1. Mathematical Modeling Overview on Mathematical Modeling in Chemical Engineering By Wiratni, PhD Chemical Engineering Gadjah Mada University Yogyakarta - Indonesia

2. Outline  Description of mathematical models  Strategy in mathematical modeling  Summary

3. Description

4. Mathematical model mathematical expressions that represents some phenomena

5. Why need mathematical models?  Engineering is more easily conducted by means of NUMBERS  Everything needs to be QUANTIFIED  Engineer must be able to TRANSLATE any problems into NUMBERS, by which they make sizing of process equipment

6. Translation into math expression  Verbal statement: The concentration of reactant A is decreasing with increasing time  Mathematical expression: C A = C Ao – k 1 t 3 – k 2 t 2 – k 3 t or ?? or

7. Categories of mathematical models  Empirical model  Deterministic model  Stochastic model

8. Empirical models C A = C Ao – k 1 t 3 – k 2 t 2 – k 3 t  Any mathematical expressions with some adjustable parameters  No particular theoretical background  Example: polynomials

9. Deterministic models  Based on theoretically accepted laws  Example: From kinetics:

10. Chemical Engineering Tools  Mass balance  Energy balance  Rate processes  Equilibrium  Economics  Humanity Basic principles in mathematical model development

11. Stochastic models  Take into account the uncertainty of the phenomenon  Incorporating the concept of probability into deterministic model  Example: in processes involving living organisms

12. Comparison EmpiricalDeterministicStochastic Difficulty <>>>> Accuracy <>>>>>

13. Focus for undergraduate level DETERMINISTIC MODELS

14. Strategy

15. Principles in math modeling  Think simple: separate the incidental from the essentials; focus on the essentials  Back to basic: mass balance, energy balance, rate processes, equilibrium

16. Principles in math modeling ESSENTIAL INCIDENTAL Mass balance Energy balance Rate process Equilibrium FUNDAMENTAL MODEL Refining Fundamental Model Improved Accuracy

17. Example: Batch reactor data Time Concentration of reactant, C A T1T1 T2T2 T3T3 T 1 < T 2 < T 3 What can you infer from the data?

18. Observation  Reactant depletion over time  Different set of data for different temperature  Reactant depletes more quickly at higher temperature Which one essential and which one incidental?

19. Essential vs. incidental  What essential: DEPEND ON YOUR MOST FUNDAMENTAL PURPOSE  With respect to batch reaction data: your final goal is to design a reactor  The size of reactor: depend on how fast the reaction is  the essential of the model is relation between C A and time  temperature is incidental factor

20. Focus on the essential  How to correlate C A and time?  Back to basic: rate process for reaction is governed by reaction kinetics law  In batch reactor (one of the possibilities): -r A =

21. Model’s variables and constants  Variable: Independent Dependent  Constants: Adjustable parameters to fit the data on particular mathematical model

22. Example to find model constants  Experimental data: For T 1 : t C A (-dC A /dt) t 1 C A1 y 1 t 2 C A2 y 2... t n C An y 3

23. Trial and error procedure  Try n=1 -(dC A /dt) C a n (with n=1) Plot of experimental data should be linear if it is true that n = 1 If the plot of experimental data is not linear, try another value of n

24. Determination of k 2  Assume it is correct that n = 1  The value of k 2 is then the slope of the plot -(dC A /dt) C a n (with n=1) Slope = k 2

25. Incorporating the incidentals  From the example, you may get 3 different values of k 2 for three different temperatures: Tk 2 T 1 (k 2 ) 1 T 2 (k 2 ) 2 T 3 (k 2 ) 3

26. Correlating k 2 and T  Back to basic: use Arrhenius correlation: k = A exp (- E/R/T)

27. Correlating k 2 and T Plot of ln k 2 vs. (1/T)

28. Correlating k 2 and T ln k 2 1/T Slope = -E/R Intercept = ln A

29. Complete model  Combining the essential and incidental, you get kinetic model:  You can predict the concentration of remaining A in the reactor at any time and temperature!

30. Utilizing kinetics model in CSTR design  Back to basic: use mass balance (assuming isothermal reactor) F vin C Ain F vout C Aout Rate of mass in + Rate of mass formed by reaction - Rate of mass reacted - Rate of mass out = Rate of accumulation

31. Steady-state CSTR modeling Rate of mass in + Rate of mass formed by reaction - Rate of mass reacted - Rate of mass out = Rate of accumulation F vin.C Ain + 0- (-r A ).V - F vout.C A =0 USEFUL FOR SCALE-UP

32. Other tools for scale up  Correlations among dimensionless groups  Empirical, but ok as long as you pick the correct dimensionless groups

33. Example: Scale-up mixing Power number Reynold number

34. Summary

35. QUANTITATIVE APPROACH IN SCALE-UP (Mathematical Modeling) Lab scale Simulation Math modeling Pilot plant scale Commercial scale Engineering design Model improvement

36. Important tools for scale-up  Reliable correlations of dimensionless groups  Reliable mathematical models  Numerical methods to solve the models

37. Required fundamentals  Mass balance  Energy balance  Rate processes Physical: momentum transfer, mass transfer, heat transfer Chemical: reaction rate  Equilibrium: Phase equilibrium Chemical equilibrium

38. Accuracy  Highly accurate models: time/energy consuming, costly  Moderately accurate models: quick, low cost  For engineering purpose: does not need 100% (absolute) correct answers  we can do with ‘careful estimation based on theoretical supports’

39. Have a nice journey to be chemical engineers!