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 iI jJ f iI (1) R (i, j ) f F ( j ) FSK ( j ) jJ (2) R (i, j ) f E (i ) FSR (i ) iI (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 iI k jJ 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 ) jJ (7) f R (i, j ) f EXP (i) f E (i) FSR (i) iI (8) iI jJ f EXP (i) f IMP ( j ) iI (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 ) iI j J R SR IMP RR F mix F iI mREG fEXP i CSR c, i EXP IMP mix SR SK SK (c, j ) j J , c C (10) c C (11) (12) iI 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 REFERENCES [1] Liu, Y.A., Lucas, B., and Mann, J. 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