1. Introduction
Process Simulation

2. Classification of the models
Black box – white box
Black box – know nothing about process in apparatus, only dependences between inputs and outputs are established. Practical realisation of Black box is the neural network
White box – process mechanism is well <??> known and described by system of equations

3. Classification of the models
Deterministic – Stochastic
Deterministic – for one given set of inputs only one set of outputs is calculated with probability equal 1.
Stochastic – random phenomenon affects on process course (e.g. weather), output set is given as distribution of random variables

4. Classification of the models
Microscopic- macroscopic
Microscopic – includes part of process or apparatus
Macroscopic – includes whole process or apparatus

5. Elements of the model Balance dependences Based upon basic nature laws
of conservation of mass
of conservation of energy
of conservation of atoms number
of conservation of electric charge, etc.
Balance equation (for mass): (overall and for specific component without reaction) Input – Output = Accumulation or (for specific component if chemical reactions presents) Input – Output +Source = Accumulation

6. Elements of the model Constitutive equations
Newton eq. – for viscous friction
Fourier eq. – for heat conduction
Fick eq. – for mass diffusion

7. Elements of the model
Phase equilibrium equations – important for mass transfer
Physical properties equations – for calculation parameters as functions of temperature, pressure and concentrations.
Geometrical dependences – involve influence of apparatus geometry on transfer coefficients – convectional streams.

8. Structure of the simulation model
Structure corresponds to type of model equations
Structure depends on:
Type of object work:
Continuous, steady running
Periodic, unsteady running
Distribution of parameters in space
Equal in every point of apparatus – aggregated parameters (butch reactor with ideal mixing)
Parameters are space dependent– displaced parameters

9. Structure of the model Steady state Unsteady state
Aggregated parameters
Algebraic eq.
Ordinary differential eq.
Displaced parameters
Differential eq.
Ordinary for 1- dimensional case
Partial for 2&3- dimensional case (without time derivative, usually elliptic)
Partial differential eq.
(with time derivative, usually parabolic)

10. Process simulation
the act of representing some aspects of the industry process (in the real world) by numbers or symbols (in the virtual world) which may be manipulated to facilitate their study.
Facilitate - ułatwiać

11. Process simulation (steady state)
Flowsheeting problem
Specification (design) problem
Optimization problem
Synthesis problem
by Rafiqul Gani

12. Flowsheeting problem Given: To calculate: All of the input information
All of the operating condition
All of the equipment parameters
To calculate:
All of the outputs
FLOWSHEET
SCHEME
INPUT
OPERATING
CONDITIONS
EQUIPMENT
PARAMETERS
PRODUCTS

13. R.Gani

14. Specyfication problem
FLOWSHEET
SCHEME
INPUT
OPERATING
CONDITIONS
EQUIPMENT
PARAMETERS
PRODUCTS
Given:
Some input & some output information
Some operating condition
Some equipment parameters
To calculate:
Undefined inputs&outputs
Undefined operating condition
Undefined equipment parameters

15. Specyfication problem
NOTE: degree of freedom is the same as in flowsheeting problem.

17. Assume value to be guessed: D, Qr
Given: feed composition and flowrates,
target product composition
Assume value to be guessed:
D, Qr
Find: product flowrates, heating duties
Solve the flowsheeting
problem
Adjust D, Qr
Is target product
composition satisfied ?
STOP

18. Process optimisation
the act of finding the best solution (minimize capital costs, energy... maximize yield) to manage the process (by changing some parameters, not apparatus)

20. Assume value to be guessed: D, Qr
Given: feed composition and flowrates,
target product composition
Assume value to be guessed:
D, Qr
Find: product flowrate, heating duty
Solve the flowsheeting
problem
Adjust D, Qr
Is target product
composition satisfied
AND =min.
STOP

21. Process synthesis/design problem
the act of creation of a new process.
Given:
inputs (some feeding streams can be added/changed latter)
Outputs (some byproducts may be unknown)
To find:
Flowsheet (topology)
equipment parameters
operations conditions

22. Process synthesis/design problem
flowsheet
undefined
INPUT
OUTPUT

24. Assume value to be guessed: D, Qr, N, NF, R/D etc.
Given: feed composition and flowrates,
target product composition
Assume value to be guessed:
D, Qr, N, NF, R/D etc.
Find: product flowrate, heating duty,
column param. etc.
Solve the flowsheeting
problem
Adjust D, Qr
As well as
N, NF, R/D etc.
Is target product
composition satisfied
AND =min.
STOP

25. Process simulation - why?
COSTS
Material – easy to measure
Time – could be estimated
Risc – hard to measure and estimate

26. Modelling objects in chemical and process engineering
Unit operation
Process build-up on a few unit operations

27. Software for process simulation
Universal software:
Worksheets – Excel, Calc (Open Office)
Mathematical software – MathCAD, Matlab
Specialized software – process simulators. Equipped with:
Data base of apparatus models
Data base of components and mixtures properties
Solver engine
User friendly interface

28. Software process simulators (flawsheeting programs)
Started in early 70’
At the beginning dedicated to special processes
Progress toward universality
Some actual process simulators:
ASPEN Tech /HYSYS
ChemCAD
PRO/II
ProSim
Design II for Windows

29. Chemical plant system
The apparatus set connected with material and energy streams.
Most contemporary systems are complex, i.e. consists of many apparatus and streams.
Simulations can be use during:
Investigation works – new technology
Project step – new plants (technology exists),
Runtime problem identification/solving – existing systems (technology and plant exists)
test

30. Chemical plant system
characteristic parameters can be specified for every system separately according to:
Material streams
Apparatus

31. Apparatus-streams separation
Assumption:
All processes (chemical reaction, heat exchange etc.) taking places in the apparatus and streams are in the chemical and thermodynamical equilibrium state.
Why separate?
It’s make calculations easier

32. Streams parameters Flow rate (mass, volume, mol per time unit)
Composition (mass, volume, molar fraction)
Temperature
Pressure
Vapor fraction
Enthalpy

33. Streams degrees of freedom
DFs=NC+2
e.g.: NC=2 -> DFs=4
Assumed: F1, F2, T, P
Calculated:
enthalpy
vapor fraction

34. Apparatus parameters & DF
Characteristics for each apparatus type. E.g. heat exchanger :
Heat exchange area, A [m2]
Overall heat-transfer coefficient, U (k) [Wm-2K-1]
Log Mean Temperature Difference, LMTD [K]
degrees of freedom are unique to equipment type

35. Types of flowsheeting calculation
Steady state calculation
Dynamic calculation

36. Calculation subject
Number of equations of mass and energy balance for entire system
Can be solved in two ways:

38. Types of balance calculation
Overall balance (without use of apparatus mathematical model)
Detailed balance on the base of apparatus model

39. Overall balance Apparatus is considered as a black box
Needs more stream data
User could not be informed about if the process is physically possible to realize.

40. Overall balance – Example1 2 4 3
Countercurrent, tube-shell heat exchanger
Given three streams data: 1, 2, 3 hence parameters of stream 4 can be easily calculated from the balance equation.
DF=5
There is possibility that calculated temp. of stream 4 can be
higher then inlet temp. of heating medium (stream 1).

41. Overall balance – Example
1, mB 2 4
3, mA
Given:
mA=10kg/s
mB=20kg/s
t1= 70°C
t2=40°C
t3=20°C
cpA=cpB=idem
At first sight – na pierwszy rzut oka

42. Apparatus model involved
Process is being described with use of modeling equations (differential, dimensionless etc.)
Only physically acceptable processes taking place
Less stream data required (smaller DF number)
Heat exchange example: given data for two streams, the others can be calculated from a balance and heat exchange model equations

43. Loops and cut streams Loops occur when: To solve:
some products are returned and mixed with input streams
when output stream heating (cooling) inputs
some input (also internal) data are undefined
To solve:
one stream inside the loop has to be cut (tear stream)
initial parameters of cut stream have to be defined
Calculations have to be repeated until cut streams parameters are converted.

44. Loops and cut streams

45. Simulation of system with heat exchanger using MathCAD

46. I.Problem definition
Simulate system consists of: Shell-tube heat exchanger, four pipes and two valves on output pipes. Parameters of input streams are given as well as pipes, heat exchanger geometry and valves resistance coefficients. Component 1 and 2 are water. Pipe flow is adiabatic.
Find such a valves resistance to satisfy condition: both streams output pressures equal 1bar.

47. II. Flawsheet s6 s1 1 2 3 4 6 7 5 s2 s3 s4 s5 s7 s8 s9
s10

48. Numerical data: Stream s1 Ps1 =200kPa, ts1 = 85°C, f1s1 = 10000kg/h

49. Equipment parameters:
L1=7m d1=0,025m
L2=5m d2=0,16m, s=0,0016m, n=31...
L3=6m, d3=0,05m
z4=50
L5=7m d5=0,05m
L6=10m, d6=0,05m
z7=40

50. III. Stream summary table
Uknown:Ts2, Ts3, Ts4, Ts5, Ts7, Ts8, Ts9, Ts10, Ps2, Ps3, Ps4, Ps5, Ps7, Ps8, Ps9, Ps10, f1s2, f1s3, f1s4, f1s5, f2s7, f2s8, f2s9, f2s10
number of unknown variables: 26
WE NEED 26 INDEPENDENT EQUATIONS.

51. Equations from equipment information
f1s2= f1s1 f1s7= f1s6
f1s3= f1s2 f1s8= f1s7
f1s4= f1s3 f1s9= f1s8
f1s5= f1s4 f1s10= f1s9
14 equations. Still do define 26-14=12 equations

52. Heat balance equations
New variable: Q
Still to define: =11 equations

53. Heat exchange equations
New variables: k, DTm: number of equations to find =11

54. Heat exchange equations
Two new variables: aT and aS
number of equations to find: =12

55. Heat exchange equations
Three new variables: NuT, NuS, deq,
number of equations to find: =12

56. Heat exchange equations

57. Heat exchange equations
Two new variables ReT and ReS,
number of equations to find: =10

58. Pressure drop

59. Pressure drop Two new variables Re1 and l1,
number of equations to find: =9

60. Pressure drop One new variables and l2T,
number of equations to find: 9+1-3=7

61. Pressure drop Two new variables Re3 and l3,
number of equations to find: 7+2-3=6

62. Pressure drop
Number of equations to find: 6-1=5

63. Pressure drop Two new variables Re5 and l5,
number of equations to find: 6+2-3=4

64. Pressure drop One new variables and l2S,
number of equations to find: 4+1-3=2

65. Pressure drop Two new variables Re6 and l6,
number of equations to find: 2+2-3=1

66. Pressure drop
Number of equations to find: 1-1=0 !!!!!!!!!!!!!!

67. Agents parameters Temperatures are not constant
Liquid properties are functions of temperature
Density 
Viscosity 
Thermal conductivity 
Specyfic heat cp
Prandtl number Pr

68. Agents parameters
Data are usually published in the tables

69. Agents parameters Data in tables are difficult to use Solution:
Approximate discrete data by the continuous functions.

70. Approximation Approximating function
Polynomial
Approximation target: find optimal parameters of approximating function
Approximation type
Mean-square – sum of square of differences between discrete (from tables) and calculated values is minimum.

71. Polynomial approximation

72. The end as of yet.