Flowrate Targeting Algorithm for Interplant Resource Conservation

Ind. Eng. Chem. Res. 2010, 49, 6439–6455

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Flowrate Targeting Algorithm for Interplant Resource Conservation Network. Part 1: Unassisted Integration Scheme Irene M. L. Chew, Dominic C. Y. Foo,* and Denny K. S. Ng Department of Chemical and EnVironmental Engineering, UniVersity of Nottingham Malaysia, Broga Road, 43500 Semenyih, Selangor, Malaysia

Raymond R. Tan Center for Engineering and Sustainable DeVelopment Research, De La Salle UniVersity, 2401 Taft AVenue, 1004 Manila, Philippines

This paper is part 1 of a series describing a new algorithm for targeting minimum fresh resource and waste flow rates for an interplant resource conserVation network (IPRCN). The IPRCN enables the reuse of the excess process sources among different networks, which reduces the consumption of fresh resource and generation of waste simultaneously. Four hypothetical examples are presented to illustrate both concentrationand property-based utility gas and water integration problems. In addition, the proposed targeting algorithm is adapted in the synthesis of total resource network, where the minimum regeneration and waste treatment flow rates can be targeted prior to the detailed design of an IPRCN. Introduction On the basis of the recent world water statistical analyses, 47% of the world population or an equivalent of 3.9 billion people will be living in areas of high water stress by 2030.1,2 Rapid growth of industries which involve water consumption for utility cooling/heating, processing, air conditioning, cleaning, etc. has contributed to serious water pollution and shortage in the world.3 Thus, efforts from various parties such as government, industrial practitioners, and scientists are urged to undertake effective measures to improve water efficiency. Efficient utilization through water reuse/recycle has hence been identified as a promising means for reducing the water stress faced by the global community.4 It is now well-understood that the efficient use of other valuable resources (e.g., utility gas, steam, etc.) plays a vital role in achieving sustainable development.2,4 The development of various systematic design techniques based on process integration has made it a promising tool in resource conservation activities. Numerous research works have been carried out to systematically address water reuse/recycle problems. Significant advances were achieved in the insightbased pinch analysis techniques to target the minimum fresh water and wastewater flow rates for various water network synthesis problems, ranging from direct reuse/recycle,5–15 regeneration,5,6,13,16–22 pretreatment,23 and wastewater treatment.12,24–28 Detailed reviews for most of these techniques were reported by Bagajewicz29 and Foo.30 On the other hand, utility gas network problems have also gained significant attention from the process integration community. Targeting for minimum gas utility was initiated by Towler and co-workers for the integration of refinery hydrogen network.31,32 Later works in this area include the various graphical7,11,17,33,34 and algebraic/numerical approaches.35,36 Provided that only stream purity is of interest, gas network problems are structurally equivalent to water network ones. The * To whom correspondence should be addressed. Tel.: +60-3-89248130. E-mail: [email protected] (I.M.L.C.); dominic.foo@ nottingham.edu.my (D.C.Y.F.); [email protected] (D.K.S.N.); [email protected] (R.R.T.).

assumption is usually valid since gas pressure can be readily adjusted if needed using compressors and valves, or by appropriate rerouting of the recycle stream.17 Apart from the concentration-based water and hydrogen integration, the stream quality in many chemical processes is often characterized by their chemical or physical properties. Hence, property integration has been proposed to handle such a problems.37,38 Several important works on property integration based on the pinch analysis technique have been reported, including the various graphical11,38,39 and algebraic/numerical39–43 targeting techniques. Nevertheless, all the above-mentioned works in water, utility gas, and property integration are restricted to a single resource conservation network and cannot be applied directly for IPRCN problems that involve multiple networks. Because of this limitation, new targeting techniques were developed for IPRCN, where source(s) from one network can be reused to another network so long as a driving force exists. In the context of interplant water integration (IPWI), the flow rate targeting technique was first introduced by Olesen and Polley44 for the fixed load problem. Spriggs et al.45 later proposed the use of the material recoVery pinch diagram (MRPD)7,10 for the fixed flow rate problems, however without detailing the targeting procedure. Recently, Foo46 extended the use of water cascade analysis (WCA), which was originally developed for a single water network8,13 into IPWI of the fixed flow rate problems. However, the approach requires the generation of all alternative IPWI schemes before the minimum water flow rate targets can be determined. This is cumbersome especially when dealing with large number of water networks. Bandyopadhyay et al.47 recently proposed a generalized decomposition algorithm that may be applied for the special case of IPRCN, in which each individual network is supplied with a different quality of external fresh resource but shares the common process sources. On the other hand, mathematical optimization techniques have also been reported to handle the more complicated cases of IPRCN problems. In a reported case of integrated pulp and bleached paper production,48 mass integration strategies were incorporated in the mathematical model to handle multiple

10.1021/ie901802m  2010 American Chemical Society Published on Web 06/28/2010

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Ind. Eng. Chem. Res., Vol. 49, No. 14, 2010

contaminants present in the problem. Later work by Chew et al.49 studied two different schemes of IPWI, i.e., direct and indirect integration. The former involves the integration among individual process sinks and sources via a cross-plant pipeline, while indirect integration recovers resources via a centralized utility hub.49 The latter scheme was also extended for IPRCN based on property integration.50 More recently, automated targeting technique that incorporates the targeting concept of pinch analysis into a mathematical optimization framework has been proposed to locate the minimum flow rates/cost targets prior to detailed network design.51 Other mathematical optimization works in IPRCN include the multiperiod problem52 and the fuzzy optimization approach that considers the goals of individual companies and other decision-makers (e.g., government) in an eco-industrial park (EIP).53–55 It is also worth noting that the IPRCN problem is analogous to the interplant heat integration problem, where energy integration is carried out across different plants.56–60 In the work of Rodera and Bagajewicz,59 two distinctive forms of interplant heat integration schemes are introduced, which are termed as the unassisted and assisted schemes. The former takes place when interplant heat transfer occurs within the pinch region (bounded by the temperatures of the pinch temperature of the participating plants) to achieve the maximum overall energy recovery, with minimum overall utility targets. On the other hand, assisted heat integration scheme occurs when heat transfer outside the pinch region is needed to achieve maximum energy recovery.59 In their later work, mathematical optimization technique is adopted to deal with problems of multiple plants.60 As will be shown in this series of papers, the same principles of unassisted and assisted schemes apply for material recovery in an IPRCN. The unassisted integration scheme takes place when maximum material recovery is achieved by transferring cross-plant streams within the pinch region, which is bounded by the upper and lower pinch concentrations (for concentrationbased IPRCN) or property operators (for property-based IPRCN) of the participating networks. On the other hand, the assisted integration scheme requires cross-plant streams to be transferred within and outside the pinch region to achieve maximum material recovery. In general, targeting using the unassisted scheme for an assisted problem will result in lower material recovery (resulting in higher fresh resource and waste generation) as compared to when the assisted scheme is used. However, the use of the assisted scheme will result in higher capital cost due to additional cross-plant piping as will be shown in part 2 of the series.61 In the targeting for IPRCN, it is important to determine if a given problem belongs to the unassisted or assisted type. Hence, targeting using the unassisted scheme is always performed first and the obtained overall flow rate targets are compared to the single-network targets when all the sinks and sources are treated as being in the same network. If the two flow rate targets match, maximum resource conservation has been achieved and thus the case is concluded as an unassisted case. For cases with unmatched targets (i.e., the minimum resource flow rate of the single network solution is much lower), the problem is of the assisted type, where the assisted scheme is required to determine the flow rate targets. The use of the assisted integration scheme will be discussed in detail in part 2 of the series.61 In this part 1 of the series, a new targeting algorithm that locates the minimum flow rate targets for the unassisted integration scheme is proposed. Note that the targeting algorithm is generic in nature, such that any of the established and equivalent flow rate targeting tools may be used to determine

Figure 1. Principles of the unassisted integration scheme.

the minimum IPRCN flow rate targets. Hence, the targeting algorithm is illustrated using algebraic and graphical targeting tools, i.e., the cascade analysis technique,8,13,35,39 MRPD,7,10,38 and the material surplus composite curVe (MSCC).62 Besides, the implication of pinch shifting in the individual network is also analyzed. In most cases, the shifting of the original pinch of an individual network (to a higher quality level) when receiving cross-plant streams indicates that an excess flow rate of cross-plant streams has been transferred. Hence, pinch shifting is normally avoided, in order to reduce the unnecessary piping cost.46 However, as will be shown in the later section, there are exceptional cases where the shifting of pinch could be permitted in order to achieve the actual minimum flow rates in the individual network. Finally, targeting involving a total network is also presented, where regeneration and waste treatment are included in the analysis. Four examples involving concentration- and property-based IPRCN problems are used to illustrate the newly proposed algorithm. Principle of the Unassisted Integration Scheme The basic principle of the unassisted scheme is illustrated using an integration of two networks, i.e., networks A and B in Figure 1. For ease of discussion, a simplified form of cascade analysis is utilized.8,13,35,39 As shown, a material cascade for each network is set up in ascending order of its quality index level; the latter may take the form of concentration (for water and hydrogen networks) or property operator values (for property integration problems). In most cases, a higher quality resource is indicated by the quality index of lower numerical values and vice versa. As shown in Figure 1, the fresh resource (FR) is added at the first quality level of the material cascade, while waste (FW) is withdrawn from the last quality level. Each network has its own individual pinch point, in which the network is divided into the higher (HPR) and lower purity region (LPR). The HPR is located above the network pinch in the material cascade, where resource deficit is experienced (represented by the shaded area in Figure 1). In contrast, LPR is located below the network pinch (represented by the dotted area in Figure 1), in which material surplus is observed.8,13,35,39 As mentioned earlier, an unassisted scheme occurs when cross-plant streams are observed within the pinch region. As shown in Figure 1, the pinch region is bounded by the upper and lower pinch points (levels m ) 2, 4) of the participating networks. For an unassisted integration scheme, the cross-plant

Ind. Eng. Chem. Res., Vol. 49, No. 14, 2010 Table 1. Limiting Water Data for Example 1 flow rate concentration sources flow rate concentration water FSKj i FSRi (mol/s) CSRi (mol %) network k sinks j (mol/s) CSKj (mol %)

35

A

B32

SK1 SK2 SK3 SK4 SK5 SK6

SK7 SK8 SK9 SK10

120.00 27.80 80.00 60.00 100.00 150.00

2495.00 180.20 554.40 720.70

0.10 1.40 2.50 2.50 3.00 10.00

19.39 21.15 22.43 24.86

ΣjFSKj 4488.10

SR1 SR2 SR3 SR4 SR5 SR6 SR7

80.00 40.00 80.00 28.55 80.00 120.00 75.00

1.70 1.70 2.50 4.00 5.00 10.00 15.00

SR8 SR9 SR10 SR11 SR12 SR13

623.80 415.80 1801.90 138.60 346.50 457.40

7.00 20.00 25.00 25.00 27.00 30.00

ΣiFSRi

4287.55

streams are normally transferred from the LPR of the network with a higher purity pinch (network A) to the HPR of the network with a lower purity pinch (network B), within the pinch region. As shown in Figure 1a, since the pinch location of network A is of a higher quality level (and hence lower index) than that of network B, a driving force exists between the two pinches. This leads to feasible material recovery between the two networks. Clearly, the cross-plant streams from the LPR of network A will reduce its waste flow rate, as material is withdrawn from the region that has excess resource(s). On the other hand, network B will gain additional fresh resource savings as it receives additional sources in its HPR, where resource deficit is experienced. Note that the overall pinch(es) of the IPRCN will always coincide with the pinch(es) of one or more of the individual networks (Figure 1b). This will be shown using the examples in the following section. Targeting for Reuse/Recycle Example 1: Hydrogen Network. The first example consists of the integration of two hydrogen networks (A and B) taken from Alves and Towler32 and Foo and Manan,35 with the limiting data given in Table 1. In the following section, a novel three-step IPRCN targeting algorithm will be presented to locate the minimum fresh hydrogen and purge gas flow rate targets for the overall network. For this example, the algebraic targeting tool of gas cascade analysis (GCA)35 is utilized. Note that besides GCA,35 any equivalent flow rate targeting tool for the hydrogen network7,11,17,32,36 may also be used. Step 1: Identification of Limiting Data for IPRCN. The first step of the new targeting algorithm calls for the identification of a new set of limiting data for IPRCN targeting. Note that this new limiting data is different from the original data used for flow rate targeting in the individual networks (given in Table 1), as it consists of the individual waste streams emanating from each network. In order to obtain the new limiting data, flow rate targeting is first carried out for each individual network using the GCA technique.35 The first step in conducting a GCA is to locate the hydrogen sink and source flow rates at their respective concentration levels (Cm). As shown in Table 2, concentration levels are arranged in an ascending order and the flow rate of each hydrogen sink (FSKj) and source (FSRi) is located in its respective concentration level in columns 2 and 3. Column 4 represents the net flow rate (Σi FSRi - Σj FSKj) between the hydrogen sources and sinks at each concentration level; positive values indicate a flow rate surplus, and negative values indicate a flow rate deficit. The net flow rate surplus/deficit is then cascaded down the concen-

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Table 2. Flow Rate Targeting with GCA for Network A in Example 135 Cm (mol %)

0.1 1.4

ΣjFSKj (mol/s)

ΣiFSRi (mol/s)

-120

120

-27.8

27.8

1.7 140

3

100

80

-100 28.55

5

80

80

15 100

120

-30

75

75

cumulative ∆Lm (mol/s)

FH ) 125.21 5.21

0.07 -0.07

97.41

0.78

37.41

0.19

-60

28.55

150

∆Lm (mol/s)

120

4

10

FC,m (mol/s)

-22.59 120

2.5

ΣiFSRi ΣjFSKj (mol/s)

-62.59

-0.63

-34.04

-0.34

45.96

2.30

15.96

0.80

FP ) 90.96

0.07 0.00 (limiting pinch) 0.78 0.97 0.34 0.00 (secondary pinch) 2.30 3.10

77.32 80.41

tration levels to yield the cumulative flow rate (FC, m) in column 5. The impurity load (∆Lm) in column 6 is obtained from the product of the cumulative flow rate (FC, m) and the difference across two concentration levels. Cascading the load down the concentration levels yields the cumulative load (cumulative ∆Lm) in column 7. Note that, for simplicity, the cascade table in Table 2 only shows the feasible cascade in which the minimum fresh hydrogen flow rate (FH) is used (this latter parameter was originally determined from the infeasible load cascade with an assumed zero fresh hydrogen flow rate). Readers may refer to the original work35 for the steps in generating the feasible cascade. Table 2 shows the cascade table for network A using the original limiting data in Table 1. Note that the fresh hydrogen for this network is supplied at the concentration level of 0.1 mol %. As shown in Table 2, the minimum fresh hydrogen (FH) and purge (FP) flow rates for this network are targeted as 125.21 and 90.96 mol/s, respectively.35 This is a double-pinch problem with the limiting pinch concentration identified at 1.7 mol % and the secondary pinch at 5 mol %.35 The network is then divided by the pinch into the HPR (0.1-1.7 mol %), intermediate purity region (1.7-5.0 mol %), and LPR (5.0-100 mol %). The HPR experiences hydrogen scarcity and hence fresh hydrogen is needed to meet the demand of the hydrogen sinks. In contrast, excess hydrogen sources are purged from the LPR and will be extracted as the new limiting data for the IPRCN study. To identify purge streams from a hydrogen network, it is important to identify the pinch-causing source at the secondary pinch concentration, as well as its allocation flow rates to each region.26 From Table 2, this source corresponds to SR5, with its allocated flow rates determined from the concentration intervals just higher and just lower than the pinch concentration (5 mol %), i.e., 34.04 (to intermediate purity region) and 45.96 mol/s (to LPR). To identify the individual purge streams generated in the LPR, the waste targeting procedure proposed by Ng et al.26 is adopted, which takes advantage of the flexibility in the allocation of sources in the LPR. As the process sinks in the LPR are often not saturated to their maximum concentration limits, it is

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Ind. Eng. Chem. Res., Vol. 49, No. 14, 2010 Table 5. New Limiting Data with Unassisted Integration Scheme for Example 1

Table 3. Purge Stream Identification with GCA for Network A (Example 1) ΣjFSKj ΣiFSRi ΣiFSRi - ΣjFSKj Cm (mol %) (mol/s) (mol/s) (mol/s)

FC,m (mol/s)

∆Lm (mol/s)

flow rate water flow rate FSRi concentration concentration network FSKj k sinks j (mol/s) CSKj (mol %) sources i (mol/s) CSRi (mol %)


FPINCH ) 15 5 10

150

15

120

-30

75

75

15

0.75

-15

-0.75

60

51

100

0.75 A

0.00 (PINCH) 51.00

therefore possible to generate purge streams of better quality (and thus with better potential for reuse in another network) for IPRCN simply by allocating the lowest quality sources that can be tolerated for in-plant reuse/recycle. As shown in Table 3, a separate GCA is conducted for sinks and sources in the LPR. As no fresh hydrogen is supplied to this region, the pinchcausing source is the highest quality hydrogen source in the LPR. Next, the minimum pinch flow rate26 needed for the LPR is determined. This is done by replacing the fresh hydrogen concentration with the concentration of the pinch-causing source at 5 mol % (Table 3). As shown in Table 3, the minimum pinch flow rate (FPINCH) needed to satisfy the flow rate and load requirement in the LPR is determined as 15 mol/s. The purge stream at 5 mol % (P1) is hence obtained by deducting the minimum pinch flow rate from the allocated flow rate of the pinch-causing source to the LPR, i.e., 30.96 mol/s () 45.96 15 mol/s). Besides, another purge stream (P2) of 60 mol/s is emitted from the final concentration level of 15 mol % (Table 3). Summing the individual purge streams yields a total flow rate of 90.96 mol/s () 30.96 + 60 mol/s), which matches the overall purge target (FP) identified previously in Table 2. On the other hand, network B is supplied with fresh hydrogen of 5 mol % impurity. Note that the fresh hydrogen supply is of different impurity than that of network A. This is to demonstrate the more generic case with multiple fresh resources. In the actual case, this may due to the two networks being serviced by different utility gas suppliers or being designed prior to the consideration of IPRCN. Repeating the GCA targeting technique for this network identifies its minimum fresh hydrogen and purge flow rates of 268.82 and 102.52 mol/s (see Table 4), respectively, consistent with other earlier works.7,32 Note that network

B

sink in HPR SK1 SK2 SK3 SK4 SK5

120.00 27.80 80.00 60.00 100.00

sink in HPR SK7 2495.00 SK8 180.20 SK9 554.40 SK10 720.70

0.10 1.40 2.50 2.50 3.00

19.39 21.15 22.43 24.86

source in HPR SR1 SR2 SR3 SR4 SR5 purge in LPR P1 P2

80.00 40.00 80.00 28.55 34.04

1.70 1.70 2.50 4.00 5.00

30.96 60.00

5.00 15.00

source in HPR SR8 623.80 SR9 415.80 SR10 1801.90 SR11 138.60 SR12 346.50 SR13 354.88 purge in LPR P3 102.52

7.00 20.00 25.00 25.00 27.00 30.00 30.00

B has a pinch concentration identified at 30 mol %, which is higher than both pinches of network A. This defines the pinch region that is bounded by the pinch concentrations of the two networks, i.e., 5 (network A) and 30 mol % (network B). Note further that the pinch concentration of network B is observed at the second last concentration level (see Table 4), with no other hydrogen sinks and sources present in the LPR. Hence, the purge stream (P3) is essentially originated from the pinchcausing source of SR13. After identification of all purge streams as potential sources for the IPRCN study, the original sinks and sources in the HPR are then extracted as part of the new limiting data for the IPRCN. Note that for the LPR, only the individual purge streams are extracted, while the original process sinks and sources are omitted. This is due to the reason that the sinks are fully loaded to their maximum impurity load limit, while the excess sources have been extracted as the purge streams. The complete new limiting data for the IPRCN is summarized in Table 5. As shown, for network A, two purge streams are identified, i.e.,

Table 4. Flow Rate Targeting with GCA for Network B in Example 135 Cm (mol %)

ΣjFSKj (mol/s)

ΣiFSRi (mol/s)

ΣiFSRi - ΣjFSKj (mol/s)

FC,m (mol/s)

∆Lm (mol/s)


FH ) 268.82 5.00

0.00

7.00 19.39

623.80

-2 495.00

2 495.00

20.00

415.80

415.80

21.15

180.20

-180.20

22.43

554.40

-554.40

24.86

720.70

-720.70

25.00

1 940.50

1 940.50

27.00

346.50

346.50

30.00

457.40

457.40

100.00

268.82

537.64

892.62

11 059.57

623.80

537.64 -1 602.38

-977.45

-1 186.58

-1 364.57

-1 366.78

-1 749.48

-1 921.18

-4 668.46

-2 641.88

-369.86

-701.38

-1 402.76

-354.88

-1 064.64

FP ) 102.52

7 176.47

11 597.22 10 619.77 9 255.20 7 505.72 2 837.26 2 467.39 1 064.64 0.00 (PINCH) 7 176.47


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Table 6. Flow Rate Targeting with GCA for IPRCN in Example 1 Cm (mol %) CH1 ) 0.10 1.40

ΣjFSKj (mol/s)

ΣiFSRi (mol/s)

120.00

-120.00

27.80

-27.80

1.70

120.00

2.50

140.00

3.00

100.00

80.00

28.55

CH2 ) 5.00

201.86 (FH2) + 65

7.00

623.80

15.00

-60.00

28.55

20.00

5.21

6.78

-22.59

-6.78

97.41

77.93

37.41

18.71

-62.59

-62.59

-34.04

-34.04

232.82

465.64

856.62

6 852.96

916.62

4 023.96

-1 578.38

-962.81

-1 162.58

-1 336.97

21.15

180.20

-180.20

-1 342.78

-1 718.76

22.43

554.40

-554.40

-1 897.18

-4 610.15

24.86

720.70

-720.70

-2 617.88

-366.50

25.00

1 940.50

1 940.50

27.00

346.50

346.50

30.00

457.40

457.40

100.00

P1 and P2, which are originated from sources SR5 and SR7 (see Table 1). Note that, since SR5 is the pinch-causing source for network A, only part of its flow rate is extracted (34.04 mol/s), as the remaining are sent for the sinks as well as the purge stream (P1) in the LPR. The same principle applies to the pinch-causing source of network B (i.e., SR13). Step 2: Targeting for Minimum Resource Flow Rates for IPRCN. Targeting is next conducted for IPRCN using the new limiting data in Table 5, with the results summarized in Table 6. Because of the different quality of fresh hydrogen sources, i.e., CH1 ) 0.1 mol % (for network A) and CH2 ) 5 mol % (for network B), the multiple-source targeting procedure14 is performed. The overall minimum fresh hydrogen flow rates are hence targeted as 125.21 (FH1 for 0.1 mol % source) and 201.86 mol/s (FH2 for 5 mol % source), respectively. The detailed multiple-source targeting procedure may be found in the original work by Foo.14 Besides, the minimum purge flow rate (FP) for the IPRCN scheme is determined as 126.52 mol/s. Note that this is a double-pinch problem, similar to the original problem of network A. However, only the limiting pinch (1.7 mol %) coincides with the limiting pinch in network A, while the secondary pinch (30 mol %) coincides with that of network B. It is worth noting that the single-network targets (i.e., without having to identify individual purge streams) are also determined as being identical to the targets obtained from the unassisted scheme, i.e., FH1 ) 125.21, FH2 ) 201.86, and FP ) 126.52 mol/s. Hence, it can be concluded that example 1 is classified as an unassisted case. Step 3: Flow Rate Targeting for Individual Networks. After identification of the overall minimum flow rates for the IPRCN, the minimum fresh hydrogen and purge flow rates

6.78 0.00 (limiting pinch) 77.93 96.64 34.06 0.02 465.66

60.00

415.80


FH1 ) 125.21

623.80

-2 495.00 415.80

∆Lm (mol/s)

266.86

60.00 2 495.00

FC,m (mol/s)

120.00

-100.00

4.00

19.39

ΣiFSRi - ΣjFSKj (mol/s)

7 318.62

-677.38

-1 354.76

-330.88

-992.64

FP ) 126.52

8 856.42

11 342.59 10 379.77 9 042.81 7 324.05 2 713.90 2 347.40 992.64 0.00 (secondary pinch) 8856.42

within each network are now identified. As described earlier, unassisted integration occurs when the resource is recovered within the pinch region of the two hydrogen networks, i.e., between 5 and 30 mol % for example 1. Hence, purge streams that exist within this region are used as cross-plant streams to be integrated with the sink in another network for maximum hydrogen recovery. This includes purge streams P1 (5 mol %) and P2 (15 mol %) from network A as well as P3 (30 mol %) from network B. Note that in conventional pinch analysis, recovery of materials across the pinch is forbidden, as it violates thermodynamic (driving force) principles. However, this restriction is relaxed in IPRCN, where sources in the LPR of a network may be used in the HPR of another network, as long as a driving force exists.46 In essence, a network which possesses a pinch of higher purity is regarded to be “cleaner” as compared to a network with lower purity pinch. This means that excess source(s) from the LPR of the former may be used in the HPR of the latter. It is thus proposed that the targeting for the individual networks begins with the cleanest network, which is then followed by the next cleanest network. Note that this sequence applies to all IPRCN cases with two or more individual networks. For example 1, network A possesses a pinch concentration of the highest purity (5 mol %) between the two networks, and hence its flow rate targeting will be performed first. Note that network A will not receive any cross-plant stream from network B since the former has a relatively purer pinch concentration. This renders its fresh hydrogen flow rate unchanged at 125.21 mol/s (FH1). In contrast, the previously determined purge flow rate of 90.96 mol/s may be reduced after its purge stream P1 (5 mol %) and P2 (15 mol %) are used as cross-plant streams in network

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Figure 2. IPRCN with the unassisted integration scheme for example 1 (flow rate in moles/second; quality in mole %, given in parentheses).

B. The revised purge flow rate for network A will be determined later once the minimum flow rates for network B are obtained. Next, flow rate targeting is carried out for network B. Since this network possesses a pinch concentration of 30 mol %, both P1 (5 mol %) and P2 (15 mol %) from network A can be used as cross-plant streams for maximum hydrogen recovery (apart from the fresh hydrogen source). Since network B is now served by three sources (i.e., P1, P2 and fresh hydrogen), a multiplesource targeting procedure14 is performed to determine the minimum flow rates for these sources. Note that this step is essential as it will avoid sending excess cross-plant stream, which will incur higher operating (compression duty) and capital costs. This is conceptually similar to the IPWI case where crossplant streams are minimized to reduce piping cost.46 The targeting procedure determines that the entirety of the purge streams of P1 (30.96 mol/s) and P2 (60 mol/s) are consumed by network B, which then determines its minimum fresh hydrogen (FH2) and purge (FP) flow rates of 201.86 and 126.52 mol/s, respectively (GCA is not shown for brevity). At this point, it may be calculated that the purge flow rate of network A is totally eliminated. An IPRCN that achieves these targets is shown in Figure 2, designed using the nearest neighbor algorithm.10 Example 2: IPWI. Example 2 demonstrates a hypothetical example taken from Bandyopadhyay et al.,47 which analyze an IPWI case between two water networks.5,63 For this case, only a single fresh water source at 0 ppm is used. The limiting water data is shown in Table 7. In this example, a recently developed graphical targeting tool known as the MSCC62 is used for illustration. The MSCC is an extension of the water surplus diagram6 but eliminates its tedious trial-and-error steps. To construct the MSCC, water sink and source composite curVes are first plotted on a concentration versus cumulative flow rate

Table 7. Limiting Water Data for Example 2 water network k

A63

B

5

sinks j

flow rate flow rate concentration FSKj concentration (t/h) CSKj (ppm) sources i FSRi (t/h) CSRi (ppm)

SK1 SK2 SK3 SK4

50 100 80 70

20 50 100 200

SR1 SR2 SR3 SR4

50 100 70 60

50 100 150 250

SK5 SK6 SK7 SK8

20 100 40 10

0 50 50 400

SR5 SR6 SR7 SR8

20 100 40 10

100 100 800 800

Σj FSKj

470

Σi FSRi

450

diagram. The area enclosed by the two composite curves represents the pure water surplus/deficit load at each concentration level. Next, the cumulative water surplus/deficit load diagram is constructed using the earlier identified pure water surplus/deficit load. Subsequently, an interVal flow rate diagram is plotted by converting the cumulative water surplus/deficit load to the interval fresh water flow rates at each concentration level. Finally, the identified interval fresh water flow rates are used to construct the MSCC, which consists of the water surplus/ deficit composite curVes. To determine the minimum water flow rates, the water surplus composite curve is shifted to the right until it is entirely to the right and above the sink composite curve. A detailed procedure on the construction of the MSCC is found in Saw et al.62 With the utilization of the MSCC, the minimum fresh water (FFW) and wastewater (FWW) flow rates for network A are identified as 70 and 50 t/h, respectively, with a pinch concentration at 150 ppm (Figure 3a). To identify the wastewater streams generated from network A, MSCC is constructed for the LPR


6445

Table 8. New Limiting Data with Unassisted Integration Scheme for Example 2 water network k sinks j

A

B

Figure 3. Flow rate targeting with MSCC for network A in example 2.

(Figure 3b). Note that the fresh water segment for water flow rate targeting is replaced by the pinch-causing source (SR3) at 150 ppm for this case. Saw et al.62 reported that 60 t/h of the pinch-causing source is allocated to LPR, and the minimum pinch flow rate (FPINCH) in the LPR is reported as 35 t/h (Figure 3b). Hence, 25 t/h () 60 - 35 t/h) of wastewater stream (W1, 150 ppm) is emitted from SR3, while 25 t/h wastewater stream (W2, 250 ppm) is emitted from SR4.62 Applying the WSCC for network B determines that both the minimum fresh water and wastewater flow rates are 90 t/h, with the pinch concentration located at 100 ppm (results are not shown for brevity). This defines the pinch region that is bounded within the concentration range of 100-150 ppm. Three wastewater streams are also identified, i.e., W3, W4, and W5. The new limiting water data for the IPRCN are summarized in Table 8 (step 1 of the IPRCN targeting algorithm). In step 2 of the targeting algorithm, MSCC is used to determine the minimum water flow rates of the IPRCN and is shown in Figure 4. Note that the latter is constructed using the new limiting water data in Table 8. As shown, the overall minimum fresh water and wastewater flow rates are identified as 155 and 135 t/h, respectively. The pinch concentration is observed at 100 ppm, coinciding with the original pinch concentration of network B. In the third step of the targeting algorithm, flow rate targeting is carried out for the individual networks, starting with network B which has lower pinch concentration (100 ppm). Its fresh water flow rate remains unchanged at 90 t/h, since it does not receive any cross-plant flow rate. Targeting is then carried out for network A with a higher pinch concentration (150 ppm). Since the pinch region is found between 100 and 150 ppm, only wastewater streams W1 (network A) and W3 (network B) fall within this concentration range. Wastewater stream W3 is then used as the cross-plant stream to reduce fresh water consumption for network A. The targeting step next determines that only 15

flow rate FSKj concentration (t/h) CSKj (ppm)

sink in HPR SK1 50.00 SK2 100.00 SK3 80.00

sink in HPR SK1 20.00 SK2 100.00 SK3 40.00

sources i

flow rate concentration FSRi CSRi (t/h) (ppm)

source in HPR SR1 50.00 SR2 100.00 SR3 10.00 wastewater in PR W1 25.00 W2 25.00

20 50 100

0 50 50

50 100 150 150 250

source in HPR SR5 SR6

20.00 50.00

100 100

wastewater in LPR W3 W4 W5

44.29 40.00 5.71

100 800 800

t/h of this wastewater stream is needed in network A62 (targeting result is not shown for brevity). By incorporation of 15 t/h of W3 along with the original limiting water data of network A, it is then determined that its fresh water and wastewater targets are 65 and 60 t/h, respectively. Besides, since 15 t/h of W3 is utilized as a cross-plant stream in network A, the wastewater flow rate of network B is hence reduced to 75 t/h () 90 - 15 t/h). When flow rate targeting is conducted by taking all sources and sinks as a single network, the actual minimum flow rates are also determined as 155 and 135 t/h, respectively. Similar to example 1, the unassisted scheme is sufficient to achieve the maximum water recovery for this example. Figure 5 is one of the possible IPRCN for example 2 designed using the nearest neighbor algorithm.10 Example 3: Property-Based IPRCN. In example 3, the new targeting algorithm is applied to a property-based IPRCN problem. This example is adopted from Chew and Foo,51 which involves water integration between two wafer fabrication plants in an EIP. The limiting water data is given in Table 9. As shown, both networks have several water sinks and sources that may be considered for reuse/recycle. Resistivity (R) is taken as the main characteristic in evaluating the water reuse/recycle opportunity between both networks. An external source of ultrapure water (UPW) with 18 MΩ cm is used when process water sources are insufficient for use in the sinks. The mixing rule for resistivity is given as follows:64 1 ) j R

xi

∑R i

(1)

i

For this example, the MRPD developed by Kazantzi and ElHalwagi38 for property integration is used. In the MRPD, the sink and source composite curves are plotted on a cumulative impurity load versus cumulative flow rate diagram. Note that the individual segments of the composite curves are arranged in an ascending order of their slope, in which the latter represent the property operators of the individual sinks/sources. For fresh resource with nonzero quality index, a locus is added, with its slope being the operator of the fresh resource. The source composite curve is then moved along the locus until it just touches the sink composite curve and is below and to the right of the sink composite curve.

6446


Figure 4. Flowrate targeting with MSCC for IPRCN in example 2. Table 9. Limiting Water Data for Example 3 water network k

A40

B64

Figure 5. IPRCN with the unassisted scheme for example 2 (flow rate in tons/hour; quality in parts per million, given in parentheses).

The previously described IPRCN flow rate targeting algorithm is next carried out for example 3. In the first step of the unassisted scheme, MRPD is conducted separately for both networks A and B, resulting in the MRPDs for the individual networks as in Figure 6. As shown, the minimum UPW (FUPW) and wastewater (FWW) flow rates for network A are targeted as 1516.47 and 1096.47 t/h, respectively, with the pinch occurring at the property operator level of 0.5 (MΩ cm)-1. Note that one may easily determine the extent of water recovery from the overlap of the two composite curves (designated by FRC). Meanwhile, network B has the minimum UPW (FUPW) and wastewater (FWW) flow rates of 221.04 and 334.28 t/h,

process

sinks wet (SK1) litography (SK2) CMP (SK3) Etc (SK4) sources wet I (SR1) wet II (SR2) litography (SR3) CMP I (SR4) CMP II (SR5) Etc (SR6) sinks wafer Fab (SK5) CMP (SK6) sources 50% spent (SR7) 100% spent (SR8)

heavy metal flow resistivity, operator, concentration -1 rate (t/h) R (MΩ cm) Ψ (MΩ cm) (ppm)

500.00 450.00

7.0 8.0

0.142 9 0.125 0

700.00 350.00

10.0 5.0

0.100 0 0.200 0

250.00 200.00 350.00

1.0 2.0 3.0

1.000 0 0.500 0 0.333 3

5.0 4.5 5.0

300.00 200.00 280.00

0.1 2.0 0.5

10.000 0 0.500 0 2.000 0

10.0 4.5 5.0

182.00

16.0

0.062 5

159.00

10.0

0.100 0

227.12

8.0

0.125 0

5.0

227.12

2.0

0.500 0

11.0

respectively, with the pinch at 0.125 (MΩ cm)-1. Separate MRPDs (not shown) are then plotted for the LPR to identify the individual wastewater streams for each network, following the waste targeting procedure proposed by Ng et al.,26 resulting in the new limiting water data in Table 10. The pinch region is then defined by the two property operator levels, i.e., 0.125-0.5 (MΩ cm)-1. In step 2 of the unassisted algorithm, MRPD is plotted using the new limiting data, which then locate the overall minimum UPW (FUPW) and wastewater (FWW) flow rate targets as 1647.09 and 1340.34 t/h, respectively (Figure 7). A pinch is observed at the higher property operator levels of 0.5 (MΩ cm)-1, which coincides with the original pinch point for network A. Flow rate targeting is then carried out for the individual networks in step 3 of the targeting procedure. First, the individual wastewater streams that fall within the pinch region are identified, i.e., W1 and W2 of network A as well as W6 and W7 of network B. Flow rate targeting is then carried out for network B, which possesses the pinch operator of a higher quality level. Since network B does not receive any additional water source, its fresh water flow rate remains unchanged at


6447

Figure 6. Flowrate targeting with MRPD for the individual networks in example 3: (a) network A and (b) network B. Table 10. New Limiting Data with Unassisted Integration Scheme for Example 3 flow flow water rateFSKj operator, Ψ rateFSRi operator, -1 network k sinks j (t/h) (MΩ cm) sources i (t/h) Ψ (MΩ cm)-1

A

B

sinks in HPR SK1 SK2 SK3 SK4

500.00 450.00 700.00 350.00

sinks in HPR SK5 182.00 SK6 159.00

0.142 9 0.125 0 0.100 0 0.200 0

0.062 5 0.100 0

sources in HPR SR2 SR3

133.53 350.00

0.500 0 0.333 3

wastewater in LPR W1 W2 W3 W4 W5

66.47 200.00 250.00 280.00 300.00

0.500 0 0.500 0 1.000 0 2.000 0 10.000 0

sources in HPR SR7

119.96

0.125 0

wastewater in LPR W6 107.16 W7 227.12

Flow rate targeting is then carried out for network A. A minimum cross-plant stream of 107.16 t/h (W6) is first identified using the multiple-source targeting procedure65 in network A, along with the UPW and wastewater flow rates as 1426.06 and 1113.22 t/h, respectively (result not shown). It is then determined that wastewater flow rate in network B is reduced to 227.12 t/h () 334.28 -107.16 t/h) after exporting 107.16 t/h of W6 to network A. Figure 8 shows one of the possible IPRCN for example 3, which is also designed using the nearest neighbor algorithm.10 It is worth noting that these targets are identical to the single-network target. This resembles the same situation as in the previous cases, where the unassisted scheme is sufficient to achieve the minimum overall flow rate targets. Finally, note that even though the targeting procedure is demonstrated for IPRCN that involves two networks in the above examples, the same procedure is also applicable for any IPRCN that involves more than two networks. Pinch Shifting in IPRCN

0.125 0 0.500 0

221.04 t/h, while its revised wastewater flow rate will be determined at a later stage.

Figure 7. Flowrate targeting with MRPD for IPRCN in example 3.

As presented earlier, the targeting algorithm for IPRCN is conducted in three steps, i.e., identification of limiting data for IPRCN, targeting for minimum flow rates for IPRCN, and finally flow rate targeting for individual networks. Note that within the third step, the minimum cross-plant stream flow rates for the IPRCN are also determined, which is normally carried out using the multiple-source targeting procedure.14,65 As discussed in the original work of multiple-source targeting, pinch shifting from its original location is always avoided, as this indicates that the excess source is fed to the network, which is generally not desired.14,65 This is the same case for IPRCN. When the individual pinch is shifted upon receiving cross-plant streams, this means that the excess flow rate of the cross-plant streams has been used. Hence, pinch shifting is usually avoided in the IPRCN problems. Apart from not yielding any further material recovery, sending excess cross-plant streams leads to higher capital (e.g., piping) and utility (e.g., compression duty for utility gas) costs.46 However, there are exceptional cases where pinch shifting within the individual network may be allowed. This relaxation is necessary so that the targeted minimum flow rates in the individual networks (step 3) will match the overall minimum flow rate targets (step 2). In general, when a network receives sufficient cross-plant streams, new pinch point(s) will occur at the quality level of

6448


Figure 8. IPRCN with the unassisted integration scheme for example 3 (flow rate in tons/hour; resistivity in megaohms centimeters, given in parentheses). Table 11. Limiting Water Data for Example 4 flow flow concentration rate concentration water rate network k sinks j FSKj (t/h) CSKj (ppm) sources i FSRi (t/h) CSRi (ppm)

66

A

B67

C16

SK1 SK2 SK3 SK4 SK5

182.35 45.55 138.70 92.55 105.00

0 10 10 10 87

SR1 SR2 SR3 SR4 SR5

105.00 182.35 138.70 92.55 45.55

17 44 49 83 115

SK6 SK7

200.00 80.00

20 75

SR6 SR7 SR8

150.00 60.00 100.00

10 50 85

SK8 SK9 SK10 SK11 SK12

40.00 50.00 30.00 60.00 40.00

0 100 100 300 400

SR9 SR10 SR11 SR12 SR13

40.00 50.00 30.00 60.00 40.00

200 200 400 400 600

ΣiFSRi

1 094.15

ΣjFSKj 1 064.15

Figure 9. Generation of the new cross-plant stream due to pinch-shifting.

the cross-plant streams, while the original pinch remains unchanged. However, there are also cases where the new pinch point(s) is generated at a quality level higher than that of the cross-plant stream. Figure 9 is adapted from Figure 1 for illustration. As shown, a new limiting pinch is generated in network B (at quality level m ) 1 above the pinch region) after receiving cross-plant streams from network A within the pinch region (at quality levels m ) 2 and 3). For this kind of situation, the total minimum fresh resource flow rates across both networks A and B (as determined in step 3 of the IPRCN targeting algorithm) will not match the overall minimum flow rate targets (as determined in step 2). To rectify this problem, step 3 of the IPRCN targeting algorithm is revised as follows. The revised step involves the extraction of the pinch-causing source from the newly formed limiting pinch of network B and to be used as the new cross-plant stream for network A. In cases where there is more than one network that may receive this new cross-plant stream, the latter should be sent to a network with a pinch of higher quality. Doing this ensures the maximum material recovery within the IPRCN. For the case in Figure 9, since the HPR of network B (m ) 1 - 4) is self-sufficient in terms of flow rate (i.e., no excess source flow rate), extraction of the new cross-plant stream will thus cause a flow rate deficit

in this region. Hence, a new supplementary source is required to restore the flow rate balance in this region. This may be found in the LPR of the overall network. Since the resource is normally in excess in the LPR, extracting the supplementary source from this region will not cause a flow rate deficit in the overall network. In most cases, the supplementary source normally coincides with one of the existing cross-plant streams. Hence, higher cross-plant flow rates are normally observed for IPRCN with a supplementary source. To illustrate this revised procedure, example 4 that involves an IPWI of three water networks16,66,67 is used. For this example, WCA is utilized as the flow rate targeting tool. Example 4: IPWI. Limiting water data for example 4 are shown in Table 11. After step 1 of the targeting algorithm, the new limiting water data for the unassisted integration scheme are shown in Table 12. Step 1 also determine that the pinch region for this case is found between 44 (network A) and 200 ppm (network C). Table 13 shows the WCA targeting for the IPRCN using the limiting data in Table 12 (step 2). As shown, the minimum flow rate targets for fresh water and wastewater are determined as 364.99 and 394.99 t/h, respectively. The overall pinch concentration is found at 44 ppm, which coincides with the original pinch concentration of network A. The overall minimum flow rate targets are identical to the single-network targets, which indicate that the problem is an unassisted case.

Ind. Eng. Chem. Res., Vol. 49, No. 14, 2010 Table 12. New Limiting Data with Unassisted Integration Scheme for Example 4 flow water rate FSKj concentration (t/h) CSKj (ppm) network k sinks j

A

B

C

sink in HPR SK1 182.35 SK2 45.55 SK3 138.70 SK4 92.55

sink in HPR SK6 200.00 SK7 80.00

sink in HPR SK8 SK9 SK10

40.00 50.00 30.00

0 10 10 10

20 75

0 100 100

sources i

source in HPR SR1 SR2

ΣjFSKj (t/h)

ΣiFSRi ΣiFSRi (t/h) ΣjFSKj (t/h)

222.35

-222.35

10

276.80 150.00

-126.80

17 20

105.00

105.00 22.34

44

182.35

wastewater in LPR W1 160.01 W2 138.70 W3 0.67 W4 32.43 source in HPR SR6 150.00 SR7 60.00 SR8 64.71 wastewater in LPR W5 35.29

17 44

44 49 83 115

10 50 85 85

source in HPR SR9

40.00

200

wastewater in LPR W6 W7 W8

20.00 20.00 40.00

200 400 600

FC,m (t/h)

∆Lm (kg/h)

138.70

138.70

50

60.00

60.00

75 83

0.67

85 100 115 200 400 600 1 000 000

-80.00

80.00

100.00

32.43 60.00 20.00 40.00

1 426.41

15.84

110.89

cumulative ∆Lm (kg/h)

120.84

362.52

-79.16

-1 899.82

103.19

515.95

241.89

241.89

301.89

7547.27

221.89

1775.13

222.56

445.12

322.56

4 838.41

242.56

3 638.41

274.99

23 374.23

334.99

66 998.18

354.99

70 998.18

1 426.41 1 537.30

0.00 (PINCH) 515.95

8 305.12 10 080.25

100.00

10 525.37 15 363.78

32.43

19 002.19

60.00

42 376.42

20.00 40.00

1 899.82

757.85

0.67

-80.00

80.00

142.64

182.35

49

ΣjFSKj (t/h)

ΣiFSRi (t/h)

0

ΣiFSRi s ΣjFSKj (t/h)

FC,m (t/h)

∆Lm (kg/h)


0

10

105.00 -200.00

200.00

Cm (ppm)

FFW ) 0.00

FFW ) 364.99 0

Table 14. Flow Rate Targeting with WCA for Network B, after Integration with Network A (Example 4)

flow rate FSRi concentration (t/h) CSRi (ppm)

Table 13. Flow Rate Targeting with WCA for IPRCN in Example 4 Cm (ppm)

6449

109 374.60 FWW ) 394 753 915 394.99

180 372.79 394 934 287

After the third step in the unassisted integration scheme, the minimum fresh water flow rates for networks A, B, and C are targeted as 331.81, 0, and 40 t/h, respectively, while their wastewater targets are identified as 269.55, 40.98, and 91.28 t/h, respectively (WCA is not shown for brevity). Among the wastewater streams that are found within the pinch region (i.e.,

20

150

10.98 (W1)

10.98

50

60

60

85 1 000 000

0.00

150.00

1 500.00

-50.00

-1 199.99

-200

200

44

75

0.00 150

-80

80 100

100

0.00 (limiting pinch) 1 500.01 300.01

-39.02

-234.15

20.98

524.38

-59.02

-590.25

FWW ) 40.98

40 971 811

65.86 590.25 0.00 (secondary pinch) 40 971 811

W1, W2, W3, W4, W5, W6), only W1 from network A is needed as the cross-plant stream to be sent to networks B and C. The minimum flow rates for these streams are determined using the targeting procedure14 as 10.98 and 51.28 t/h, respectively. However, the sum of the total fresh water and wastewater flow rates of the individual networks are identified as 371.81 and 401.81 t/h, respectively, which are higher than that of the overall flow rate targets determined in step 2 (i.e., 364.99 and 394.99 t/h). The revised step 3 is used here to overcome the problem. As mentioned earlier, cross-plant stream W1 is transferred from network A to both networks B and C. However, only network B experiences pinch-shifting upon receiving W1, while network C has its pinch point unchanged at 200 ppm. This indicates that the new cross-plant stream will only be generated in network B alone (to be used in network A, with a pinch of higher quality). Table 14 shows the flow rate targeting for network B after integration with network A (step 3). As shown, a double-pinch problem occurs here, where a new limiting pinch is generated at 10 ppm, while the original pinch remains at 85 ppm. Hence, the new cross-plant stream for network A is to be extracted from the pinch-causing source (SR6) at the new limiting pinch at 10 ppm. As shown in Table 14, 150 t/h of the pinch-causing source SR6 (observed from the FC,m column in the interval just lower than the limiting pinch) is allocated to the region between pinches in network B, i.e., 10-85 ppm. In this region (between pinches), the entire SR6 along with 10.98 t/h of the cross-plant stream (W1, identified in step 3), 60 t/h of SR7, and 59.02 t/h of the allocated flow rate of SR8 (observed from the FC,m column in the interval just above the secondary pinch) are consumed by sinks SK6 (200 t/h, 20 ppm) and SK7 (80 t/h, 75 ppm). In other words, the sinks and sources in this region between pinches are in flow rate balance. Hence, if the new cross-plant stream is to be extracted from SR6, this region will experience flow rate deficit. After the proposed strategy, a supplementary source found in the LPR of the overall network may be used to restore the flow rate balance in this region. As mentioned earlier, the overall pinch concentration for the IPRCN is found at 44 ppm. From Table 12, it is observed that all waste streams in network A belong to the LPR of the overall network and hence may be utilized as the supplementary source for network B. In this example, since W1 is an existing crossplant stream between networks A and B (with excess flow rate, see Table 12), it is best to make the most use of this stream,

6450


in the LPR following the waste targeting procedure of Ng et al.,26 with the results shown in Table 15. As shown, the flow rate of the supplementary source W1 is first set at its maximum available flow rate (i.e., 160.01 t/h, see Table 12). It is then determined that the minimum pinch flow rate (FPINCH) of 141.18 t/h (at 10 ppm) is needed in the LPR of network B. Next, multiple-source targeting14 is performed to determine the minimum flow rate of W1, by fixing the minimum pinch flow rate at 141.18 t/h. This determines that 58.82 t/h of W1 is required in network B (WCA is omitted for brevity). Having identified the minimum pinch flow rate also indicates that the remaining SR6 flow rate, i.e., 8.82 t/h () 160.01 - 141.18 t/h) can be extracted as the new cross-plant stream for network A. Tables 16 and 17 show the flow rate targeting results for networks A and B, respectively, after including the previously determined new cross-plant streams. Note that the minimum fresh water target for network A is now reduced to 324.99 t/h, while that for network B remains unchanged at 0 t/h. The wastewater flow rates are also determined as 223.71 and 80 t/h for networks A and B, respectively. On the other hand, the minimum fresh water and wastewater flow rates for network C remain unchanged at 40 and 91.28 t/h, respectively. Summation of the minimum flow rates for all individual networks gives the total minimum fresh water and wastewater targets of 364.99 and 394.99 t/h, respectively, which are identical to the singlenetwork targets. Figure 10 shows the simplified cascade diagram for example 4 with the revised step 3. As shown, 8.82 t/h of new cross-plant stream is generated from the pinch-causing source (SR6) at the limiting pinch of network B (10 ppm) for

Table 15. Minimum Pinch Flow Rate Targeting with WCA for Network B in Example 4 (with Maximum Flow Rate of W1) Cm (ppm)

ΣiFSRi ΣjFSKj (t/h)

ΣjFSKj ΣiFSRi (t/h) (t/h)

FC,m (t/h)

∆Lm (kg/h)


FPINCH ) 141.18 10

0

20

-200

200

44

160.01 160.01 (W1)

50

60

75

1 411.76

-58.82

-1 411.76

-80 100

101.19

607.12

161.19

4 029.66

81.19

811.86

181.19

181 171 070

607.12 4 636.78

100

1 000 000

1 411.76 0.00

60

80

85

141.18

5 448.65

0

181 176 518

i.e., by maximizing its cross-plant flow rate, before exploring other new cross-plant streams. This will reduce the overall network complexity of the IPRCN. Making use of the supplementary source from W1 enables the unused SR6 in network B to be extracted as a new cross-plant stream for network A. To achieve this, the minimum pinch flow rate (at 10 ppm) for the region between pinches of network B is to be determined. Detailed targeting steps to identify these flow rates are discussed next. To determine the minimum pinch flow rate to the region between pinches in network B, flow rate targeting is performed

Table 16. Flow Rate Targeting with WCA for Network A in Example 4 (with Revised Step 3) Cm (ppm)

ΣjFSKj (t/h)

0

182.35

10

276.80

ΣiFSRi (t/h)

ΣiFSRi - ΣjFSKj (t/h) -182.35

17

8.82 (new cross-plant stream) 105.00

44

72.25

-267.98 105.00

138.70

138.70

83

92.55

92.55 -105.00

105.00

115

∆Lm (kg/h)

45.55

45.55


FFW ) 324.99 142.64

1 426.41

-125.34

-877.35

-20.34

-549.06

51.91

259.55

190.61

6 480.75

283.16

1 132.64

178.16

4 988.49

FWW ) 223.71

223 684 705

72.25

49

87

FC,m (t/h)

1 426.41 549.06 0.00 (PINCH) 259.55 6 740.31 7 872.95 12 861.44

1 000 000

223 697 567

Table 17. Flow Rate Targeting with WCA for Network B in Example 4 (with Revised Step 3) Cm (ppm)

ΣjFSKj (t/h)

ΣiFSRi (t/h)

ΣiFSRi - ΣjFSKj (t/h)

FC,m (t/h)

∆Lm (kg/h)


FFW ) 0.00 0

0 0.00

10 20

141.18 ) (150 - 8.82)

44

58.82 (W1)

50 75 85 1 000 000

-200.00

200

60

100

0.00 141.18

1 411.76

-58.82

-1 411.77

0.00

0.00

60.00

1 500.01

-20.00

-200.00

FWW ) 80.00

79 993 609

58.82 60.00 -80.00

80

0.00

141.18

100.00 0.00

(limiting pinch) 1 411.77 0.00 (secondary pinch) 0.00 (tertiary pinch) 1 500.01 1 300.02 79 994 909


6451

Figure 10. Simplified cascade diagram with the unassisted integration scheme for example 4 (with revised step 3).

Figure 11. IPRCN with the unassisted integration scheme for example 4, with revised step 3 (flow rate in tons/hour; quality in parts per million, given in parentheses).

use in network A, while 47.84 t/h of the supplementary source is extracted along with the original cross-plant stream W1 of 10.98 t/h (44 ppm; a total of 58.82 t/h) for use in network B. Besides, 51.28 t/h of cross-plant stream (W1) is also sent to network C from network B. Figure 11 shows one of the possible IPRCN for example 4, designed using the nearest neighbor algorithm.10 Source Interception and Waste Treatment Interception units are commonly used to improve the quality of the material source(s). The intercepted source(s) can either be further reused/recycled in the network (often known as regeneration) or sent for environmental discharge under specified quality limits. For the former case, the intercepted source(s) can either be consumed internally within an individual network or may be integrated with sinks in another network via IPRCN. Two examples are shown here to illustrate the concept.

In the following subsections, example 3 is first revisited to illustrate the incorporation of a fixed outlet quality type interception unit in an IPRCN case. The targeting procedure shall follow the work that is presented by Ng and co-workers.20,21 Next, example 2 is revisited by incorporating a fixed outlet quality type regeneration unit and a fixed removal ratio type waste treatment unit. The targeting for waste treatment shall follow another works of Ng et al.26,27 Because of space limitations, only a brief description of the procedure is outlined here. Readers are referred to the original works for the detailed steps. Example 3 Revisited. Two scenarios are analyzed here. In the first scenario, a centralized regeneration unit with a fixed resistivity value (Rout) of 9 MΩ cm is installed for both networks to purify water sources for further reuse/recycle. This is conceptually similar to the centralized utility hub presented by Chew and co-workers.49,50 Following the algebraic regeneration

6452


Table 18. Regeneration Flow Rate Targeting with WCA for Example 3 Revisited Ψm (MΩ cm)-1 ΣjFSKj (t/h)

ΣiFSRi (t/h)

ΣiFSRi - ΣjFSKj (t/h)

FC,m (t/h)

∆βm [(t/h) (MΩ cm)-1] cumulative ∆βm [(t/h) (MΩ cm)-1]

FUPW ) 331.05 0.055 6 0.062 5

0 -182

182

0.066 7 0.100 0

-859

859

149.05

0.000 6

149.05

0.005 0

0.002

450

0.142 9

500

0.003 -709.95

FReg ) 1 557.48

0.125

0.333 3

0.002 3

0

Cout ) 0.111 1

0.200 0

331.05

227.12

1557.48 -222.88 -500 -350

350

847.53

0.011 8

624.65

0.011 2

124.65

0.007 1

249.64

0.008 0.000 (limiting pinch) 0.012 0.023

-225.35 249.64

-0.007 9

FWW ) 24.29

-0.030 0 24 290

0.030 0.000 (secondary pinch)

1 000 000

targeting techniques of Ng et al.,20,21 the total minimum regeneration flow rate (FReg) for both networks is determined

as 1557.48 t/h, with the overall minimum UPW (FUPW) and wastewater (FWW) flow rates of 331.05 and 24.29 t/h, respec-

Figure 12. IPRCN with the shared interception unit for example 3 revisited (flow rate in tons/hour; property in megaohms centimeters, given in parentheses).

Figure 13. IPRCN with individual interception units for example 3 revisited (flow rate in tons/hour; property in megaohms centimeters, given in parentheses).

Ind. Eng. Chem. Res., Vol. 49, No. 14, 2010 Table 19. Regeneration Flow Rate Targeting with WCA for Example 2 Revisited Cm (ppm)

ΣjFSKj (t/h)

ΣiFSRi (t/h)

ΣiFSRi ΣjFSKj (t/h)

0

20

-20

20

50

-50

Cout ) 30

240

100

80

1 000 000

50 150.96

∆Lm (kg/h)

16.67

0.3 -0.3

169.04

0.3 0.0

135.71

2.7

-54.29

-2.7

FWW) 16.67

1670.3

-190 70.96


FFW ) 36.67

-33.33 FReg) 169.04

50

FC,m (t/h)

(limiting pinch) 2.7 0.0 (secondary pinch) 16 670.3

tively. These are shown in the cascade table in Table 18. Note that the impurity load (βm) in columns 6 and 7 is introduced for the property-based problem, replacing the impurity load for the concentration-based problem (as in Tables 2 and 3, etc.). Note also that the targeting algorithm makes use of the new limiting data in Table 10 rather than that shown in Table 9. The main reason for this is that the inherent water demand of

Figure 14. Minimum wastewater treatment flow rate targeting for example 2 (revisited).

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both networks is reduced after being fulfilled by a portion of the water sources. This eventually leads to smaller targets for fresh water, wastewater, and regeneration flow rates. In order to determine the minimum flow rate targets for UPW, regeneration, and cross-plant streams for each individual network, step 3 of the IPRCN targeting scheme is to be followed. First, the pinch region is identified by the individual pinches of both networks, i.e., 0.125-0.5 (MΩ cm)-1. Individual waste streams that are present within the pinch region are then identified, i.e., W1 of 66.47 t/h and W2 of 200 t/h (0.5 (MΩ cm)-1) from SR2 and SR5, respectively, in network A and W6 of 107.16 t/h (0.125 (MΩ cm)-1) from water source SR7 in network B. Targeting is first carried out for network B, which possesses the pinch point of higher quality, i.e., 0.125 (MΩ cm)-1. With the use of the multiple-source targeting procedure,65 the minimum regeneration flow rate (FReg) needed for network B is determined as 149.95 t/h, while its UPW is further reduced to 191.05 t/h (from 221.04 t/h in the reuse/recycle scheme, Figure 6). Targeting is then carried out for network A (with a pinch of lower quality) to identify the minimum UPW, regenerated flow rate, and cross-plant streams. These values correspond to 140, 1407.52, and 227.12 t/h, respectively. Figure 12 shows the IPRCN that incorporates the interception units, designed using the nearest neighbor algorithm.10 As shown, the wastewater in network B is completely eliminated after 227.12 t/h of SR8 is sent for regeneration. Besides, upon the incorporation of the regeneration unit, source SR7 is no longer being reused/recycled in the local network (as in Figure 8) but is entirely used as a cross-plant stream for network A. In a second scenario, two individual interception units of different performance are installed for each network. The interception unit of network A has a fixed resistivity value (Rout,A) of 8 MΩ cm, while that of network B with 11 MΩ cm (Rout,B). The targeting steps remain the same as in the previous case. This leads to the minimum UPW, regeneration, and wastewater flow rates as 252.00, 1394.42, and 16.72 t/h for network A as well as 146.25, 152.34, and 74.78 t/h for network B (results not shown for brevity). An IPRCN that achieves these targets is shown in Figure 13, which is designed using the nearest neighbor algorithm.10

Figure 15. IPRCN with the water regeneration and wastewater treatment units for example 2 revisited (flow rate in tons/hour; quality in parts per million, given in parentheses).

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Example 2 Revisited. For this example, a fixed outlet concentration type regeneration unit is assumed, with Cout ) 30 ppm. In addition, a waste treatment unit with a removal ratio (RR) of 0.9 is used to purify the wastewater streams before it is discharged to the environment. For the latter, the targeting procedure shall follow the work of Ng et al.26,27 As shown in Table 19, the minimum regeneration flow rate target (FReg) is determined as 169.04 t/h, with the minimum fresh water (FFW) and wastewater flow rates (FWW) of 36.67 and 16.67 t/h, respectively. Note that the targeting procedure to determine the individual network flow rates is the same as that presented in example 3 but is omitted here for brevity. It is then observed from Table 19 that the final wastewater discharge of 16.67 t/h is emitted at 100 ppm (only the allocated flow rate of the pinch-causing source is observed below the secondary pinch). Next, an interception unit of RR ) 0.9 is used for wastewater treatment before it is discharged to the environment. Assuming an allowable discharge limit, CD, of 20 ppm, the minimum impurity load (∆mR) to be removed from the wastewater is determined as 1.33 kg/h, as shown in Figure 14. For the treatment unit of RR ) 0.9, the total impurity load to be fed to the treatment unit (∆mF) is determined as 1.48 kg/ h, which corresponds to the minimum treatment flow rate (FT) of 14.81 t/h and a bypass flow rate (FB) of 1.86 t/h (see Figure 14). Figure 15 shows the IPRCN that incorporates the interception units, designed using the nearest neighbor algorithm.10 It is interesting to note that when the regeneration unit is considered, the original cross-plant pipeline connecting SR6 and SK3 (Figure 5) is no longer needed. This resulting network simplification reduces capital costs for water reuse/recycle. Conclusions A new three-step flow rate targeting algorithm for IPRCN is presented. This approach is based on the principle of the unassisted integration scheme, where cross-plant streams are found within the pinch region bounded by the pinch points of the individual networks. Although in all the examples shown, the resulting overall flow rate targets were identical to those found if the networks were treated as a single network, there may still be cases where the unassisted integration scheme does not achieve the single-network targets. For these latter cases, the assisted integration scheme is needed, which will be discussed in part 2 of the series.61 The proposed algorithm is a generic algorithm that can be applied in conjunction with any established pinch-based targeting techniques. Furthermore, the algorithm is also applicable in problems involving both concentration- and property-based integration. For problems involving source interception and waste treatment, it has also been shown that one should make use of the new limiting data (as identified via the new targeting algorithm) in the synthesis of the optimum IPRCN. Acknowledgment The financial support from University of Nottingham through New Researcher Fund (NRF Grant 3822/A2RBR9) and Research Studentship is gratefully acknowledged. Funding from the Ministry of Science, Technology and Innovation (MOSTI) Malaysia through Science Fund (Grant 03-02-12-SF0018) is deeply appreciated. Literature Cited (1) World Water Assessment Programme. The United Nations World Water DeVelopment Report 3: Water in a Changing World, UNESCO, Paris and Earthscan, London, 2009.

(2) Organisation for Economic Co-operation and Development (OECD). OECD EnVironmental Outlook to 2030, Paris, 2008. (3) Rosegrant, M. W.; Cai, X.; Cline, S. A. Global water outlook to 2025-AVerting an impending crisis. International Food Policy Research Institute and International Water Management Institute: Washington, DC, 2002. (4) UNEP Year Book. United Nations Environment Programme (2009). www.unep.org/geo/yearbook/yb2009/ Accessed on April 22, 2010. (5) Wang, Y. P.; Smith, R. Wastewater minimisation. Chem. Eng. Sci. 1994, 49 (7)), 981–1006. (6) Hallale, N. A new graphical targeting method for water minimisation. AdV. EnViron. Res. 2002, 6 (3), 377–390. (7) El-Halwagi, M. M.; Gabriel, F.; Harell, D. Rigorous graphical targeting for resource conservation via material recycle/reuse networks. Ind. Eng. Chem. Res. 2003, 42 (19), 4319–4328. (8) Manan, Z. A.; Tan, Y. L.; Foo, D. C. Y. Targeting the minimum water flowrate using water cascade analysis technique. AIChE J. 2004, 50 (12), 3169–3183. (9) Almutlaq, A. M.; Kazantzi, V.; El-Halwagi, M. M. An algebraic approach to targeting waste discharge and impure fresh usage via material recycle/reuse networks. Clean Technol. EnViron. Policy 2005, 7 (4), 294– 305. (10) Prakash, R.; Shenoy, U. V. Targeting and design of water networks for fixed flow rate and fixed contaminant load operations. Chem. Eng. Sci. 2005, 60 (1), 255–268. (11) Bandyopadhyay, S. Source composite curve for waste reduction. Chem. Eng. J. 2006, 125 (2), 99–110. (12) Bandyopadhyay, S.; Ghanekar, M. D.; Pillai, H. K. Process water management. Ind. Eng. Chem. Res. 2006, 45, 5287–5297. (13) Foo, D. C. Y.; Manan, Z. A.; Tan, Y. L. Use cascade analysis to optimize water networks. Chem. Eng. Prog. 2006, 102 (7), 45–52. (14) Foo, D. C. Y. Water cascade analysis for single and multiple impure fresh water feed. Chem. Eng. Res. Des. 2007, 85 (A8), 1169–1177. (15) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R. Automated Targeting Technique for Single-Component Resource Conservation Networks. Part 1: Direct Reuse/Recycle. Ind. Eng. Chem. Res. 2009, 48, 7637–7646. (16) Kuo, W. C. J.; Smith, R. Design of water-using system involving regeneration. Process Saf. EnViron. Prot. 1998, 76, 94–114. (17) Agrawal, V.; Shenoy, U. V. Unified conceptual approach to targeting and design of water and hydrogen networks. AIChE J. 2006, 52 (3), 1071–1081. (18) Bai, J.; Feng, X.; Deng, C. Graphical based optimization of singlecontaminant regeneration reuse water systems. Chem. Eng. Res. Des. 2007, 85 (A8), 1178–1187. (19) Feng, X.; Bai, J.; Zheng, X. On the use of graphical method to determine the targets of single-contaminant regeneration recycling water systems. Chem. Eng. Sci. 2007, 62 (8), 2127–2138. (20) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R.; Tan, Y. L. Ultimate flowrate targeting with regeneration placement. Chem. Eng. Res. Des. 2007, 85 (A9), 1253–1267. (21) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R.; Tan, Y. L. Extension of targeting procedure for “Ultimate flowrate targeting with regeneration placement” by Ng et al. Chem. Eng. Res. Des. 85 (A9) 1253-1267. Chem. Eng. Res. Des. 2008, 86 (10), 1182–1186. (22) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R. Automated targeting technique for single-component resource conservation networks. Part 2: Single-pass and partitioning waste-interception systems. Ind. Eng. Chem. Res. 2009, 48 (16), 7647–7661. (23) Tan, R. R.; Ng, D. K. S.; Foo, D. C. Y. Graphical approach to minimum flowrate targeting for partitioning water pretreatment unit. Chem. Eng. Res. Des. 2010, 88 (4), 393–402. (24) Wang, Y. P.; Smith, R. Design of distributed effluent treatment systems. Chem. Eng. Sci. 1994, 49 (18), 3127–3145. (25) Kuo, W. C. J.; Smith, R. Effluent treatment system design. Chem. Eng. Sci. 1997, 52 (23), 4273–4290. (26) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R. Targeting for total water networksPart 1: Waste stream identification. Ind. Eng. Chem. Res. 2007, 46 (26), 9107–9113. (27) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R. Targeting for total water networksPart 2: Waste treatment targeting and interactions with water system elements. Ind. Eng. Chem. Res. 2007, 46 (26), 9114–9125. (28) Bandyopadhyay, S. Targeting minimum waste treatment flow rate. Chem. Eng. J. 2009, 152, 367–375. (29) Bagajewicz, M. A review of recent design procedures for water networks in refineries and process plants. Comput. Chem. Eng. 2000, 24 (9-10), 2093–2113. (30) Foo, D. C. Y. State-of-the-art review of pinch analysis techniques for water network synthesis. Ind. Eng. Chem. Res. 2009, 48 (11), 5125– 5159.

Ind. Eng. Chem. Res., Vol. 49, No. 14, 2010 (31) Towler, G. P.; Mann, R.; Serriere, A. J. L.; Gabaude, C. M. D. Refinery hydrogen management: Cost analysis of chemically-integrated facilities. Ind. Eng. Chem. Res. 1996, 35 (7), 2378–2388. (32) Alves, J. J.; Towler, G. P. Analysis of refinery hydrogen distribution systems. Ind. Eng. Chem. Res. 2002, 41 (23), 5759–5769. (33) Zhao, Z.; Liu, G.; Feng, X. New graphical method for the integration of hydrogen distribution systems. Ind. Eng. Chem. Res. 2006, 45 (19), 6512– 6517. (34) Zhao, Z.; Liu, G.; Feng, X. The integration of the hydrogen distribution system with multiple impurities. Chem. Eng. Res. Des. 2007, 85 (A9), 1295–1304. (35) Foo, D. C. Y.; Manan, Z. A. Setting the minimum utility gas flowrate targets using cascade analysis technique. Ind. Eng. Chem. Res. 2006, 45 (17), 5986–5995. (36) Deng, C.; Feng, X. Conceptually Targeting Water and Hydrogen Networks with Multiple Resources, 8th World Congress of Chemical Engineering (8th WCCE), Montreal, Quebec, Canada, August 23-27, 2009. (37) Shelley, M. D.; El-Halwagi, M. M. Component-less design of recovery and allocation systems: A functionality-based clustering approach. Comput. Chem. Eng. 2000, 24 (9-10), 2081–2091. (38) Kazantzi, V.; El-Halwagi, M. M. Targeting material reuse via property integration. Chem. Eng. Prog. 2005, 101 (8), 28–37. (39) Foo, D. C. Y.; Kazantzi, V.; El-Halwagi, M. M.; Manan, Z. A. Surplus diagram and cascade analysis technique for targeting property-based material reuse network. Chem. Eng. Sci. 2006, 61 (8), 2626–2642. (40) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R.; Pau, C. H.; Tan, Y. L. Automated targeting for conventional and bilateral property-based resource conservation network. Chem. Eng. J. 2009, 149, 87–101. (41) Qin, X.; Gabriel, F.; Harell, D.; El-Halwagi, M. M. Algebraic techniques for property integration via componentless design. Ind. Eng. Chem. Res. 2004, 43, 3792-3798. (42) Ng, D. K. S.; Foo, D. C. Y.; Kazantzi, V. and El-Halwagi, M. M. (2006). Cascade Analysis Technique for Targeting Property-Based Material Reuse/Recycle Network, AIChE Annual Meeting, San Francisco, CA, November 12-17, 2006. (43) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R.; El-Halwagi, M. M. Automated Targeting Technique for Concentration and Property-Based Total Resource Conservation Network. Comput. Chem. Eng. 2010, 34 (5), 825– 845. (44) Olesen, S. G.; Polley, G. T. Dealing with plant geography and piping constraints in water network design. Process Saf. EnViron. Prot. 1996, 74 (4), 273–276. (45) Spriggs, D.; Lowe, E.; Watz, J.; El-Halwagi, M. M.; Lovelady, E. M. Design and development of eco-industrial parks, AIChE Spring Meeting, New Orleans, LA, April 25-29, 2004. (46) Foo, D. C. Y. Flowrate targeting for threshold problems and plantwide integration for water network synthesis. J. EnViron. Manage. 2007, 88 (2), 253–274. (47) Bandyopadhyay, S.; Sahu, G. C.; Foo, D. C. Y.; Tan, R. R. Segregated targeting for multiple resource networks using decomposition algorithm. AIChE J. 2010, 56 (5), 1235–1248. (48) Lovelady, E. M.; El-Halwagi, M. M.; Krishnagopalan, G. A. An integrated approach to the optimisation of water usage and discharge in pulp and paper plants. Int. J. EnViron. Pollut. 2007, 29 (1/2/3), 274–307. (49) Chew, I. M. L.; Tan, R.; Ng, D. K. S.; Foo, D. C. Y.; Majozi, T.; Gouws, J. Synthesis of direct and indirect interplant water network. Ind. Eng. Chem. Res. 2008, 47 (23), 9485–9496.

6455

(50) Lovelady, E. M.; El-Halwagi, M. M.; Chew, I. M. L.; Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R. A property-integration approach to the design and integration of eco-industrial parks, 7th International Conference on Foundations of Computer-Aided Process Design (FOCAPD), Breckenridge, CO, June 7-12, 2009. (51) Chew, I. M. L.; Foo, D. C. Y. Automated targeting for inter-plant water integration. Chem. Eng. J. 2009, 153 (1-3), 23–36. (52) Liao, Z. W.; Wu, J. T.; Jiang, B. B.; Wang, J. D.; Yang, Y. R. Design methodology for flexible multiple plant water networks. Ind. Eng. Chem. Res. 2007, 46 (14), 4954–4963. (53) Aviso, K. B.; Tan, R. R.; Culaba, A. B.; Cruz, J. B. Bi-level fuzzy optimization approach for water exchange in eco-industrial parks. Process Saf. EnViron. Prot. 2010, 88, 31–40. (54) Aviso, K. B.; Tan, R. R.; Culaba, A. B. Designing eco-industrial water exchange networks using fuzzy mathematical programming. Clean Technol. EnViron. Policy DOI: 10.1007/s10098-009-0252-1. (55) Aviso, K. B.; Tan, R. R.; Culaba, A. B.; Foo, D. C. Y.; Hallale, N. Fuzzy Optimization of Topologically Constrained Eco-Industrial Resource Conservation Networks with Incomplete Information. Eng. Optimiz. In press, DOI: 10.1080/0305215X.2010.486031. (56) Morton, R. J.; Linnhoff, B. Individual process improvements in the context of site-wide interactions, Inst. Chem. Eng., Annual Research Meeting, Bath, U.K., April 1984. (57) Ahmad, S.; Hui, D. C. W. Heat recovery between areas of integrity. Comput. Chem. Eng. 1991, 15 (12), 809–832. (58) Amidpour, M.; Polley, G. T. Application of problem decomposition in process integration. Chem. Eng. Res. Des. 1997, 75 (1), 53–63. (59) Rodera, H.; Bagajewicz, M. Targeting procedures for energy savings by heat integration across plants. AIChE J. 1999, 45 (8), 1721–1742. (60) Bagajewicz, M.; Rodera, H. Energy savings in the total site heat integration across many plants. Comput. Chem. Eng. 2000, 24 (2-7), 1237– 1242. (61) Chew, I. M. L.; Foo, D. C. Y.; Tan, R. R. Flowrate targeting algorithm for interplant resource conservation network. Part 2: Assisted integration scheme. Ind. Eng. Chem. Res. DOI: 10.1021/ie901804z. (62) Saw, S. Y.; Lee, L.; Lim, M. H.; Foo, D. C. Y.; Chew, I. M. L.; Tan, R. R.; Klemesˇ, J. An extended graphical targeting technique for direct reuse/recycle in concentration- and property-based resource conservation networks. Clean Technol. EnViron. Policy (in press, doi: 10.1007/s10098010-0305-5). (63) Polley, G. T.; Polley, H. L. Design better water networks. Chem. Eng. Prog. 2000, 96 (2), 47–52. (64) El-Halwagi, M. M. Process Integration; Elsevier: Amsterdam, The Netherlands, 2006. (65) Alwi, S. R. W.; Manan, Z. A. Targeting multiple water utilities using composite curves. Ind. Eng. Chem. Res. 2007, 46, 5968–5976. (66) Ku-Pineda, V.; Tan, R. R. Environmental performance optimization using process water integration and Sustainable Process Index. J. Cleaner Prod. 2006, 14 (18), 1586–1592. (67) Gabriel, F.; El-Halwagi, M. M. Simultaneous synthesis of waste interception and material reuse networks: Problem reformulation for global optimization. EnViron Prog. 2005, 24 (2), 171–180.

ReceiVed for reView November 13, 2009 ReVised manuscript receiVed May 6, 2010 Accepted May 25, 2010 IE901802M

Flowrate Targeting Algorithm for Interplant Resource Conservation

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