INTER-PLANT WATER INTEGRATION: A CASE STUDY
FOR A MALAYSIAN INTEGRATED IRON AND STEEL MILL
IRENE M. L. CHEW, DOMINIC C. Y. FOO,
Department of Chemical and Environmental Engineering
University of Nottingham, Malaysia Campus
Broga Road, 43500 Semenyih, Selangor, Malaysia.
Irene.Chew@nottinham.edu.my
MAZURA. M. MAZLAN, PETER Y. C. HO
ERE Consulting Group
9, Jalan USJ 21/6, 47630 Subang Jaya, Malaysia
ABSTRACT
Iron and steel industry is a typical industry with enormous water consumption where large
portion of water is used for process cooling and washing. Implementation of proper water
integration scheme enables effective water recovery through reuse/recycle activities which
consequently reduce the overall water consumption in the mill. In this work, two inter-plant
water integration (IPWI) schemes are implemented for a Malaysian integrated iron and steel
mill that is currently at its conceptual design stage, that is, direct integration and indirect
integration. An integrated iron and steel mill usually occupies large site, hence IPWI is a
practical approach to be implemented as different water-using processes may be grouped into
different water networks according to their geographical location. The first scheme of direct
integration is formulated as a mixed integer linear program (MILP) model and solved to
achieve a globally optimal solution. For the indirect integration scheme, a mixed integer
nonlinear program (MINLP) is formulated and solved using a relaxation linearisation
technique to obtain an optimal solution. A total of 94% and 95% of water recovery rate are
achieved for the two schemes, respectively.
Keywords: Inter-plant water integration; Centralised utility hub; Water minimisation;
Integrated iron and steel mill; Process optimisation
INTRODUCTION
Process integration has been proven as a promising tool in maximising resource
conservation. Successful applications were reported for both insight-based and mathematical
optimisation approaches.
This includes petrochemical complexes [1-2], chemical
manufacturing [3], oil refineries [4-5], pulp and paper mill [6-7] and etc. Iron and steel
industry is another typical industry with enormous water consumption; hence, water
reuse/recycle is a common practice in this industry.
Iron and steel industry is generally divided into integrated and the conventional nonintegrated mills. Water efficiency is a measurement on how effective of water reuse/recycle
activity within the mill; and is often calculated as the amount of fresh water consumed in
producing a ton of steel (m3/ton steel). According to the reported sources, water efficiency in
an integrated mill is ranges from 5 m3/ton steel produced to 15 m3/ton steel produced [8-9].
Tian et al. [9] recently reported their work on water integration in iron and steel mill
using pinch analysis techniques, where a high water recycling rate of 97 % is achieved.
Although successful water conservation effort has been demonstrated in this work, the
conventional pinch analysis technique is limited in considering the water sources and sinks as
a single network. Hence, water-using processes that are physically located far away have
been excluded from the study. If these units are included in the study, water saving can be
more significant. On the other hand, water reuse/recycle strategy that was implemented on a
5.1 million ton/y integrated iron and steel mill at Port Kembala, has improved the water
efficiency from 5.5 to 3.5 m3/ton steel [10]. However, note that the water reuse/recycle
strategy proposed by Hird [10] is based on operational practical experience without prior
setting of minimum water flowrate targets, where optimum solution may be missed.
In this paper, two recently proposed IPWI schemes, that is, direct and indirect
integration [11] are analysed on a Malaysian iron and steel mill. The water using processes
are first grouped into different sections according to its processing tasks and geographical
location. Water network is synthesised thereafter using mathematical optimisation technique
to address the forbidden/compulsory matches [12] in the mill.
WATER USAGE IN INTEGRATED IRON AND STEEL MILL
In general, there are three types of wastewater generated from an integrated iron and
steel mill, i.e. cooling water, wash water and scrubber water. Cooling water can be further
classified into direct cooling and indirect cooling water. In the latter, cooling takes place
without the direct contact of cooling water with the equipment that require cooling. Table 1
shows the typical water using processes in an integrated iron and steel mill.
TABLE 1: Water-using Processes in an Integrated Iron and Steel Mill
Types of
Water
Water usage
Sections
Coke quench tower
Casting and rolling (C/R)
Cooling
Basic oxygen furnace (BOF)
water
Indirect
Sinter, coking, blast furnace (BF),
Equipments/machines cooling
cooling
BOF, continuous casting, C/R
Dust suppression
Raw material storage yard
BF scrubber
Scrubber Cleaning of gasses
Coke oven gas (COG) cleaning
Coking Oven
water
Acid fume cleaning
C/R acid fume scrubber
Ore cleaning
Raw material storage yard
Coke quench tower baffle cleaning
Coke quench tower
Wash
Slag processing
BF and BOF
water
Rinse water
C/R
Acid pickling liquid
Spent pickle baths
Direct
cooling
Coke cooling
Molten steel/slab cooling
BOF gas cooling
PROBLEM STATEMENT
It is given a set of water networks of the fixed flowrate problem with process sinks
and sources that may be considered for water reuse/recycle. Each water-using process has a
sink and source where sink, j denotes water-using processes that require water; while source,
i denotes water outlet which can be reused/recycled within the sink. Each sink and source
has a limiting water flowrate and concentration. It is required to synthesise an optimum interplant water network with direct and indirect water integration schemes.
MATHEMATICAL MODEL
Mathematical models for direct and indirect integration are adopted from Chew et al.
[11].
Direct Integration. The superstructure for direct integration scheme of an IPWI is shown in
Fig. 1. As shown, apart from being reused/recycled to sinks in the local network (dotted line),
process sources may also be integrated with sinks in other water networks (dashed line) for
further water recovery. Fresh water is the external source to be considered after the available
process sources are fully utilised. The unused water from the process source(s) is then sent
for treatment, before it is discharged to the environment.
Sources
Section A
i= 1
i = NSources
Fresh
water
Section B
i=1
i = NSources
Fresh
water
FSR
CSR
FSR
CSR
CF fF
Sinks
fR
FSR
CSR
FSR
CSR
CF fF
fE
fE
FSK
CSK
FSK
CSK
j=1
j = Nsinks
Waste
FSK
CSK
FSK
CSK
j=1
j = Nsinks
Waste
FIGURE 1: Superstructure for Direct Integration
The objective function for direct integration is given in Eq. 1 which minimises the
total annualised cost, ObjCOST for the water network system. Note that WCOST, and ECOST are
the unit cost of fresh water and effluent treatment, respectively.



min ObjCOST  
  f F ( j )  WCOST   f E (i )  ECOST 
j
i


f
f
iI
jJ
f
iI
(1)
R
(i, j )  f F ( j )  FSK ( j )
jJ
(2)
R
(i, j )  f E (i )  FSR (i )
iI
(3)
R
(i, j )  CSR (c, i)  f F ( j )  CF (c)  FSK ( j )  CSK (c, j )
LBCP  xDIR (i, j )  f R i, j   UBCP  xDIR (i, j )
  x i, j   N
iI k jJ k 
DIR
j  J , c  C (4)
i  I k , j  J k ' , k  k ' (5)
k  k  (6)
Eq. 2 and Eq. 3 state the flowrate balances for a sink, and source, respectively, with
variable fR(i,j) denoted reuse/recycle flowrate from source i to sink j. Eq. 4 ensures the
received contaminant loads for a sink does not exceed its maximum limit. Eq. 5 gives the
upper bound (UBCP) and lower bound (LBCP) of the cross-plant flowrates fR(i, j) while binary
variable xDIR(i, j) in Eq. 5 indicates the existence of cross-plant pipeline for direct integration.
Eq. 6 limits the total number of cross-plant pipeline to N. Note that in both Eq. 5 and Eq. 6,
source i is located in network k while sink j in network k’, indicating cross-plant integration.
Indirect Integration. The superstructure for indirect integration scheme of an IPWI is shown
in Fig. 2. Apart from being reused/recycled to sinks in the local network (dotted line),
process sources are sent to a centralised utility hub where the water is regenerated and
channelled back to the individual network for further reuse/recycle (dashed line). The unused
process source(s) is treated before it is discharge to the environment. The cross-plant
flowrate that is sent from source i to the utility hub is known as the export flowrate (fEXP(i))
while the cross-plant flowrate that is sent from utility hub to sink j is known as the import
flowrate (fIMP(j)). The resulting water mixture in the centralised utility hub has a contaminant
concentration of cmix(c).
Sources
Section A
i= 1
i = NSources
Fresh
water
fR
FSR
CSR
FSR
CSR
CF fF
fE
fEXP
Section B
FSR
CSR
FSR
CSR
CF
i=1
i = NSources
Fresh
water
FSK
CSK
FSK
CSK
j=1
j = Nsinks
Waste
fIMP
Hub
FSK
CSK
FSK
CSK
fR
fE
fF
j=1
j = Nsinks
Waste
FIGURE 2: Superstructure for Indirect Integration
The objective function in Eq. 1 is modified to 1 (a) to include the regeneration unit
cost, RCOST. Eq. 7, Eq. 8 and Eq. 9 state the flowrate balances for a sink, source and utility
hub, respectively. Eq. 10 sets the maximum allowable contaminant load entering sink j and
Eq. 11 is the contaminant load balance for the centralised utility hub. In this work, a
regeneration unit with a fixed removal ratio, RR is used. The RR is defined as the ratio of the
total contaminant mass removed at the regeneration unit, mREG, to the total inlet contaminant
load, shown in Eq. 12. Eq. 10 and Eq. 11, each contains a bilinear term, which renders the
model an MINLP problem. These bilinear terms are then linearised through a reformulationlinearisation technique [13].



min ObjCOST  
  f F ( j )  WCOST   f E (i )  ECOST  mREG  RCOST   AWH
j

J
i

I


(1a)
 f R (i, j )  f IMP ( j )  f F ( j )  FSK ( j )
jJ
(7)
 f R (i, j )  f EXP (i)  f E (i)  FSR (i)
iI
(8)
iI
jJ
 f EXP (i)   f IMP ( j )
iI
(9)
j
 f (i, j)  C (c, i)  f ( j)  C (c)  f ( j)  c (c)  F ( j)  C
 f ( j )  c (c)   f (i)  C (c, i)  (1  RR )
iI
j J
R
SR
IMP
RR 
F
mix
F
iI
mREG
 fEXP i  CSR c, i 
EXP
IMP
mix
SR
SK
SK
(c, j )
j  J , c  C (10)
c  C (11)
(12)
iI
CASE STUDY FROM A MALAYSIAN IRON AND STEEL MILL
A case study of an integrated iron and steel mill with a total steel production capacity
of 5 mil ton/y is analysed here. The plant consists of the upstream steel making plant and the
downstream casting and rolling mills (C/R). Three interconnected processes exist in the
upstream which are the blast furnace (BF), basic oxygen furnace (BOF) and continuous
casting. Below are the assumptions made in the case study:
(1) Typical electrodialysis unit with Cl- RR = 90 % is used to regenerate water [14]
(2) Freshwater supply contains maximum Cl- level of, CF = 15 mg/L [10]
(3) WCOST = USD 0.13/m3, ECOST = USD 0.22/m3, and RCOST = USD 0.24/m3
(4) Total number of plant operating day = 350 days/yr
(5) Water from indirect cooling (SR11) can be reused in other sinks but not vice versa (SK11
only accepts fresh water)
(6) Water from acid pickling plant (SR10) is forbidden to be reused in other sinks but it can
reuse water from other process sources.
(7) Self-recycle is made compulsory in each unit.
Limiting Data Extraction. The plant is generally divided into four sections, i.e. raw material
storage yard, cooking plant, steel making plant and C/R section. Sink flowrate at unit 11
(SK11) represents the total water used in indirect cooling in cooking plant, steel making plant
and C/R mills. Several contaminants present in the wastewater streams and their
concentration differ from one process to another. For the case of water recovery,
concentration of chloride ion (Cl-) was chosen as the limiting contaminant in the water
integration study. Chlorides are known to be present in the ore and tend to form hydrochloric
acid and alkali chlorides during the sintering process [15]. Hence, its present in the water
stream is the most important constraint in water recovery. A set of water limiting data with
Cl- as limiting contaminant is tabulated in Table 2. Few assumptions have been made in
setting the limiting Cl- concentration and flowrate such as:
(1) Water loss for wet cyclone scrubber (SK1) is set at a rate of 10% due to evaporation
(2) Water losses from unit 2 through 10 (SK2-SK10; SR2-SR10) are set at a rate of 3 % per
unit due to evaporation
(3) Water loss in indirect cooling (SK11) is at a rate of 2% due to evaporation and blow down
(4) Maximum acceptable Cl- level is taken as 20 mg/L for wet cyclone scrubber (SK1), coke
quench tower (SK2) and indirect cooling (SK11) to avoid visible stack emission [10]
(5) Maximum acceptable Cl- level is taken as 19 mg/L for COG scrubber (SK3) using
advance wet flue gas desulfurization method [16]
TABLE 2: Water Limiting Data for an Integrated Iron and Steel Mill
Sink
ClUnit
SKj
CSK(mg/L)
(k=A) Raw Material storage yard
Wet cyclone
20
1
scrubber
(k=B) Cooking Plant
Cook quench tower
20
2
COG Scrubber
19
3
(k=C) Steel Making Plant
Hot air scrubber [9]
75
4
Slag processing
80
5
Mold cooling
20
6
Slab cooling
20
7
(k=D) Casting/Rolling Mills
Fume absorber
20
8
Rinsing
20
9
Acid pickling
100
10
(k=E) Indirect cooling
Indirect cooling
20
11
Total
Sink Flowrate
FSK (mil m3/y)
Source
SRi
ClCSR (mg/L)
Source Flowrate
FSR (mil m3/y)
10.00
1
23
9.00
12.29
12.29
2
3
23
23
11.92
11.92
59.60
39.73
198.66
198.66
4
5
6
7
100
100
20.5
20.5
57.81
38.54
192.70
192.70
44.73
178.92
44.73
8
9
10
21
20.5
400
43.39
173.55
43.39
468.56
1268.16
11
20.2
459.18
1234.10
OPTIMISATION RESULTS
The mathematical models were formulated in GAMS version 2.5 using 1.66 GHz
Intel Core Duo Processor. Table 3 summarised the optimisation results for both direct and
indirect integration schemes. Water efficiency for these scenarios are reported as 16.49 and
13.59 m3/ton steel, respectively. It is demonstrated that, only a marginal 1 % improvement of
water reuse/recycle rate is observed through indirect integration scheme as compared to direct
integration scheme. Nevertheless, a noticeable increase of water savings are achieved
through indirect integration in which fresh water consumption and wastewater generation is
further reduced by 18 % and 30 %, respectively. The direct integration model is solved with
a total of 152 continuous variables, 242 binary variables, and 445 constraints while the
indirect integration on the other hand is solved with a total of 175 continuous variables, 121
binary variables, and 471 constraints. Fig. 3 and Fig. 4 show the optimised network design
for two different schemes, respectively. Referring to these figures, indirect cooling (SK11)
only receives fresh water and self-recycle water according to the forbidden matches setting.
Also, water source from acid pickling liquid (SR10) can only be utilised as self-recycle
stream (Fig. 3 and Fig. 4); no cross-plant piping connection is observed between the hub and
SR10 (Fig. 4).
TABLE 3: Summary Results for IPWI
Water efficiency, m3/ton steel
Total annualised cost, ObjCOST (mil USD/y)
Total freshwater consumption (mil m3/y)
Total wastewater generation (mil m3/y)
Water reuse/recycle rate (%)
Utility hub concentration, cM (mg/L)
Regeneration flowrate in hub (mil m3/y)
Number of cross-plant pipeline
CPU time, s
Direct
Integration
16.49
21.36
82.44
48.37
94
7
0.124
Indirect
Integration
13.59
23.48
67.94
33.88
95
8.4
29.97
7
0.451
CONCLUSION
The above work has demonstrated two IPWI schemes in an integrated iron and steel mill.
The use of mathematical optimisation approach enables the inclusion of forbidden matches
during network synthesis. Indirect integration helps to further reduce the overall fresh water
and wastewater flowrates by introducing a regeneration unit in the centralised utility hub.
Future work can consider multiple fresh feeds by adding an additional regeneration unit to
pre-treat the fresh water prior to its consumption.
ACKNOWLEDGEMENTS
The financial support from University of Nottingham through New Researcher Fund (NRF
3822/A2RBR9) and Research Studentship is gratefully acknowledged. Funding from the
Ministry of Science, Technology and Innovation (MOSTI) Malaysia through Science Fund
(03-02-12-SF0018) is deeply appreciated.
i=9
11.81
FSR = 173.55 (20.5 mg/L)
i = 10
FSR = 43.39 (400 mg/L)
0.84
fE = 8.16
(23 mg/L)
fF = 18.02
FSK = 468.56
(20 mg/L)
i = 11
FSR = 459.18
8.65
450.53
(20.2 mg/L)
6.38
i=3
FSR = 11.92 (23 mg/L)
11.08
11.08
D
161.74
7.24
12.32
0.84
(21 mg/L)
fE = 40.21
3.17
10.06
scrubber
FSK = 59.60
(75 mg/L)
processing
FSK = 39.73
(80 mg/L)
C
18.06
j = 7 Slab
18.06
j = 6 Mold
cooling
FSK = 198.66
(20 mg/L)
cooling
FSK = 198.66
(20 mg/L)
i=4
28.83
28.98
FSR = 57.81 (100 mg/L)
scrubber
FSK =12.29
(19 mg/L)
i=2
0.84
9.95
FSR = 11.92 (23 mg/L)
FSK = 44.73
(100 mg/L)
j = 5 slag
3.66
j=3 COG
quench tower
FSK =12.29
(20 mg/L)
FSK = 178.92
(20 mg/L)
j = 4 Hot air
B
1.50
j=2 Cook
j = 10 Acid pickling
fF = 36.12
1.41
fF = 5.16
16.34
j = 9 Rinsing
20.17
i=8
FSR = 43.39
j =11 Ind Cooling
E
5.51
j=8 Fume absorber
FSK = 44.73
(20 mg/L)
7.87
scrubber
FSK =10
(20 mg/L)
i=1
FSR = 9
fF = 21.85
A
j=1 Wet Cyclone
fF = 1.29
0.84
16.33
22.16
i=5
FSR = 38.54
29.24
0.84
8.46
(100 mg/L)
i=6
FSR = 192.7 (20.5 mg/L)
i=7
FSR = 192.7 (20.5 mg/L)
5.07
179.76
0.84
175.53
FIGURE 3: Direct Integration
A
scrubber
FSK =10
(20 mg/L)
i=1
FSR = 9
7.95
(23 mg/L)
17.57
fF = 33.28 6.23
j=8 Fume absorber
2.06
j= 1 Wet Cyclone
j=9 Rinsing
FSK = 178.92
(20 mg/L)
FSK = 44.73
(20 mg/L)
146.97
i=9
0.84
FSR = 173.55 (20.5 mg/L)
1.06
9.48
D
j=10 Acid pickling
FSK = 44.73
(100 mg/L)
25.75
9.51 fE=33.88
i = 10
FSR = 43.39 (400 mg/L)
fF = 18.02
j = 11 Indirect cooling
FSK =468.56
(20 mg/L)
E
450.53
i = 11
FSR = 459.18 (20.2 mg/L)
8.65
2.53
B
j=2 Cook
quench tower
FSK =12.29
(20 mg/L)
FM = 29.97
mil m3/yr
CM = 8.4 mg/L
90 % removal
efficiency
22.02
23.76
j=3 COG
4.9
7.29
i=4
5.16
j = 4 Hot air
j = 6 Mold
j = 7 Slab
scrubber
FSK = 59.60
(75 mg/L)
processing
FSK = 39.73
(80 mg/L)
cooling
FSK = 198.66
(20 mg/L)
cooling
FSK = 198.66
(20 mg/L)
42.21
0.84
29.54
i=5
FSR = 38.54 (100 mg/L)
i=6
C
9.35
j = 5 slag
FSR = 57.81 (100 mg/L)
9
3
i=3
FSR = 11.92 (23 mg/L)
8.21
14.76
scrubber
FSK =12.29
(19 mg/L)
8.9
14.38
8.91
fF = 16.64
3.37
i=2
6.77
FSR = 11.92
29.01
i=8
FSR = 43.39 (21 mg/L)
8.48
184.22
FSR = 192.7 (20.5 mg/L)
i=7
6.23
FSR = 192.7 (20.5 mg/L)
FIGURE 4: Indirect Integration with Regeneration Unit
186.47
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