactor Component In Out Feed 100 kmol/hr 100 kmol/hr Phenol 0.5 mol P/mol 0 Ammonia 0.5 mol N/mol 0 Aniline mol A/mol Water mol H/mol 6. Energy Balance The calculation on energy balance of the process is the last procedure before the system identification can be done. The energy balance is the measure of the energy consumed in the plant. For this research, the energy balance is carried out for the CSTR reactor. The assumption for the energy balance are: 1. The system is an unsteady state. 2. Since the effect of pressure difference to the energy balance in the process gives a very small value as compare to the values contributed by the sensible heat and the heat of formation, heat obtained from the pressure difference is assumed to be negligible. 66

7 7. Equation used in calculations 7.1 General Equations (1) Based on the assumptions stated above, (2) Hence, equation (1) can be reduced to (3) 7.2 Equation for Reactive Process 7.3 Equation for Process with Phase Changes (4) 7.4 Equation for Non-reactive Process (5) 7.5 Equation for Heat Capacity, C p (6) 67

8 Hence for sensible heat, 7.6 Total Heat for the Energy Balance ( for non-reactive process): (7) 8. Calculation of Energy balance at the Reactor Figure 3 shows the continuous stirred tank reactor where the inlet only consist of aniline and water and Table 2 shows the parameters needed for energy balance around the reactor K K Fig. 3 : Continuous Stirred Tank Reactor 68

9 Table 2 : Parameters for Reactor Component ṅ in,3 ṅ out,4 kmol/hr kj/mol kmol/hr kj/mol Phenol Ammonia Aniline Water Enthalpy at Reactor Input For Phenol Where 69

10 For Ammonia Where Enthalpy at Reactor Output For Aniline Where 70

11 For Water Where Heat of reaction is calculated as below: Multiply the stoichiometry constant of the reaction with the enthalpy of formation for each of the compound in the reaction equation. Phenol: = 1 = kj/kmol Ammonia: = 1 = kj/kmol Aniline: = 1 = 86.7 kj/kmol 71

12 Water: = 1 = kj/kmol Heat of reaction, : = ( ) kj/kmol - ( ) kj/kmol = kj/kmol C 6 H 5 OH + NH 3 C 6 H 5 NH 2 + H 2 O Equal to: P = kpa ; T =180 C Mole fraction of A = 0.5 Mole fraction of B = 0.5 F Ao = 0.5 F To = 0.5 (100 kmol/hr) = 50 kmol/hr F Bo = 0.5 F To = 0.5 (100 kmol/hr) = 50 kmol/hr = v = o - v (8) (9) When neglecting the pressure drop across the system, equation will become: Substitute equation 9 into equation 8: = v = o - v o - o 72

13 = v = o - - o (10) C Ao = y Ao C To = y Ao =... = kmol/m 3 ε = y Ao δ = )) = 0 By using equation 10: C A = = kmol/m 3 C B = kmol/m 3 C C = = kmol/m 3 C D = kmol/ m 3 2 -r A = k C A C B = k C A = 9.26x10 16 (0.0059) 2 = 3.22x10 12 kmol/m 3 hr 73

14 Based on the enthalpy calculation, the heat flow for reactor is: = = out n i i in n i 9. System Identification In the Aminolysis of Phenol, there are many variables involved. The variables contribute to its process operation and this makes it as a Single-Input Single-Output (SISO) process. These variables is notify as an input or manipulated variables (MV) which have direct effect on the process performance and easy to stimulate.(ahmmed S.Ibrehem, 2011). From the designed model in the Aspen Hysis, the input or manipulated variables (MV) are percent ratio of the temperature input, feed flowrate and feed composition while the controlled variables (CV) is the Phenol production system. The step involves in the system identification are as in below (Ahmmed S.Ibrehem & Hikamt SI, 2009): 1. Actual calculation for the system from optimum condition of the temperature input, xn,i is done to calculate the yn,i. 2. Step (1) is repeated by increasing the decreasing the temperature input according to the specify step change. 3. Each of the element from the first matrix in the step (1) must be subtracted with the correspond element in the step (2) matrix and the difference must be divided with yn,i. 4. The change in the xn,i parameter is calculated by repeating step (3). 5. To produce the sensitivity matrix k that depends completely on scale matrix without using any proper factor, the element in the step (3) and step (4) must be divided. 6. o find the average angle θ that represents the overall effects of the input temperature, the k average must be calculated. 7. Step (1) to (6) must be repeated for the feed flowrate and feed composition variables. From the parameter average analysis, a preliminary partition of estimating into different groups depends on the slope angle θ of k average and these groups are classified as follows (Ahmmed S.Ibrehem, 2011): θ 20 : large effect on the system. 74

20 θ 15 : middle effects on the system. 15 θ 10 : weak effects on the system. 10 < θ: cannot be established. Note: (11) Figure 4 represent the variables in the production of phenol system. Fig. 4: Representation of the Variables in the Aminolysis of Phenol Process 10.

15 20 θ 15 : middle effects on the system. 15 θ 10 : weak effects on the system. 10 < θ: cannot be established. Note: (11) Figure 4 represent the variables in the production of phenol system. Fig. 4: Representation of the Variables in the Aminolysis of Phenol Process 10. Result and Discussion The variables are changing with the same step change for example: the optimum temperature of the feed is C with the step change of 10, the effect of output must be determine by using temperature C and C which is ± of the optimum temperature. Depending on the system identification method, the calculation made can be seen in Table 3,4 and 5. Table 3 shows the percent ratio of temperature input toward the output process performance. Optimum inlet temperature is C which will produce kmol/hr of Aniline at the conversion of.92. he average slope is. 2 and θ average is hat is mean the temperature ratio have the large effect on the system which is about %. Table 4 shows feed composition variables toward the output process performance. The optimum value for the composition of the feed is 0.5 which will gives 92.4% of Aniline produced. From the able, the average slope is. and θ average is 7.. hat is mean the composition ratio have the large effect on the system which is about 52.67%. Table 5 shows the relationship of the feed flowrate effects toward the output process performance. The optimum value for the flowrate of the feed is kmol/hr which will gives kmol/hr of Aniline produced. All the calculation can be seen in Table 4.6. The average slope is.9 and θ average is.2. hat is mean the composition ratio have the large effect on the system which is about 48.05%. 75

16 Table 3: Calculation of the Percent Different for Temperature Ratio toward Output Profile Ratio (xi) (xi-xn,i)/xn,i yi (yi-yn,i)/yn,i Slope (k) k average θ average % effect Table 4 : Calculation of the Percent Different for Composition Ratio towards the Output profile Ratio(xi) (xixn,i)/xn,i yi (yi-yn,i)/yn,i Slope (k) k average θ average % effect

17 Table 5 : Calculation of the Percent Different for Flowrate Ratio towards the Output profile Ratio (xi) (xi-xn,i)/xn,i yi (yi-yn,i)/yn,i Slope (k) k average θ average % effect Therefore from above result the θ average are; Θ 1 = Θ 2 = Θ 3 = By using equation 11, the optimum and can be obtained. (12) (13) 77

18 Therefore; (14) (15) (16) (17) By using equation 13, (18) By using MATLAB, the equation 15 until 18 is being calculated hence getting the result as in Table 6: Table 6: The Result of and by using MATLAB software

19 11. Conclusion By using the active identification, it is easy to analyse the phenol system. The result can also be further used in the industry. From this study, the most active input parameter that give large effect on the output system is the input temperature. However, the feed flowrate also give slightly lower effect on the output system compare to the input temperature. In term of the interaction between the output, the feed composition has the largest effect while the input temperature and feed composition have no effect on the interaction between the output. References Ahmmed S.Ibrehem, (2011). System Identification for Experimental Study for Polymerization Catalyst Reaction in Fluidized Bed. Bulletin of Chemical Reaction Engineering & Catalysis, 6 (2), Ahmmed S.Ibrehem, and Hikamt SI. (2009). New Dynamic Analysis and System Identification of Biodiesel Production Process From Palm Oil. Bulletin of Chemical Reaction Engineering & Catalysis, 4(2), Global Industry Analysts. (2010). Aniline- A global strategic business report, from Hatzantonis H., Goulas, A., and Kiparissides, C. (1998). A Comprehensive Model for the Prediction of Particle-Size Distribution in Catalyzed Olefin Polymerization Fluidized-Bed Reactors. Chemical Engineering Science, 53, ICIS. (2008). Chemical Profile-Aniline. ICIS Chemical Business. Retrieved from ICIS.com website: Kent, J. A. e. (2003). Riegel s andbook of Industrial hemistry (Vol. 10). New York: Kluwer Academic/Plenum Publishers. Lawrence, F. R. (1996). In W. J. Marshall (Ed.), Ullmann's Encyclopedia of Industrial Chemistry (4 ed.). Weinheim: Wiley-VCH. Merriam Webster. (2011). Encyclopedia Britannica. Retrieved from aniline website: Wong E.W., and Birkhoff R. (2005). Dupont/KBR Aniline Process. In M. R. A. ed (Ed.), McGraw-Hill Handbooks (pp ). New York: McGraw-Hill Professional Publishing. 79

Process Design Decisions and Project Economics Prof. Dr. V. S. Moholkar Department of Chemical Engineering Indian Institute of Technology, Guwahati

Process Design Decisions and Project Economics Prof. Dr. V. S. Moholkar Department of Chemical Engineering Indian Institute of Technology, Guwahati Module - 2 Flowsheet Synthesis (Conceptual Design of