Episode 47 : CONCEPTUAL DESIGN OF CHEMICAL PROCESSES

SAJJAD KHUDHUR ABBAS
Ceo , Founder & Head of SHacademy
Chemical Engineering , Al-Muthanna University, Iraq
Oil & Gas Safety and Health Professional – OSHACADEMY
Trainer of Trainers (TOT) - Canadian Center of Human
Development
Episode 47 : CONCEPTUAL DESIGN
OF CHEMICAL PROCESSES

INTRODUCTION
 Chemical process design is the application of chemical
engineering knowledge (chemical, physical and/or biological
transformations of raw materials) into products and economics in
the conceiving a chemical process plant to profitably
manufacture chemicals in a reliable and safe manner without
unduly affecting adversely the environment and society
 Chemical process plants are by nature large capital investment
projects that
 are expensive to build and operate
 have very long life times and
 manufacture specific chemicals
 Chemical process plants must be designed well to avoid large
financial losses over long periods of times due to inefficient
processes/poor operations

INTRODUCTION
 Main design objectives of chemical processes:
 design of a grassroot plant or
 a retrofit design for existing chemical plants
 Complimentary objectives
 profitable, safe, reliable, flexible, controllable
and operable
 Not all of these objectives can be fulfilled
however and some trade offs must be made in
order to produce a practical design

UNIT OPERATIONS
 In the past, chemical process plants are
designed using unit operations first
proposed by G.E. Davis in 1887
 Unit operations was formalised by A.D.
Little in 1915 as the defining principle of
chemical engineering
 The concept was earlier proposed by the
ancient alchemists, in the course of
transforming and purifying their chemicals
through a series of operations of heating,
distillation, evaporation etc.

UNIT OPERATIONS
 New chemical process plants were then
designed by
 arranging the unit operations in the same
sequence as the original laboratory
methods
 increasing the size of equipment linearly
for greater capacity
 In the 40’s, it was realised that scaling-up
is not linear and pilot plant studies
needed to be done in order to determine
the correct scaling-up parameters

UNIT OPERATIONS
 Up to the late 70’s, chemical process design was
still done by
 arranging unit operations in the sequence
proposed by the industrial chemists using block
diagrams and later PFDs
 performing the mass and energy balance
 sizing the individual equipment
 determining the economic viability of the plant
 Alternative PFDs were not easily generated due to
 the empirical nature of the chemical technology
 the large number of uncertain variables to be
determined all at once

UNIT OPERATIONS
 Design parameters were determined in ad
hoc manner & specific for particular process
 No systematic method for generating
alternative PFDs and optimising them
 Short cut methods of designing heat and
mass transfer equipment already available
 Equipment costing methods have been fairly
developed using costing charts
 Possible integration and optimisation of unit
operations due to interconnections within the
chemical process system was not
understood

PROCESS SIMULATION
 With powerful computers and better
understanding of thermodynamics in the late
60’s to early 80’s, computational and
optimisation methods were used in process
system engineering
 Since the 60’s, primitive process simulation
softwares were owned by large petrochemical
companies
 These were mainly the sequential modular
type where the unit operation modules were
solved one by one in the direction of mass
flow

PROCESS SIMULATION
 Modular simulation consists of
 a top level of flowsheet topology where unit module are
sequenced, recycle and tear streams determined, and
convergence made,
 a middle level where the unit operations are modeled and
solved and
 a lower level where physical and thermodynamic models are
solved
 By the late 70’s, the solution of modular flowsheets
was significantly improved leading to simultaneous
modular flowsheets which are the basis of commercial
process simulation softwares such as
 ASPEN/PLUS from Aspen Technology Inc. and
 HYSYS from Hyprotech Ltd

 Most process simulations use phase equilibrium
thermodynamic models including non-idealities in
both liquid and gas phases for their unit operation
models
 Popular
models
activity
models used are the equation of state
for hydrocarbon mixtures and liquid
coefficients models for non-electrolyte,
non-ideal solutions
 Group contribution models such as UNIFAC are
becoming popular when no empirical vapour-liquid
equilibrium data is available
 Rate-based models are very well developed and
may well become more important when tray
efficiency could not account for non-ideal
PROCESS SIMULATION

PROCESS SIMULATION
 In the 90’s, stoichiometric and equilibrium
reactor
handling
models are primitive with poor
of multiple reactions in
completely mixed and plug flow reactors
 Incorporation of rigorous generic models
for multi-phase industrial reactors is still a
long way off
 Some process simulator companies do
model these reactors for individual process
licence owners

PROCESS SIMULATION
 The generic modeling of adsorption, membrane
and solid drying processes are not well developed
enough to be included in process simulations
 A shortcut method for the generic design of
adsorption columns presented by Wan Ramli Wan
Daud 2000b shows some promise
 Solids handling was neglected in process
simulation work
 It is now more important due to the increased
popularity of fluidised bed reactors and pneumatic
conveying

PROCESS SIMULATION
 In the 80’s and 90’s significant improvement was
made in the equation-oriented process simulation
where the equations for all unit operations are
combined and solved simultaneously
 Allows specifications of certain design parameters
without having to solve another iterative loop
 The computational effort is reduced by the
exploitation of sparse matrices
 Succesful solution requires careful initialisation
based on users’ past experience
 It is used in quick on-line real time modelling and
optimisation where models are simpler and initial
points are taken from previous solutions

PROCESS SIMULATION
 Both simulations require simultaneous solution of
large sets of non-linear equations which are mainly
based on Newton or quasi-Newton or Broyden
methods due to their good convergence properties
 Rapid solution of very large flowsheets can be
achieved by a suitable decomposition strategy
 by recycle tearing streams for the modular simulation
 by utilising powerful sparse matrix solvers for equation
oriented simulation
 Although process simulation is a powerful tool, it
is not possible to produce optimised design by
simply using it because the optimum configuration
and operating principle of the process plant could
only be produced by process synthesis

PROCESS SYNTHESIS
 Contemporary process design method is an
iterative problem solving and optimisation method
using both heuristic and algorithmic methods
 Design method begins with the determination of
the design requirements and objectives which are
promulgated in either an economic or utlitarian
way
 A conceptual design is then produced through the
synthesis of several feasible alternative designs
and the rapid selection of the most viable of these
alternatives based on an economic performance
criterion without using rigorous performance
models of their operational principles

PROCESS SYNTHESIS
 During synthesis, design variables or parameters
are selected or determined and optimised through
 Heuristics, intuitions and experience or
 algorithmic methods using shortcut performance models
of the equipment or chemical process
 Complex design problems are decomposed into
their constituent parts where
 each part is further synthesised,
 its performance is modelled on its operational principle
and
 its design variables or parameters are determined in a
similar manner
 while maintaining integral relationship with other
parts as well as with the overall design

PROCESS SYNTHESIS
 First approach: Process synthesis can be solved by
mathematical modelling alone based on the
principles of process flowsheeting
 Assumes
linearly
that technology emerges from
which
science
scientificis not true because
the physical phenomena in anknowledge on
engineering artefact does not lead to knowledge on
the operating principles and design of the artefact
 The chemical process plant has a large number of
variables that are defined by a smaller number of
equations, with some inexplicable to deterministic
models and most highly non-linear
 Able to synthesise small plants where variables are
defined adequately by equal number of equations
unless efficient decomposition procedures are used

PROCESS SYNTHESIS
 Second approach: Process synthesis could be
solved by expert knowledge obtained from
experience, intuition/insight and inspirations
 Expressed as heuristic rules/rules of thumbs
which set unknown parameters rapidly
 Some heuristics relate external performance
parameters with the operating variables of the
artefact simply and directly without complex
non-linear mathematical modelling

PROCESS SYNTHESIS
 The synthesis problem is
decisions for generating
decomposed into an
and
the
heirarchy of
exploring process alternatives starting from
top down and considering a few design variables
at a time like peeling an onion
 Basic assumption : design parameters at the top
level also reflect design parameters further down
 Old alchemical maxim of the relationship between
the macrocosmos and the microcosmos: “what is
above so below”

HEIRACHICAL PROCESS SYNTHESIS
First & Second Levels
 In Douglas version, after decomposition by
removing all the heat exchangers, the first level
involves use of heuristics to select
 Process Mode:
 Design Variables: Batch or continuous
 The second level involves construction of the
input-output structure of the process and targeting
the production rate by using heuristics:
 Whether the feed should be pretreated
 Destination of products
 Design variables : conversion of limiting reactant and
allowable purge concentration of excess reactant
 Economic potential of process:
Products sales less raw materials’ cost

First & Second Levels

Input-Output Structure

Input-Output Structure: Destination StreamsToluene Hydro-Dealkylation Process
H , CH
2 4
Toluene
Purge H , CH
2 4
Toluene Hydro-
Dealkylation
Process
Benzene
Diphenyl
Component Normal Boiling
Point(C)
Light/Heavy Destination
Hydrogen -253 Light Recycle dan Purge
Methane -161 Light Recycle dan Purge
Benzene 80 Heavy Main Product
Toluene 111 Heavy Recycle
Diphenyl 253 Heavy Fuel

Input-Output Structure: Material BalanceToluene Hydro-Dealkylation Process
FG
FT
1 S 
n  B
PB
2SB
2
PB 1 SB  1 SB PB
2SB
PB  FE 
SB 2SB
FH  yFH FG  FE  
PM  1 yPH PG  1 yFH FG  PB SB
 F  P SBBFH GPG  FE  1 y
PH E Gy  F P
1 SB PB
2SB
PG  FG 
1 1 y 1 S  2
 PH B
y  y S
PB
PH BFH
GF
PB  n1  2n2
FT  n1
FH  FE  n1  n2
PD  n2
FT  PB SB
PM  FM  n1
n1  PB SB
RG PG
Toluene Hydro-
Dealkylation
Process
PB
PD

Input-Output Structure: Material BalanceToluene Hydro-Dealkylation Process
FG
FT
PB = 265 kgmole h-1 benzene
RG PG
Toluene Hydro-
Dealkylation
Process
PB
PD
XB FG
(kgmole h-1)
FT
(kgmole h-1)
PH
(kgmole h-1)
PM
(kgmole h-1)
PD
(kgmole h-1)
0.1 312.49 266.13 31.31 281.75 0.56
0.2 312.64 266.35 31.33 281.99 0.68
0.3 312.84 266.67 31.37 282.31 0.83
0.4 313.13 267.12 31.42 282.77 1.06
0.5 313.58 267.81 31.50 283.49 1.41
0.6 314.34 268.99 31.63 284.70 1.99
0.7 315.82 271.27 31.90 287.06 3.13
0.8 319.51 276.97 32.55 292.94 5.98
0.9 336.48 303.20 35.56 320.02 19.10

Second Level Economic Potential
-5000000
Conversion of Limiting Reactant
5000000
0
10000000
15000000
20000000
25000000
30000000
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Penukaran Toluena
h
n)t
/
M
R(i
o
n
o
m
k
E
i
n
s
e
ot
P
yph=0.1
yph=0.5
yph=0.65
yph=0.786
EconomicPotential(RM)/Year Excess Reactant
Concentration in
Purge Stream
Toluene Hydro-Dealkylation Process
 CB PB  CFD PD  CFPPG CT FT CH FG
fPE2

Input-Output Structure: Destination Streams
BenzeneAlkylation
Process
Benzene Recycle
Propane and Propylene
As Fuel
Cumene
P-diisopropyl Benzene
As Fuel
Propylene
Benzene
Benzene Alkylation Process
Point (C)
C3H8 -42.1 Light Fuel
C6H6 80.1 Heavy Recycle
C9H12 152.4 Heavy Main Product
C12H18 210.3 Heavy Fuel

Input-Output Structure: Material Balance
Benzene Alkylation
Process
PC
FG
FB
PG
PD
RB
PC  n1  n2
P 1 2 PP GF  n  n  y P
FB  n1
PD  n2
FP  yFP FG  PC SC  yPP PG
1 S P
n  C C
2SC
1 S P
 C C
1
2SC
n2
1 S P
C
C C
B
2S
F 
1 S P
 C C
C
D
2S
P
F Pr G PPr Gy F  y P
 y 1 y  1 yPP SC y FPPPFP
PC
GF 

Input-Output Structure: Material Balance
Benzene Alkylation
Process
PC
FG
FB
PG
PD
RB
P = 104 kgmole h-1 cumeneC
XB FG
(kgmole h-1)
PD
(kgmole h-1)
FB
(kgmole h-1)
PG
(kgmole h-1)
0.1 113.04 0.00 103.99 9.04
0.2 113.10 0.03 104.02 9.05
0.3 113.17 0.06 104.05 9.05
0.4 113.24 0.10 104.09 9.06
0.5 113.33 0.13 104.13 9.07
0.6 113.41 0.17 104.17 9.07
0.7 113.51 0.22 104.21 9.08
0.8 113.61 0.27 104.26 9.09
0.9 113.73 0.32 104.31 9.10
1.0 135.75 10.45 114.44 10.86

Second Level Economic Potential
-5000000
45000000
40000000
35000000
30000000
25000000
20000000
15000000
10000000
5000000
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
ypp=0.1
ypp=0.5
ypp=0.7
ypp=0.898
Excess Reactant
Concentration in
Purge Stream
EconomicPotential(RM)/Year
fPE2  CC PC CFDIPB PDIPB CFP PP CP FP CB FB

Third Level
 The third level involves the construction of the
reactor and recycle structures of the process by
using heuristics to decide on
 the number of reactor systems required
 their types (completely mixed or plug flow)
 operating modes and conditions and
 heat management
 number of recycle streams
 whether a gas recycle is required, and
 recycle flow rates as functions of conversion and mole or
recycle ratio
 Annual costs of reactors & compressors are
subtracted from economic potential at this level

Third Level

Recycle Structure

Recycle Structure
Reactor
Separation
& Purification
System
Compressor
2 4
Benzene Product
Diphenyl Product
Hydrogen Feed
Toluene Feed
Toluene Recycle
Vapour Recycle RG
H , CH
Purge
H , CH
2 4
Toluene Hydro-Dealkylation Process FT
 T 
 F 
R  M y F  y RFH G PH G
X
yFH 11 yPH 1SB 2
y  y 

S y  X

MR
R 
FH PHTB PH
PB
G
RT 1XT FT
RT
Component Normal Boiling Point (C) Light/Heavy Destination
Hydrogen -253 Light Recycle dan Purge
Methane -161 Light Recycle dan Purge
Benzene 80 Heavy Main Product
Toluene 111 Heavy Recycle
Diphenyl 253 Heavy Fuel

Recycle Structure
Propane & Prop
As Fuel
ne
Propylene
Benzene
Benzene
RB 1 XPMRyPFFG
Reactor
Recycle RB
ylene
Separation
& Purification
System
Cumene
P-diisopropyl Benze
As Fuel
Point (C)
C6H6 80.1 Heavy Recycle
C9H12 152.4 Heavy Main Product
C12H18 210.3 Heavy Fuel

Adiabatic Temperature
• For simple reaction A  B,
• The adiabatic coversion
• Energy Balance for Reactors N

j1
• Adiabatic temperature
• In general
njH Pc T T  Fc T Tm0 k pk m
k1i1
KM
rj i pi a m
m
n H  Pc T T 0 T T n Hrj  i pi a
i1j1
 j
M
m a m j rj
N
m
Pc
i1j1
M
i pi
N
m
Picpi FA1XAcPA FAXAcpB
i1
M
FA1XAcPA FAXAcpB
Tm 25
j1 FAcpA
F X H 
N
n H o
A A r
m
j r
X H c c T 25
X c c cA pB pA pA
pB pA m
o
rA
Ta Tm 
cpATa Tm
H c c T 25
X 
pB pA a
o
r
Aa
Xa Fc T 25 njH
j1i1
N
a
rj
M
i pi a

800
600
1000
1200
1400
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Penukaran Toluena
kit
a
b
ai
d
A
u
h
u
S
1800
1600
Molar Ratio
MR=1
MR=2
MR=3
MR=4
Adiabatic Temperature
AdiabaticTemperature

Reactor Heat Management
XA
r = 1
r = 10
r = 100
1.0
0
T
Isothermal Reactor for Non-Autocatalytic Irreversible Reaction

Adiabatic Reactor for Non-Autocatalytic Irreversible Reaction
r = 1
r = 10
r = 100
XA
1.0
T
Endothermic Reaction
0
r = 1
r = 10
r = 100
XA
1.0
T
Exothermic Reaction
0

Endothermic Reaction Exothermic Reaction
Isothermal Reactor for Single Reversible Reaction
r = 1 r = 10 r = 100
XA
1.0
T
0
r = 0
Equilbrium
r = 1
r = 10 r = 100
XA
1.0
T
0
r = 0
Equilibrium

Adiabatic Reactor for Single Reversible Reaction
Endothermic Reaction Exothermic Reaction
Adiabatic Curve
r = 1 r = 10 r = 100
XA
1.0
T
0
r = 0
Equilibrium
XAf
r = 1
r = 10 r = 100
XA
1.0
T
0
r = 0
Equilibrium Adiabatic Curve
XAf

HEIRACHICAL PROCESS SYNTHESISCompressor and Reactor Sizing
n 1
P P n1 n
 12 1
nZRT1
W  RG
T T  P P n1 n
2 1 2 1
  FAo CAo
0
 rA
X A dX AV 
A
V  X A
F CAo Ao  r

F ln1 1  X T 
 y F  y R  F  oPH Go FH Gk exp(E / RT )
V

0.5
XT Compressor
(kW)
Reactor
Volume
(m3)
Reactor
Length
(m)
Diameter
(m)
0.1 3702.73 307.68 22.87 4.14
0.2 1787.160 314.99 22.87 4.19
0.3 1149.083 324.39 22.87 4.25
0.4 830.590 336.69 22.87 4.33
0.5 640.259 353.34 22.87 4.44
0.6 514.624 376.95 22.87 4.58
0.7 427.371 413.26 25.61 4.53
0.8 368.636 478.94 25.61 4.88
0.9 358.480 668.20 25.61 5.76

Third Level Economic Potential
2.11F    Cp81508.74
3IMSd
0.82
d
IMSk
pt
WW 
K 
      Fm Fp FIR
 MSd 
MSk
rt D L I 
 I  7775.3
K   

1.066 0.82
2.18
3
Material of
Construction
Carbon Steel Carbon steel chromium-
molybdenum
Stainless Steel
Fm 1.00 2.15 3.75
Compressor Fd
Centrifugal compressor with electric motor 1.0
Centrifugal compressor with turbine 1.15
Reciprocating compressor with steam 1.07
Reciprocating compressor with electric motor 1.29
Reciprocating compressor with engine 1.82
Pressure
(Bar)
1.6 6.8 13.6 20.4 27.2 34.0 40.8 47.6 54.4 61.2 68.0
FP 1.00 1.05 1.15 1.20 1.35 1.45 1.6 1.8 1.9 2.3 2.5

-20000000
-30000000
-40000000
-10000000
0
30000000
20000000
Molar Ratio
10000000
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
0.9 1
Penukaran
3
a
s
r
Ai
m
o
n
k
o
E
si
e
n
t
o
P
MR=2
MR=3
MR=4
MR=5
 CH FG  K pt  Krt
fPE3  CB PB  CFD PD  CFP PG  CT FT

0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
-10000000
fPE2  CC PC  CFDIPB PDIPB  CFP PP  CP FP  CB FB  Krt
10000000
20000000
40000000
Molar Ratio
30000000
50000000
ypp=0.1
ypp=0.5
ypp=0.7
ypp=0.898

Fourth Level
 The fourth level involves the synthesis of the
separation structure of the flow sheet
 Reactor products are to be separated into a liquid
and a vapor phase by cooling, decompressing or
both with the main product in the liquid phase
because liquid purification technology like
distillation can produce very pure product
 The liquid stream is sent to the liquid separation
train consisting usually of a distillation train that
are sequenced using heuristics
 The vapor stream may be purged and the rest of
the vapor recovered &/or recycled, or be
condensed into liquid, which is sent to the liquid
separation train, and the rest of
uncondensible vapor recovered,
recycled or purged

Fourth Level
 Selection of simple or complex columns and order of
distillation columns sequence using heuristics
 Distillation columns design using short cut methods
e.g. Fenske-Underwood-Gilliland (FUG)
 For non-ideal and azeotropic distillation
 Identify azeotropes & alternative separators
 Select entrainers & identify feasible distillate & bottom
products compositions
 Design variables: pressure, temperature & product
recoveries at flash drum, absorbers, adsorbers,
membrane modules & distillation columns
 Annual costs of separation systems are added to the
economic potential at this level

Fourth Level

Separation Structure

Separation Structure: Sub-cooled Liquid

Separation Structure:
Both Superheated Vapor & Sub-cooled Liquid

Separation Structure: Superheated Vapor

Flashing to Separate Liquid and Vapour
 Dew point
 Bubble point
 Flash calculation using Rachford-Rice method
Ki 1 xi   yi
1K 1i
i
(3.23)
 yi  Ki xi 1
y 
Ki zi
1 K 1
zi  Ki xi
zi
i
i
x 
  1 ix
(3.24)
(3.25)
f  
1 Ki zi
i1 1 Ki 1
C

i1
C

i1
 0
C
i iy  x  

Top Product
3.3 bar dan 35CReactor Product
Toluene
Conversion
XT
Toluene
(kgmole h-1)
Methane
(kgmole h-1)
Hydrogen
(kgmole h-1)
0.1 2395.15 19562.99 13042.75
0.2 1065.41 9591.88 6395.489
0.3 622.22 6270.52 4181.46
0.4 400.67 4612.69 3076.54
0.5 267.81 3622.01 2416.55
0.6 179.32 2968.12 1981.40
0.7 116.26 2514.08 1680.23
0.8 69.24 2208.71 1480.45
0.9 33.69 2157.52 1463.81
Toluene
Conversion
Methane
(kgmole h-1)
Hydrogen
(kgmole h-1)
Benzene
(kgmole h-1)
Toluene
(kgmole h-1)
Diphenyl
(kgmole h-1)
XT
0.1
19489.20 12986.6 17.24 51.29 1.8x10-5
0.2 9555.07 6367.45 16.96 22.43 2.1x10-5
0.3 6246.13 4162.88 16.75 12.93 2.6x10-5
0.4 4594.45 3062.64 16.48 8.19 3.2x10-5
0.5 3607.44 2405.44 16.22 5.38 4.2x10-5
0.6 2955.99 1972.15 15.98 3.55 5.9x10-5
0.7 2503.70 1672.30 15.82 2.28 9.2x10-5
0.8 2199.56 1473.44 15.77 1.35 1.7x10-4
0.9 2149.03 1457.23 16.56 0.69 5.9x10-4
Bottom Product
Toluene
Conversion
XT
Metahne
(kgmole h-1)
Hydrogen
(kgmole h-1)
Benzene
(kgmole h-1)
Toluene
(kgmole h-1)
Diphenyl
(kgmole h-1)
0.1 73.79 56.20 247.76 2343.86 0.5636
0.2 36.81 28.04 248.04 1042.99 0.6766
0.3 24.39 18.58 248.25 609.29 0.8325
0.4 18.25 13.90 248.52 392.49 1.0580
0.5 14.58 11.11 248.78 262.43 1.4057
0.6 12.13 9.25 249.025 175.78 1.9925
0.7 10.39 7.93 249.18 113.98 3.1331
0.8 9.16 7.01 249.235 67.89 5.9827
0.9 8.49 6.58 248.44 33.00 19.0978

Top Product
1.75 bar dan 90C (a)
1.75 bar dan 90C
Reactor Product
Propylene
Conversion
XP
Propylene
(kgmole h-1)
Propane
(kgmole h-1)
Benzene
(kgmole h-1)
DIPB
(kgmole h-1)
0.1 94.121 5.273 1975.874 0
0.2 83.711 5.276 936.505 0.030
0.3 73.292 5.279 590.055 0.062
0.4 62.863 5.283 416.837 0.096
0.5 52.423 5.286 312.912 0.133
0.6 41.971 5.290 243.635 0.174
0.7 31.505 5.295 194.157 0.218
0.8 21.023 5.300 157.055 0.266
0.9 10.522 5.305 128.205 0.320
0.99 1.256 6.333 137.868 10.451
Propylene
Conversion
XP
P
(kg
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.99
Bottom Product
Propylene
Conversion
XP
Propylene
(kgmole h-1)
Propane
(kgmole h-1)
Benzene
(kgmole h-1)
Cumene
(kgmole h-1)
DIPB
(kgmole h-1)
0.1 21.748 1.368 1780.262 102.717 0
0.2 11.622 0.835 777.299 101.639 0.029
0.3 8.551 0.704 472.044 101.134 0.061
0.4 7.136 0.686 331.385 101.047 0.096
0.5 6.289 0.725 251.894 101.221 0.133
0.6 5.653 0.812 200.999 101.557 0.173
0.7 5.134 0.979 166.004 102.037 0.217
0.8 4.617 1.309 140.564 102.632 0.266
0.9 3.762 2.087 121.025 103.300 0.319
0.99 1.184 6.019 137.592 103.970 10.450

Separation Structure: Liquid Separation

Sequencing of Simple Distillation Columns
Direct Sequence
Lightest First
Indirect Sequence
Heaviest First
1, 2, 3
2, 3
1 2
3
1, 2, 3
1, 2
2
1
3

Sequencing of Complex Columns
Complex Columns:
Common Reboiler
Complex Columns
Common Condenser
1, 2, 3
1 2
3
1, 2, 3
1
3
2

Sequencing of Complex Columns
Complex Columns:
Both Top & Bottom Products
of 1st Column as Feeds to
2nd Column with
One Side Product
Complex Columns:
Side Product Above or
Below Feed Point
1, 2, 3
3
1
2 1, 2, 3
1
3
2
1, 2, 3
1
2
3

Short-Cut Method for Multi-component Distillation
Fenske-Underwood-Gilliland (FUG)
• Fenske Equation to estimate minimum number of theoretical plate
• Underwood Equation to estimate minimum reflux ratio
• Gilliland Equation to Estimate number of theoretical plates
• Plate Efficiency: O’Connel Correlation
• Area of Condenser
• Area of Reboiler
1LK  HKln LK 1HK 
min
lnm
N   LK ,HK   LK ,HK
N 
1 2
1 m     
xD,LK xF,LK LK / HK xD,HK xF,HK 
 1LK / HK
Rmin  R 1.2Rmin
0.5688
N  Nmin min
 0.75 1
N 1 R 1
R  R  





 
N 
2N m
Eo 

0.252
2.841
F
A 
U T T  T
 T T 
ln dewc cwi
cwo  dewc T
VHv
cwic cwo
c
U T TR s dewR 
VHv
RA 

Short-Cut Method for Multi-component Distillation
• Height of distillation tower
• Diameter by using Fair Correlation for
H  0.69N Eo
0.01
0.1
1
0.01 0.1 1 10
FLV
Cf(m/s)
0.127 m
0.229 m
0.305 m
0.610 m
0.457 m
0.914 m
Distance between plate
0.5
 L V
 V  L 
 LM  
F  LV
VM 
C  FST FF FHACF
FFT = (L/20)0.2
FF < 0.75
0.6u 1 A A
1 2
4VMV


V 





D 
df
T
FHA = 1 if Ah/Aa > 0.1
FHA = 5(Ah/Aa) + 0.5
if 0.06 > A /A > 0.1h a
1 2
L V
 V 

u  C
   
f


Preliminary PFD without Heat Exchangers

Design of stabilizer
Top Product
XT
Rmin R Hydrogen
(kgmole h1)
Methane
(kgmole h-1)
Benzene
(kgmole h-1)
Toluene
(kgmole h-1)
0.1 0.354 0.530 56.194 73.780 2.478 0.000123
0.2 0.346 0.520 28.036 36.807 2.480 6.1x10-5
0.3 0.341 0.511 18.577 24.386 2.482 4.1x10-5
0.4 0.335 0.502 13.902 18.246 2.485 3.0x10-5
0.5 0.328 0.493 11.109 14.576 2.488 2.4x10-5
0.6 0.323 0.484 9.253 12.133 2.490 2.0x10-5
0.7 0.318 0.477 7.930 10.387 2.492 1.7x10-5
0.8 0.313 0.469 7.012 9.158 2.492 1.5x10-5
0.9 0.309 0.464 6.581 8.491 2.484 1.4x10-5
Bottom Product
XT Hydrogen
(kgmol j-1)
Benzene
(kgmol j-1)
Toluene
(kgmol j-1)
Diphenyl
(kgmol j-1)
0.1 0.0056 245.2841 2343.8599 0.5636
0.2 0.0028 245.5596 1042.9852 0.6766
0.3 0.0019 245.7695 609.2914 0.8325
0.4 0.0014 246.0318 392.4874 1.0580
0.5 0.0011 246.2942 262.4312 1.4057
0.6 0.00093 246.5303 175.7762 1.9925
0.7 0.00079 246.6877 113.9812 3.1331
0.8 0.00070 246.7402 67.8907 5.9827
0.9 0.00066 245.9531 32.9967 19.0978
XT Height
(m)
Diameter
(m)
Condenser Area
(m2)
Reboiler Area
(m2)
0.1 23.25 0.399 739.49 41.63
0.2 23.25 0.284 111.29 20.02
0.3 23.25 0.233 37.97 12.90
0.4 23.25 0.202 21.83 8.06
0.5 23.25 0.182 14.60 6.25
0.6 23.25 0.167 10.95 5.13
0.7 23.25 0.156 8.65 4.44
0.8 23.25 0.147 7.15 4.23
0.9 23.25 0.142 6.46 5.86

Design of benzene tower
Top Product
XT
Rmin R Benzene
(kgmole h-1)
Toluene
(kgmole h-1)
0.1 7.898 11.846 245.210 0.701
0.2 3.845 5.767 245.486 0.312
0.3 2.503 3.754 245.696 0.182
0.4 1.835 2.752 245.958 0.117
0.5 1.436 2.154 246.220 0.078
0.6 1.173 1.759 246.456 0.053
0.7 0.990 1.486 246.614 0.034
0.8 0.864 1.296 246.666 0.020
0.9 0.799 1.199 245.879 0.0098
Bottom Product
XT Benzene
(kgmole h-1)
Toluene
(kgmole h-1)
Diphenyl
(kgmole h-1)
Minimum
no. of plates
No. of
theoretical
plates
No. of
actual
plates
0.1 0.0736 2343.159 0.5636 19.1 38.2 59
0.2 0.0737 1042.673 0.6766 18.9 37.7 59
0.3 0.0737 609.109 0.8325 18.6 37.2 60
0.4 0.0738 392.370 1.0580 18.4 36.8 60
0.5 0.0744 262.353 1.4056 18.2 36.4 61
0.6 0.0740 175.724 1.9925 18.0 36.0 62
0.7 0.0740 113.947 3.1331 17.8 35.6 62
0.8 0.0740 67.870 5.9827 17.7 35.4 63
0.9 0.0738 32.987 19.0978 17.7 35.3 64
XT Height
(m)
Diameter
(m)
Condenser Area
(m2)
Reboiler Area
(m2)
0.1 44.2 3.3 1291.64 327.61
0.2 44.8 2.4 700.02 173.11
0.3 45.4 2.0 494.61 122.70
0.4 45.9 1.8 390.68 98.08
0.5 46.4 1.6 328.70 84.06
0.6 46.8 1.5 287.83 76.08
0.7 47.3 1.4 259.42 73.15
0.8 47.7 1.4 244.89 76.85
0.9 47.8 1.3 257.89 110.75

Design of toluene tower
Top Product
XT
Rmin R Toluene
(kgmole h-1)
Diphenyl
(kgmole h-1)
0.1 0.0413 0.0621 2342.456 0.000168
0.2 0.0414 0.0621 1042.361 0.000202
0.3 0.0414 0.0621 608.927 0.000249
0.4 0.0415 0.0622 392.252 0.000316
0.5 0.0416 0.0624 262.274 0.000420
0.6 0.0418 0.0627 175.671 0.000596
0.7 0.0425 0.0637 113.913 0.000936
0.8 0.0450 0.0675 67.850 0.001788
0.9 0.0653 0.0979 32.977 0.005708
Bottom Product
XT Toluene
(kgmole h-1)
Diphenyl
(kgmole h-1)
Minimum
no. of plates
No. of
theoretical
plates
No. of
actual
plates
0.1 0.703 0.563 5.04 10.08 15
0.2 0.313 0.676 5.04 10.08 16
0.3 0.183 0.832 5.04 10.08 16
0.4 0.118 1.058 5.04 10.08 17
0.5 0.079 1.405 5.04 10.08 17
0.6 0.053 1.992 5.04 10.08 17
0.7 0.034 3.132 5.04 10.08 18
0.8 0.020 5.981 5.04 10.08 18
0.9 0.009 19.092 5.04 10.08 18
XT Height
(m)
Diameter
(m)
Condenser Area
(m2)
Reboiler Area
(m2)
0.1 14.6 5.7 645.60 404.20
0.2 14.9 3.8 287.29 231.79
0.3 15.2 2.9 167.84 169.53
0.4 15.5 2.3 108.12 137.31
0.5 15.7 1.9 72.31 114.46
0.6 16.0 1.6 48.45 95.13
0.7 16.2 1.3 31.45 73.52
0.8 16.5 1.0 18.80 49.22
0.9 16.5 0.7 9.40 25.89

4
as
r
A
i
on
o
m
k
E
i
en
s
ot
P
-20000000
-25000000
-30000000
-35000000
-40000000
-45000000
-15000000
-10000000
-5000000
0
20000000
15000000
10000000
5000000
0 0.2 0.4 0.6 0.8 1
Penukaran
yph=0.4
yph=0.1
yph=0.2
yph=0.3
Fourth Level Economic Potential
Molar Ratio

Fifth Level
 In the fifth level, need for heat exchanges is reconsidered
 Heat exchanger network (HEN) is optimized & integrated by pinch
analysis based on First & Second Law of Thermodynamics
 Targeting for minimum number of heat exchangers (Fisrt Law) and
minimum utility requirement (Second Law)
 Identification of Hot & Cold Streams
 Second Law: Minimum approach temperature difference: 10C
 First Law: Energy cascade diagram
 Second Law: Temperature-enthalpy & grand composite curves:
Identification of pinch temperature
 HEN synthesis above & below pinch temperature
 Optimization of HEN synthesis by stream splitting & removal
of loops

Fifth Level

Preliminary PFD with Heat Exchangers

Hot & Cold Streams; Energy Cascade Diagram
Temp. Int. 2 = 40C
120 110
150 140
190200
250 240
100 90
FCp 1000 W/C 4000 W/C 3000 W/C 6000 W/C
1
2
3
4
160 150
C C
0 W
C
250
C
240
200 190
150 140
100 90
Cold
Utility
W
70,000 W 10,000 W
-40,000 W
Temp. Int. 2
-80,000 W
Temp. Int. 3
20,000 W
Temp. Int. 5
W
60,000 W
Hot
Utility
Stream
No.
Stream
Condition
Strea
m
Entha
lpy /
C
Tin
(C)
Tout
(C)
1 Hot 1000 250 120
2 Hot 4000 200 100
3 Cold 3000 90 150
4 Cold 6000 130 190
Total
50,000 W
Temp. Int. 1
50,000
40,000 W
Temp. Int. 4
40,000

Temperature-Enthalpy & Grand Composite Curves
90
110
130
150
170
190
210
230
250
0 100 200 300 400
Enthalpy (kW)
500 600
Entalpi (kW)
C
)o
(
uh
u
S
Entalpi Panas
Entalpi Sejuk
Entalpi Sejuk
Teranjak
90
110
130
150
170
190
210
230
250
0 20 40 60
Enthalpy (kW)
80 100 120
Entalpi (kW)
C)o
(
u
h
u
S
Hot Enthalpy
Cold Enthalpy
Shifted Cold
Enthalpy
Temperature(oC)
Temperature(oC)

Integrated PFD

H
I
EIRACHICAL PROCESS SYNTHESIS
ntegrated PFD

Sixth Level Poor process static & dynamic properties arise from using economic
viability for process selection causing off-spec products & excessive utilities
 Seider et al and Daud (2001) added a sixth level, where a plant-wide control
scheme is developed by using heuristics first introduced by Newell and Lee
 Selection of Control Variables:
 Heuristic 1: Select state variable representing
inventory that is not self
regulating
 Heuristic 2 Select state variable representing self regulating
inventory that transgress equipment’s limit or process condition
 Heuristik 3 Select state variable representing self
regulating inventory that interacts with another inventory
 Selection of Manipulated Variables:
 Heuristic 1: Select variable that acts directly with control variable
 Heuristic 2: Select variable that is more sensitive to control variable changes
 Heuristic 3: Select variable that acts vary fast
 Heuristic 4: Select variable that does not
interact with other control loops
 Heuristic 5: Select variable that does
not recycle any disturbance

Sixth Level

Mass & Energy Inventory Control: Reactor
L
LT
LCR
T
TT
FCR
FT
TCR

Mass & Energy Inventory Control: Heater

Distillation Control: Cut Control Top Product
LCR2
FCR1
LCR1
PCR1
R1FI1
FT1
L
LT1
P
PT1
L
LT2
FT1
FT2
R2 FCR2

Distillation Control: Cut Control Bottom
FCR2
PCR1
R1FI1
FT1
L
LT1
P
PT1
L
LT2
FT3
R2
FT2
FCR1
LCR1
LCR2

Distillation Control: Product Quality Control
PCR1
L
LT1
P
PT1
L
LT2
F
QT2
TCR1
FT1
T
TT1
F
QT1
QCR1
LCR1
LCR2
FCR1
QCR2

PROCESS SYNTHESIS &
OPTIMISATION
 The third approach is the algorithmic method to
search for and optimise process alternatives
 Process synthesis involving heavy mathematical
modelling are decomposed efficiently due to very
large combinatorial flowsheet possibilities and
then optimised
 One approach is a tree search in the space of
design decisions where design decisions are
recorded at a node which can be backtracked to a
previous node & branched in different directions
 The solution is optimised by using mixed integer
linear programming (MILP)

PROCESS SYNTHESIS &
OPTIMISATION
 Another method is the creation of a
superstructure of decisions containing most if
not all design alternatives and then using
mixed integer non linear programming (MINLP)
to optimise them
 Large superstructures might lead to very large
MINLP problems that might be unsolvable
 A viable alternative is to reduce the process
alternatives through the use of heuristics and
then optimise the reduced superstructure
using MINLP or MILP

PROCESS SYNTHESIS &
OPTIMISATION
 The most popular non linear programming
algorithm used in process optimisation is
the successive quadratic programming
(SQP)
 requires less function evaluations
 does not require feasible points at
intemediate iterations and
 converges to an optimal solution from an
infeasible point.

PROCESS SYNTHESIS &
OPTIMISATION
 Optimisation of reactor networks is
not very well developed mainly due to
the non-linear characteristics of
reacting systems
 Difficult to infer heuristic rules and
 Difficult to converge algorithmic
methods
 Novel method proposed by Glasser et
al. 1987 is to plot an attainable region
consisting of all the family of reactor
network solutions

PROCESS SYNTHESIS &
OPTIMISATION
 It is sufficient to get the reactor network
at the boundary of the attainable region
because any interior point is simply the
mixture of the boundary points
 In two dimensional problems, the
reactors need to be continuous stirred
tank reactors (CSTR) and plug flow
reactors (PFR) only
 The remaining problem is the integration
of reactor networks with the separation
system

CURRENT AND FUTURE
DEVELOPMENT
 More efforts should be devoted to the
generic modelling of
 adsorption
 membrane
 solid drying
 solids handling especially fluidisation and
pneumatic conveying
 Further work on integrating of process
control and process synthesis should be
developed using the structural control
matrix approach

CURRENT AND FUTURE
DEVELOPMENT
 Important issues being neglected are
 safe design and operation and
 waste minimisation
 Heuristic approach of Kletz using keywords
like intensification, substitution, and
attenuation pioneered chemical process plant
design for safety
 Recently rapid
inhenrently safe
2000
risk analysis is used to
design by Khan & Abbasia
 A related issue is design for maintainability

CURRENT AND FUTURE
DEVELOPMENT
 The minimum addition of chemical species
and their minimum production and
rejection
pioneered
minimum
in the mass exchange network
by El-Halwagi using the
number of “mass exchangers”
can minimise wastes
 Flower et al first proposed the use of mass
exchange networks for waste minimisation
 Recently Noureldina & El-Halwagi reported
a mass exchange network-based method
for pollution prevention

CURRENT AND FUTURE
DEVELOPMENT
 A method proposed recently by
Dantus & Higha is to evaluate source
reduction alternatives by
 economic performance including
waste related costs in an
environmental accounting
framework and
 the environmental impact of the
alternative

CURRENT AND FUTURE
DEVELOPMENT
 A new method which is now becoming the trend is the
combination of
 economic objectives and
 life cycle assessment (LCA)-based
environmental objectives
 Uses goal programming to identify the Pareto surfa
of non inferior solutions
 More research
incorporating
environmental
should be directed at
waste minimisation and
impact ideas in the
heuristics-based method of Douglas

Thanks for Watching
Please follow me / SAJJAD KHUDHUR ABBAS

Episode 47 : CONCEPTUAL DESIGN OF CHEMICAL PROCESSES

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Episode 47 : CONCEPTUAL DESIGN OF CHEMICAL PROCESSES

Similar to Episode 47 : CONCEPTUAL DESIGN OF CHEMICAL PROCESSES (20)

More from SAJJAD KHUDHUR ABBAS

More from SAJJAD KHUDHUR ABBAS (20)

Recently uploaded

Recently uploaded (20)

Episode 47 : CONCEPTUAL DESIGN OF CHEMICAL PROCESSES