lecture 1 - intro to process simulation

Welcome to H82CYS Computer

Systems

H82CYS - Computer System Intro to Process Simulation 2

About this module Instructor:

Computer flowsheeting (with Aspen HYSYS):Dr Dominic FOO (CA21; x-8130)Dr Chung Lim LAW (CA24; x-8169)Dr Denny NG (CA33; ext-8606)

Process modelling (with Mathlab): Dr Suyin GAN (CA22; x-8162)

Summary: Introduction to computational techniques &

computing. To gain experience in computer programming,

engineering databases and steady-state & dynamic process simulation.

Assessment: HYSYS coursework – 30%; HYSYS test – 20%Matlab coursework – 20%; Simulink coursework –

10%; Matlab test – 20%


Weekly programmeWee

kContent

1 Introduction to computer-aided process simulation

2 Coursework 13 Modeling for reaction & separation processes4 Simulation of recycle streams5 Other important topics; Coursework 26 In class test on HYSYS7 Introduction to Matlab; mathematical

operations, matrices, types of files8 Programming; m-files, program flow control,

script and function files9 Graphics – plotting and editing graphs;

coursework10 Debugging, function files11 Simulink; coursework12 In-class test on Matlab

Introduction to Introduction to process process

flowsheetingflowsheeting


Learning outcomesAfter completing the part on process

flowsheeting, you will be able to do the following:1. Able to carry out computer-aided process

flowsheeting2. Able to perform steady-state process

simulation using a commercial software (e.g. Aspen HYSYS)

3. Able to simulate an integrated flowsheet & converge a recycle loop

4. Able to analyse and verify simulation results


References (process flowsheeting) Westerberg, A. W., Hutchison, H. P., Motard, R. L., and

Winter, P. (1979). Process Flowsheeting. Cambridge University Press, Cambridge (Chapter 2).

Turton, R., Bailie, R. C. Whiting, W. B., Shaeiwitz, J. A. (1998). Analysis, Synthesis and Design of Chemical Processes. Prentice Hall, New Jersey (Chapter 18; Chapter 11 in 2nd Edition).

Dimian, A. C. (2003). Integrated Design & Simulation of Chemical Processes. Elsevier Science (Chapters 2 & 3).

Foo, D. C. Y., Manan, Z. A., Selvan, M., and McGuire, M. L. (2005). Process Synthesis by Onion Model and Process Simulation, Chemical Engineering Progress. 101(10): 25-29.

Seider, W. D., Seader, J. D. and Lewin, D. R. (2003). Product and Process Design Principles: Synthesis, Analysis, and Evaluation. John Wiley, New York (and the CDROM).


A few important jargonsSimulation – a process of designing an

operational model of a system & conducting experiments with this model for the purpose either of understanding the behaviour of the system or of evaluating alternative strategies for the development or operation of the system… (Thomé, 1993).

Process flowsheeting – the use of computer aids to perform steady-state heat and mass balancing, sizing and costing calculation for a chemical process (Westerberg et al., 1979).


A few important jargonsSimulation implies modelling, as well as tuning

of models on experimental data. A simulation model serves to conduct “virtual experiments”.

“A model should be as simple as possible and no simpler”

~ Albert Einstein


A few important jargons

Simulation flowsheet/ process simulation

diagramProduct

Dis

tilla

tion

Flash

ReactorBoiler

Fresh feed

Steam

Light ends

S1

S2

S3

S4

S9

S8S6

S7

S5

Process flowsheet/ process flow

diagram

?


A historical perspective1966 – 1st commercial process simulator

started in Los Angeles by Simulation Science for simulating distillation column developed into PROCESS PROII.

1969 – ChemShare in Houston released DESIGN for oil & gas application continue as DESIGN II for Windows (by WinSim).

1976 – The famous ASPEN project launched jointly by the US Dept. of Energy and MIT.

1979 – 1st important process flowsheeting textbook was published (Westerberg et al., 1979)


Commercial design & simulation softwares

(Dimian, 2003)

Bought over by ASPEN

SuperPro Designer


Malaysia context

Oil, Gas & Petrochemicals

Specialty Chemicals

Oleo Chemicals

SS Simulation

Optimisation

Sensitivity Analysis

Dynamic simulation

Rating

Physical properties

Batch operation

Pinch analysis

Economics

Sizing & costing

Solid handlingHYSYS

Aspen Plus

PRO/II

SuperPro Designer

Design II for Windows


Aspen HYSYS (our main focus)


UniSim Design (the same as HYSYS)


Aspen Plus


DESIGN II for Windows

Latest version & password



UNMC subscribes to the academic

license of DESIGN II


SuperPro Designer (H8CPS1)


But that’s a lot to learn from all

these softwares!

The good news is, once you learn one of them, you learn 70% of the

rest!


Applications of process simulationProcess Industries ApplicationsOil & Gas

RefiningPetrochemicalsBasic Organic ChemicalsInorganic ChemicalsFine ChemicalsBiotechnologyMetallurgyPolymersPaper & WoodEnergyNuclear industryEnvironment

Offshore exploration, Surface treatment, Pipeline transport, Underground storage, Gas processingGasoline and fuelsHydrocarbon based chemicals, Methanol, MonomersIntermediates, Solvents, Detergents, DyesAmmonia, Sulphuric Acid, FertilisersPharmaceuticals, CosmeticsFood and bio productsSteel, Aluminium, Copper, etc.Polyethylene, PVC, Polystyrene, fibres, etc.Paper pulpPower plants, Coal gasificationWaste treatment, SafetyWater cleaning, Biomass valorisation

(Dimian, 2003)


Applications of process simulation

(Dimian, 2003)

Process Process flowsheeting flowsheeting

with with SpreadsheetSpreadsheet


Degree of freedom revisitedDegree of freedom (ndf) of a system:

ndf = nv – newhere, nv = variables; ne = independent eq

If ndf = 0 (e.g. 3 unknowns & 3 independent eq), the unknown variables can be calculated.

If ndf > 0 (e.g. 5 unknowns & 3 independent eq ndf = 2), specify the design variables & calculate the state variables.

If ndf < 0 (independent eq > unknowns) process is over-specified.

(Felder & Rousseau, 2000)


Degree of freedom revisitedUnknown variables for a single unit:

Unknown component amounts / flowrate for all inlet & outlet streams

Unknown stream T & PUnknown rate of energy transfer (as heat & power)

Equation to determine these unknowns:Material balances for each independent speciesEnergy balancePhase & chemical equilibrium relationsAdditional specified relationship among process

variables


Example : A heated mixer

Heated mixern2 (kg O2)n3 (kg N2)

25ºC

n4 (kg O2)n5 (kg N2)

50ºC

n1 (kg O2)40ºC

Q (kJ)ndf analysis:

6 variables (n1, …, n5, Q)– 3 eq (2 material balances & 1 energy balances) = 3 degrees of freedom

Specify 3 design variables & solve the rest.


Degree of freedom revisited Given the following equations:

x1 + 2x2 – x32 = 0

5x1 – x23 + 4 = 0

i. What is the ndf for this system?ii. Which design variable to be chosen for an easier

solution? Given the following equations:

5x – 3y = 710x – 3y – 6z = 14

y = 2zi. Why can’t you solve this equation?ii. Choose a design variable, specify it &

determine the rest of the state variables.


Tutorial 1: Mass & energy balances

Determine the ndf for the systemWhat are the design variables & state variables?

Mixing

nA3 (mol A/s)nB3 (mol B/s)nC3 (mol C/s)nD3 (mol D/s)nE3 (mol E/s)

T3 (ºC)


T2 (ºC)


T1 (ºC)

S1

S2

S3



Tutorial 1: Mass & energy balances Mass balance equations:

nA3 = nA1 + nA2

nB3 = nB1 + nB2

nC3 = nC1 + nC2

nD3 = nD1 + nD2

nE3 = nE1 + nE2 Energy balance equation:

H = noutHout – ninHin (assumption: P = 1 atm; temp = T1 H1 = 0; no phase change; constant Cp)

ndf = 18 variables (6 on each streams) – 6 equations= 12 degrees of freedom


Tutorial 1: Mass & energy balances Specify the design variables:Stream 1:

nA1 = 23.5 mol A/snB1 = 16.2 mol B/snC1 = 8.5 mol C/snD1 = 5.6 mol D/snE1 = 2.2 mol E/sT1 = 135.0ºC

Stream 2:nA2 = 0.0 mol A/snB2 = 57.0 mol B/snC2 = 29.0 mol C/snD2 = 15.6 mol D/snE2 = 0.0 mol E/sT2 = 23.0º Other info [constant heat capacity in J/(mol.ºC)]: CpA

= 77.3; CpB = 135.0; CpC = 159.1; CpD = 173.2; CpE = 188.7

Determine the component flowrate & T for stream 3.


Tutorial 1: Mass & energy balancesEnergy balance equation (cont.):

H = noutHout – ninHin = 0 0 = [ nA3CpA + nB3CpB + … + nE3CpE ] (T3 – T1)

– [ nA2CpA + nB2CpB + … + nE2CpE ] (T2 – T1) – [ nA1CpA + nB1CpB + … + nE1CpE ] (T1 – T1)

(reference temperature taken as T1)Rearrange the equation, solving for T3:

1233333

2222213 TT

CnCnCnCnCnCnCnCnCnCn

TTpEEpDDpCCpBBpAA

pEEpDDpCCpBBpAA

= 0



Tutorial 1 in spreadsheet solution

Time for Time for exercise…exercise…

Approaches for Approaches for process process

flowsheetingflowsheeting• Sequential-modular• Equation-oriented


Sequential-modular (SM) approach The computation takes place unit-by-unit following

a calculation sequence. Dominate steady-state simulation softwares Main advantages:

Modular development of capabilities. Easy programming and maintenance. Easy control of convergence, both at the units and

flowsheet level. Disadvantages:

Need for topological analysis and systematic initialisation of tear streams.

Difficulty to treat more complex computation sequences, as nested loops or simultaneous flowsheet & design specification loops.

Difficulty to treat specifications regarding internal unit (block) variables.

(Dimian, 2003)


Sequential modular approach

Individual equipment blocks may require iterative solution algorithms

Overall process solution is sequential & not iterative

(Turton et al., 1998)


Sequential modular approach

Option 1: Mixer Reactor TowerOption 2: Mixer + Reactor TowerOption 3: Mixer + Reactor + Tower


Tutorial 2 (continue from Tutorial 1)

MixingnA3 (mol A/s)nB3 (mol B/s)nC3 (mol C/s)nD3 (mol D/s)nE3 (mol E/s)

T3 (ºC)


T2 (ºC)


T1 (ºC)nA4 (mol A/s)nB4 (mol B/s)nC4 (mol C/s)nD4 (mol D/s)nE4 (mol E/s)

T4 = ?

S1

S2

S3 S4

Heater, Q = 100,000 J


Equation-oriented (EO) Solution is obtained by solving simultaneously

all the modelling equations. Advantages:

Flexible environment for specifications, which may be inputs, outputs, or internal unit (block) variables.

Better treatment of recycles, and no need for tear streams.

Note that an object oriented modelling approach is well suited for the EO architecture.

Disadvantages:More programming effort.Need of substantial computing resources (but this is

less and less a problem with new PCs).Difficulties in handling large differential algebraic

equations systems.Difficult convergence follow-up and debugging.


Equation solving by matrixSolve matrix equation: A X = B

where, A = a known (i x i) coefficient matrix; B = a know solution vector (i x 1); X = an unknown vector (i x 1)

Example matrix with i = 3:A (3x3) X (3x1) = B (3x1) X = A-1 B x

yz

a1 b1 c1a2 b2 c2a3 b3 c3

=d1d2d3

xyz =

d1d2d3

-1a1 b1 c1a2 b2 c2a3 b3 c3


Tutorial 3Solve for the following simultaneous

equations: x + y + z = 1

2x - 2y + 5z = 12.5 y + z = 1

Set up matrix equation: A X = B

xyz

1 1 12 -2 50 2.5 1 =

111


Matrix solving by Excel spreadsheet


Tutorial 4: BTX separation problem

C1 C235 kg B50 kg T15 kg X

n1 (kg)0.673 kg B/kg0.306 kg T/kg0.021 kg X/kg

n2 (kg B)n3 (kg T)n4 (kg X)


n6 (kg B)n7 (kg T): 10% of T in feed to C1n8 (kg X): 90% of X in feed to C1

C1: 4 variables (n1, …, n5)– 3 material balances = 1 local ndf

C2: 7 variables (n1, …, n5)– 3 material balances = 4 local ndf

Process: 5 local ndf– 3 ties (n2, n3 , n4) – 2 relations (recovery of T & X in C2 bottoms) = 0 degrees of freedom


Tutorial 4: BTX separation problem

C1 C235 kg B50 kg T15 kg X


n2 (kg B)n3 (kg T)n4 (kg X)


n6 (kg B)n7 (kg T): 10% of T in feed to C1n8 (kg X): 90% of X in feed to C1

C1 balances:B: 35 = 0.673n1 + n2T: 50 = 0.306n1 + n3X: 15 = 0.021n1 + n4

C2 balances:B: n2 = 0.059n5 + n6T: n3 = 0.926n5 + n7X: n4 = 0.015n5 + n810% T recovery: n7 = 0.1 (50) = 5.090% X recovery: n8 = 0.9 (15) = 13.5


Tutorial 4: BTX separation problemSolve the mass balance equation using

MS Excel spreadsheet:0.673n1 + n2 = 35 (1)0.306n1 + n3 = 50 (2)0.021n1 + n4 = 15 (3)0.059n5 + n6 – n2 = 0 (4)0.926n5 + n7 – n3 = 0 (5)0.015n5 + n8 – n4 = 0 (6)n7 = 5.0 (7)n8 = 13.5 (8)

lecture 1 - intro to process simulation

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