Generic Graphical Technique for Simultaneous Targeting and Design

2762

Ind. Eng. Chem. Res. 2008, 47, 2762-2777

Generic Graphical Technique for Simultaneous Targeting and Design of Water Networks Sharifah R. Wan Alwi and Zainuddin A. Manan* Chemical Engineering Department, UniVersiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

Current graphical techniques for design of water networks based on pinch analysis typically separate targeting from network design stage. In addition, most graphical allocation and network design tools that use the cleanest source to satisfy the cleanest demand fail when the flow rate of a demand is met but the mass load is not. This work presents a versatile and generic approach for simultaneous targeting and design of water networks. It includes new heuristics and significant new insights on source and demand allocation composite curves, and it introduces a new visualization tool known as the network allocation diagram (NAD). This approach can generate targets for cases with both flow-rate and mass-load constraints and simultaneously solve complex design problems involving multiple pinches. NAD also allows designers to graphically visualize, explore, evolve, and systematically choose networks that yield either the minimum water targets or the minimum number of streams. 1. Introduction The advent of water pinch analysis (WPA) as a tool for the design of an optimal water recovery network has been one of the most significant advances in the area of water conservation over the past decade.1-12 Water pinch analysis is a systematic technique for implementing strategies to maximize water reuse and recycling through integration of water-using actiVities or processes. Maximizing water reuse and recycling can minimize freshwater consumption and wastewater generation. Typical WPA solution comprises two steps, i.e., setting the minimum freshwater and wastewater flow-rate targets followed by network design to achieve the targets. Works on targeting and network design have initially focused on the mass-transferbased (MTB) water-using operations.1,2,13,14 A mass-transferbased water-using operation (also known as fixed contaminant load) is characterized by the preferential transfer of species from a rich stream to water that is being utilized as a lean stream or a mass separating agent (MSA).12 For an industrial project where flow-rate gains and losses are quite common, it may be necessary to analyze these streams separately and modify the stream data, as done by Liu et al.15 if the fixed-flow-rate approach is used. A resilient tool should be able to handle not just mass-transfer-based but also non-mass-transfer-based waterusing operations involving flow-rate gains or losses that include water used as a solvent, withdrawn as a product or a byproduct in a chemical reaction, or utilized as heating or cooling media. Graphical water pinch analysis and mathematical modeling are the two popular approaches for water network design. The former method typically cannot guarantee global optimality and has major limitations when dealing with complex waterdistribution systems involving multiple contaminants. The mathematical modeling approach offers the advantage of eliminating these drawbacks but is, however, less popular among engineering practitioners because of the difficulty in setting up the problem models and the little insights it provides on how the water-reuse network is constructed. A graphical technique based on composite curves such as the one presented in this work provides a vital visualization tool for targeting as well as * Corresponding author. Tel.: +60-07-5535609. Fax: +60-075581463. E-mail: [email protected].

Figure 1. Source and demand composite curve by El-Halwagi et al.11

for source and demand allocation. The two approaches are complimentary and are widely used to provide better engineering understanding through visualization (graphical approaches) and to handle complex problems (mathematical modeling). The next section analyzes the current graphical techniques available for targeting and design of water networks. 2. Related Works The first water network design based on composite curves was introduced by Wang and Smith.1 The method requires computations of the stream-flow-rate balances for each concentration or mass-load interval and is only limited to masstransfer-based operations. Later researchers suggested other methods for network design to achieve the targets that were not based on the composite curves. Polley and Polley5 introduced the concept of a source and demand mapping diagram and used a set of heuristics to successively match the cleanest demand with the cleanest source in ascending concentration in order to satisfy the quantity (flow rate) and quality (load) of a demand. Though the network design method by Polley and Polley5 succeeded in achieving the minimum water targets, the method

10.1021/ie071487o CCC: $40.75 © 2008 American Chemical Society Published on Web 03/13/2008

Ind. Eng. Chem. Res., Vol. 47, No. 8, 2008 2763

Figure 2. Source and demand allocation using composite curve by ElHalwagi et al.11 for freshwater with zero mass load.

Figure 4. Satisfying the cleanest demand with cleanest water source using source and demand allocation composite curve (SDAC): (a) flow-rate deficit case and (b) mass-load deficit case.

Figure 3. Source and demand allocation using composite curve by Kazantzi and El-Halwagi16 for freshwater with non-zero mass load and purest among all streams.

failed when dealing with multiple-pinch problems since, in between the pinch regions, freshwater cannot be used. Hallale10 developed a method to design networks to achieve the targets by observing the pinch division and used a linear program (LP) to guarantee a global optimum water network. Development of a graphical procedure for targeting and network design that could handle both mass-transfer-based and non-mass-transfer-based operations has lagged behind. This key research gap was bridged by El-Halwagi et al.11 as well as Prakash and Shenoy,6 who introduced the source and demand composite curves (SDCC) shown in Figure 1. The SDCC is a plot of cumulative mass load versus cumulative flow rate. It can be used to establish the minimum water flow-rate targets for both mass-transfer-based and non-mass-transfer-based waterusing operations. To construct Figure 1, the individual water demand and source lines are first plotted in ascending concentration order to form composite water source and composite water demand lines. A utility line with a slope of utility concentration is then drawn starting from the origin. Next, the composite source line is

Figure 5. Four key steps of generic simultaneous graphical targeting and design methodology.

shifted to the right along the utility line16 until it completely lies on the right-hand side of the composite demand line and touches the demand line at a pinch point. The minimum utility flow rate is given by the horizontal distance of the utility line and its intersection with the source composite. The minimum wastewater target is the overshoot of the source line. Detailed examples on the construction of SDCC are described elsewhere.11,6 The SDCC provides the pinch location and the minimum utility targets that are essential for network design step. El-Halwagi et al.11 uses the SDCC for matching and allocation of mass load and flow rates of each sources and demands. The procedure results in the source and demand allocation curves (SDACs) shown in Figure 2. The mass load allocation for each demand is shown on the y-axis of Figure 2, while the flow-rate allocation is represented on the x-axis. To satisfy a demand, the cleanest source is added to the cleanest demand in ascending concentration order to meet the mass-load and flow-rate

2764

Ind. Eng. Chem. Res., Vol. 47, No. 8, 2008

Figure 6. Comprehensive procedure for overall source and demand allocation.

requirements. If the demand quantity is met but not the quality, freshwater is added, and the remaining source(s) line is shifted to the right. Figure 2 shows an example where demand 1 (D1) flow rate is satisfied by part of source 1 (FS1,D1) and also by freshwater flow rate of FFW,D1. The mass loads are fulfilled by MS1,D1 and MFW,D1. Demand 2 (D2) flow rates are satisfied by the remainder of source 1 (FS1,D2), part of source 2 (FS2,D2), and freshwater (FFW,D2). The procedure is repeated until all demands are satisfied by the available sources and freshwater. Note that, the location of process pinch cannot be determined from SDAC alone since each demand is pinched with either the sources or the utilities. The “cleanest to cleanest” source and demand allocation rule by El-Halwagi et al.11 is also used by Polley and Polley5 for network design. Kazantzi and ElHalwagi16 extended the use of source and demand allocation for freshwater with nonzero mass load (see Figure 3). Note, however, that the cleanest to cleanest matching rule by Polley and Polley,5 El-Halwagi et al.,11 and Kazantzi and

El-Halwagi16 can only be applied for the “flow-rate deficit case”, i.e., the case where the mass load of a demand is satisfied but not its flow rate, as shown in Figure 4a. This matching rule fails to consider the “mass-load deficit case”, i.e., another important case where the source(s) meets only the flow rate of a demand but not the mass load (see Figure 4b). The cumulative water sources below and between pinch regions must have the exact same flow rate and mass load as the cumulative demand. Note that using the cleanest source that does not meet the mass load of a demand results in excess mass load of source and ultimately unsatisfied flow rate of subsequent demands. Hence, for this important case, the “cleanest to cleanest” rule used by the three authors failed. This work describes the development of a versatile and generic graphical approach for simultaneous targeting and design of a water network. It includes new heuristics and significant new insights on SDAC and introduces a new design visualization tool known as the network allocation diagram (NAD). The


Figure 7. Demand allocation on a network allocation diagram (NAD). Table 1. Limiting Data for Example 1 F, t/h

C, ppm

m, kg/h

Cum F, t/h

Cum m, kg/h

120 200 280 420 500 695

0 4 8 27.6 41.2 88

120 200 340 420 615

12 23.2 48.4 66.8 115.55

D1 D2 D3 D4 D5 D6

120 80 80 140 80 195

0 50 50 140 170 240

Sink 0 4 4 19.6 13.6 46.8

S1 S2 S3 S4 S5

120 80 140 80 195

100 140 180 230 250

Source 12 11.2 25.2 18.4 48.75

approach is applicable for mass-transfer and non-mass-transfer operations and to cases involving mass-load and flow-rate deficits and complex design problems with multiple pinches. NAD is a useful visualization tool that allows designers to graphically visualize, explore, evolve, and systematically choose networks that yield either the minimum water targets or the minimum number of streams. Section 3 of this paper describes the stepwise procedure for simultaneous targeting and design of water network. In Section

4, heuristics for other network possibilities and simplification of a water network using SDAC are elaborated. Section 5 describes how various mixing possibilities can be incorporated into SDAC and NAD. The technique presented here is applicable for a pseudo-single-contaminant system. Though a single contaminant can be rare for most water networks, a singlecontaminant analysis can also be applied to systems involving multiple contaminants by introducing system boundaries and constraints as stated by Liu et al.15 3. Methodology The graphical methodology for simultaneous targeting and network design comprises four main steps, i.e., target, allocate, design, and evolve, as shown in Figure 5. Detailed descriptions of the first three steps are described next, and the final step is described in Sections 4 and 5. Upon completion of step 3 (design), a designer can choose to either accept the water network as the final design or proceed to step 4 to evolve the network to satisfy various design needs or constraints such as to meet the minimum water targets, reduce the complexity of pipe network design, overcome geographical constraints, or meet various safety considerations.

2766


Figure 8. Final NAD for Case Study 1.

3.1. Step 1: Target. Plot the sources and demands from the cleanest to the dirtiest cumulatively to form the SDCC. Note the pinch point as well as the minimum water and wastewater flow rates and the region in which the sources and the demands are located relative to the pinch point. 3.2. Step 2: Allocate. Allocate sources to demands using SDAC based on one of the two approaches described below and illustrated in Figure 6. (1) Start from the region below the pinch by matching the cleanest source to the cleanest demand. (2) In the region below and between pinches, satisfy the mass load and the flow rate of the demand using either Approach 1 or Approach 2. (i) Approach 1 (treating the utility as an external water source): • For the flow-rate deficit case, add water utility until the demand flow rate is satisfied. • For the mass-load deficit case, pick the dirtiest source in this pinch region to satisfy the remaining mass load of the first demand.

• For the no-deficit case, use the cleanest source to satisfy the demand. (ii) Approach 2 (treating the utility as a process water source): • Treat the utility as a process water source, and satisfy the mass load and flow rate of a demand using the cleanest as well as the dirtiest sources in the pinch region, making sure the pinch point and the utility targets established in step 1 are satisfied. Approach 1 is the modified version of the “cleanest to cleanest rule” used by El-Halwagi et al.,11 Polley and Polley,5 Prakash and Shenoy,6 and Kazantzi and El-Halwagi.16 It is applicable only to cases where the utility is the cleanest stream but does not necessarily have zero mass loads. Consideration of the massload deficit case enables the source-demand allocation to be generic. Approach 2, on the other hand, treats the utility as the water source. Approach 2 can be applied for utility mass load at any level, not necessarily as the cleanest stream. (3) Once both the flow rate and the mass load of the first demand have been met, use the remaining source to satisfy the mass load of the next demand using either Approach 1 or Approach 2 until all demands below the pinch are satisfied.


Figure 9. Source and sink mapping diagram for the Sorin and Bedard4 case study that was generated using Polley and Polley5 “cleanest to cleanest” rule.

Figure 10. SDCC for Case Study 1.

(4) Repeat steps 1-3 for the region between pinches until each demand is individually satisfied. (5) For the region above the pinch, match the sources and the demands in ascending concentration order to satisfy the demand flow-rate requirements. The region above the pinch begins at the last pinch point and ends at the dirtiest end of the source composite. Note that, above the pinch: • No freshwater or higher concentration source shifting is required. • The flow rates of the sources are sufficient or bigger than those of the demands. • All source mass loads are less than the mass loads of demands. This means that demands above the pinch will be fed by a higher purity source than required. (6) Treat the remaining sources as wastewater if there is no more demand to satisfy above the pinch. 3.3. Step 3: Design. Draw the source and demand network diagram, also known as network allocation diagram (NAD),

based on SDAC. The exact flow rate of each source allocated to each demand can be obtained directly from the length of the x-axis. The NAD is constructed as follows: (1) Using the final SDAC as a basis, draw vertical lines partitioning the x-axis into demand flow-rate intervals, as shown in Figure 7 (Table 1 shows the limiting data extracted from Sorin and Bedard4 used for this example). Water demands are shown as rectangles in the demand flow-rate intervals below the x-axis, while water sources are aligned vertically on the side of the y-axis. Both demands and sources are arranged according to increasing contaminant concentration relative to the origin. (2) Next, use the same SDAC to draw vertical lines and further partition the x-axis into sources flow-rate intervals, as shown in Figure 8. The sources are also shown in rectangles just below the water demands to represent the amount of source allocated for the appropriate demand. (3) Finally, complete the NAD by drawing arrows to represent the network distribution lines/streams mapping for the various

2768


Figure 11. SDAC for Case Study 1: region below the pinch.

Figure 12. SDAC for Case Study 1: D4 mass load is not satisfied for the region between pinches.

prime sources to the relevant demands using the rectangular source boxes as a guide (refer to Figure 8). The use of the new generic approach based on both SDCC and SDAC to guide network design as described previously allows the designer to address design problems with both flowrate and mass-load constraints. It is an essential visualization tool to address complex design problems, particularly those involving multiple pinches that limited the previous “cleanest to cleanest rule” for network design. The combination of SDCC, SDAC, and source-demand distribution that forms NAD essentially guarantees the minimum water targets to be achieved. The next section describes the application of the new procedure on two case studies using Approach 1 and Approach 2. 3.4. Case Study 1: Simultaneous Targeting and Design Using Approach 1. The case study from Sorin and Bedard4 in Table 1 mentioned previously will be used to illustrate the

stepwise application of the new design technique using Approach 1. As can be seen, in this case, the utility has a concentration of 0 ppm. Figure 9 shows the source and sink mapping diagram generated using the Polley and Polley5 “cleanest to cleanest” rule. The minimum freshwater and wastewater flow-rate targets established using various graphical and numerical procedures for this problem are 200 and 120 t/h, respectively. However, the network diagram constructed using the “cleanest to cleanest” rule yielded freshwater and wastewater targets at 204.44 and 124.44 t/h, respectively (see Figure 9). The respective authors Hallale10 and Manan et al.12 initially thought that the problem lies in the multiple-pinch case. Note that this situation actually occurs when the mass-load requirements of a demand below or in between pinch regions are not met (mass-load deficit case) and could occur either below or in between pinch regions. We will now demonstrate how NAD


Figure 13. SDAC for Case Study 1: shifting of dirtiest source to satisfy D4 for region between pinches.

Figure 14. SDAC for Case Study 1: satisfying D4 for region between pinches.

led designers to the correct design to achieve the minimum flowrate targets of 200 t/h of freshwater and 120 t/h of wastewater for the Sorin and Bedard4 example. 3.4.1. Step 1. Plot the sources and demands from the cleanest to the dirtiest cumulatively to form source and demand composite curves (SDCC) The SDCC for Case Study 1 is as shown in Figure 10. The pinch points are noted at 100 and 180 ppm. This defines the regions below, between, and above the pinch points. The freshwater and wastewater targets are at 200 and 120 t/h, respectively. 3.4.2. Step 2. Draw the SDAC based on step 2 of the Approach 1 procedure described earlier. 3.4.2.1 Region below the Pinch. There are three demands located below the pinch region, i.e., D1, D2, and D3 (see Figure 10). D1 required 120 t/h of water with zero mass loads. Since

there is no water source with zero mass loads, the first demand flow rate is completely satisfied using freshwater. This corresponded to 120 t/h of freshwater usage. For demand D2, only part of S1 could satisfy the mass load of D2, as shown in Figure 11. Since the cumulative water source(s) satisfied the mass load but not the flow rate (flowrate deficit case), water utility is added until the demand flow rate is met. Thus, 40 t/h of S1 and 40 t/h of freshwater are needed to satisfy D2. The final demand located below the pinch region is D3. The remaining S1 located below the pinch region are used to satisfy D3 mass-load requirement (procedure 3). Again, the “flow-rate deficit case” occurs. Freshwater is added until the entire demand flow-rate requirements are satisfied. Hence, 40 t/h of S1 and 40 t/h of freshwater are fed to D3. The SDAC below the pinch region are shown in Figure 11.

2770


Figure 15. SDAC for Case Study 1: satisfying D5 for region between pinches.

Figure 16. SDAC for Case Study 1 for the region above the pinch.

3.4.2.2. Region between Pinches. Figure 10 shows two demands located between the pinch regions, i.e., D4 and D5. D4 flow rate is satisfied using part of S1, all of S2, and part of S3 in ascending order of source concentration based on procedure 1. However, for D4, only the flow rate is satisfied but not the mass-load requirement (see Figure 12). Using the “mass-load deficit case” procedure, S3 as the dirtiest source in the region is shifted downward along S2 line until all the massload and flow-rate requirements of D4 are satisfied, as shown in Figure 13. Hence 40 t/h of S1, 60 t/h of S2, and 40 t/h of S3 are allocated for D4 (see Figure 14). The remaining S2 (20 t/h) and S3 (60 t/h) in the pinch region are used to satisfy the remaining demand, i.e., D5. Figure 15 shows the final source and demand allocation for the region between pinches.

3.4.2.3. Region above the Pinch. Finally, to satisfy the flow rate for the above-pinch region, the pinch sources in the region are used in ascending order to satisfy the demand flow-rate requirement. Hence, 40 t/h of S3, 80 t/h of S4, and 75 t/h of S5 are used to satisfy D6 flow rate. The amount of mass load accumulated from these three sources only totaled 44.35 kg/h, but D6 could actually accept 48.75 kg/h. Excess S5 is rejected as wastewater since there was no more demand left to satisfy. The SDAC above the pinch is shown in Figure 16, and the final allocation diagram is shown in Figure 17. 3.4.3. Step 3: Draw the NAD. The NAD is drawn upon completion of steps 1 and 2. Figure 8 shows the completed NAD for case 1. Note that, though both SDAC and NAD approaches enable source and demand mass load as well as flow-rate


Figure 17. Final SDAC for Case Study 1.

Figure 18. SDCC for Case Study 2. Table 3. Example 3: Limiting Water Data from Polley and Polley5

Table 2. Limiting Data for Example 2 F, t/h

C, ppm

m, kg/h

F, t/h

C, ppm

Sink D1 D2 D3

70 70 100

S1 S2 S3

50 75 100

20 30 120

1.4 2.1 12

10 50 150

0.5 3.75 15

Source

allocations, the NAD approach allows a designer to clearly visualize the water network stream/pipeline distribution systems that are crucial for pipe network design initialization and evolution. SDAC, unfortunately, gives no indication for stream network distribution and, therefore, cannot be used to yield the final water network design. Note that, by extracting mass-load and flow-rate values directly from NAD, there is no need to

m, kg/h

D1 D2 D3 D4

50 100 80 70

20 50 100 200

Sink j 1 5 8 14

S1 S2 S3 S4

50 100 70 60

50 100 150 250

Source i 2.5 10 10.5 15

Cum F, t/h

Cum m, kg/h

50 150 230 300

1 6 14 28

50 150 220 280

2.5 12.5 23 38

calculate the flow rate and mass load for each source allocated to a demand as required by other design methods such as source-sink mapping diagram,5 nearest-neighbor algorithm,6 and concentration block diagram.17

2772


Figure 19. Use of Approach 2 for Case Study 2: application of “cleanest to cleanest” rule satisfies D1 flow rate but not the mass load.

Figure 20. Use of Approach 2 for Case Study 2: application of “cleanest and dirtiest” rule satisfies both D1 flow rate and mass load.

3.5. Case Study 2: Simultaneous Targeting and Design using Approach 2. Table 2 shows the limiting data for Case Study 2. This case study illustrates the stepwise application of the new design technique using Approach 2. In this case, freshwater has a concentration of 30 ppm. 3.5.1. Step 1. Plot the sources and demands from the cleanest to the dirtiest cumulatively to form a SDCC. Figure 18 shows SDCC for Case Study 2. A pinch point at 50 ppm separates the regions below and above the pinch. The freshwater and wastewater targets are 75 and 60 t/h, respectively. 3.5.2. Step 2. Draw the SDAC based on step 2 of the Approach 2 procedure described earlier. 3.5.2.1 Region below the Pinch. There are two demands located below the pinch region, i.e., D1 and D2 (from Figure

18). D1 requires 70 t/h of water with 1.4 kg/h mass loads. The utility is conveniently treated as a source based on Approach 2. The cleanest cumulative source are used first to satisfy the flow rate and mass load of D1. Figure 19 shows that using all of S1 and part of the utility line satisfies only the flow rate but not the mass load of D1. Hence, the dirtiest source from below the pinch is used. The S2 line is shifted downward along freshwater and S2 lines, as shown in Figure 20. Hence, 50 t/h of S1, 5 t/h freshwater, and 15 t/h of S2 are allocated to satisfy both D1 flow rate and mass load. The remaining freshwater line at 70 t/h flow rate is then used to satisfy D2. Figure 21 shows the final SDAC for the region below the pinch for Case Study 2. 3.5.2.2. Region above the Pinch. The sources in the region


Figure 21. Final SDAC for Case Study 2 for the region below the pinch.

Figure 22. Final SDAC for Case Study 2.

above the pinch are used in ascending concentration order to satisfy the demand flow-rate requirement. Hence, 60 t/h of S2 and 40 t/h of S3 are used to satisfy D3 flow rate. The total mass load from these two sources is 9 kg/h, but D3 can actually accept 12 kg/h. The remaining 60 t/h of S3 is rejected as wastewater since there is no more demand to satisfy. Figure 22 show the SDAC above the pinch and the overall SDAC. 3.5.3. Step 3: Draw the NAD Based on SDAC. The NAD is drawn upon completion of steps 1 and 2. The final NAD for Case Study 2 is shown in Figure 23.

4. Step 4: Water Network Evolution (Evolve) The network diagram obtained using the stepwise procedure described previously is only one of the many possible maximum water-recovery network designs. Often, complex network designs may result because of numerous stream splittings. It may be possible, in some cases, to avoid some stream splitting and simplify a water network at the expense of some water penalty. The SDAC from step 2 can be used with two new heuristics to explore network alternatives that achieve either the minimum water targets or the minimum streams units.

2774


Figure 23. Final NAD for Case Study 2.

Figure 24. SDAC using step 2 (allocate) for Case Study 3.

4.1. Heuristic 1. To ensure the minimum water targets and avoid the water penalty, use the SDAC to shift, cut, and allocate sources to satisfy the mass-load and flow-rate requirements for each demand individually in a given pinch region. Note that,

unlike previous heuristics stated by other researchers such as Hallale,10 not only must the pinch division be observed but also the individual demand’s quality and quantity must be fully satisfied.


Figure 25. Alternative source and demand allocation achieving the same minimum freshwater and wastewater flow-rate targets.

Figure 26. Alternative source and demand allocation with freshwater and wastewater penalties.

4.2. Heuristic 2. To ensure the minimum number of streams and reduce network complexity, use the SDAC to shift, cut, and allocate sources to satisfy a demand quantity, not necessarily the quality, using the minimum number of streams available from any pinch regions. A penalty of increased freshwater or utility consumption may be incurred. Figure 24 shows an example of the source and demand allocation curve for the limiting water data from Polley and Polley5 (see Table 3). Another possible source and demand allocation curve that achieves the minimum water targets from application of Heuristic 1 is shown in Figure 25. In this case, only sources below the pinch region are cut and shifted. For instance, part of S2 (FS2,D1 ) 10 t/h) is cut and shifted for use with D1 instead of D2 initially. This increases the freshwater to 40 t/h to satisfy D1. Alternatively, the network can be simplified by S1 (FS1,D2 ) 50 t/h) shifted for use only with D2. Part of S2 (FS2,D2 ) 10 t/h) and S3 are also cut and shifted (FS3,D2 ) 10 t/h). The remaining freshwater is used to satisfy

D2 (FFW,D2 ) 30 t/h), and all D3 is satisfied using the remaining S2 (FS2,D3 ) 80 t/h). Figure 26 is a possible SDAC constructed based on Heuristic 2 that yields a simpler structure with fewer stream splits that ensures the minimum number of streams are achieved. However, penalties of freshwater and wastewater have been incurred, with the new freshwater and wastewater targets increased to 100 and 80 t/h, respectively, as compared to the initial targets of 70 and 50 t/h, respectively. The sources from above and below the pinch are used to satisfy the demand flow rate but not necessarily the demand mass load. The demand is fed by sources with either the same or less total mass load as that required by the demand. From Figure 26, it can be seen that • D1 is completely satisfied using freshwater. • All S1 is used to satisfy D2, and the remaining flow rate of D2 is satisfied using freshwater.

2776


Figure 27. Illustration of cases involving direct reuse and sources mixing: (a) direct mapping and reuse of sources and demands and (b) mixing of sources for D1 and D2 as well as D3 and D4 before reuse.

Figure 28. Illustration of cases involving direct reuse and sources mixing: (a) mixing of sources for D1, D2, and D3 before reuse and one direct reuse and (b) mixing all sources before reuse in demands.


• Part of S2 is used to satisfy D3, and the remaining S2 becomes wastewater. • All S3 is used for D4, and all S4 becomes wastewater. The two case studies illustrate the usefulness of the SDAC as a visualization tool for network manipulations based on the two proposed heuristics. A designer could explore various water-allocation possibilities with or without water penalty using the SDAC and proceed to design a final water network using step 3. 5. Exploring Mixing Possibilities Using NAD SDAC and NAD can also be used to explore various sourcemixing possibilities. In some cases, it may be desirable to mix water sources in order to reduce a network complexity. Say, for example, four water demands are available at the same limiting concentration. Instead of directly reusing the water sources with numerous split streams, it is possible to mix all the sources in one large tank before reuse. Figure 27a represents the typical allocation curve and the resulting network diagram showing each demand mapped directly to the relevant sources and freshwater supply prior to exploring various mixing scenarios. Figure 27b shows that the relevant sources can alternatively be mixed together in tank 1 to satisfy D1 and D2 and in tank 2 to satisfy D3 and D4. Here, the mass loads and flow rates of D1 and D2 as well as D3 and D4 are satisfied in combination instead of individually. Other possible alternatives include mixing all S1 and freshwater to satisfy D1-D3 (Figure 28a) and mixing all sources to satisfy all demands (D1-D4) (Figure 28b). Clearly, the SDAC and NAD are essential tools to assist designers in visualizing and customizing various mixing and splitting possibilities to ultimately lead to the simplest and most practical network design. 6. Conclusion A versatile and generic graphical approach for simultaneous targeting and design of water networks has been developed. The approach includes new heuristics and significant new insights on source and demand allocation curves, and it introduces a new design visualization tool known as the network allocation diagram (NAD). The approach is applicable for mass-transfer and non-mass-transfer operations and to cases involving mass-load and flow-rate deficits and complex design problems with multiple pinches. NAD is a useful visualization tool that allows designers to graphically visualize, explore, evolve, and systematically choose networks that yield either the minimum water targets or the minimum number of streams. Nomenclature AbbreViations CBD ) concentration block diagram CC ) composite curve NAD ) network allocation diagram NNA ) nearest-neighbor algorithm SDAC ) source and demand allocation composite curve SDCC ) source and demand composite curve WPA ) water pinch analysis WSD ) water source diagram Symbols S ) source D ) demand C ) concentration F ) flow rate

M ) mass load Cum ) cumulative kg/h ) kilograms per hour ppm ) parts per million FSi,Dj ) amount of source i flow rate used to satisfy demand j flow rate MSi,Dj ) amount of source i mass load used to satisfy demand j mass load t/h ) ton per hour Subscripts D ) demand FW ) freshwater i ) source number j ) demand number S ) source WW ) wastewater Literature Cited (1) Wang, Y. P.; Smith, R. Wastewater Minimisation. Chem. Eng. Sci. 1994, 49, 981-1006. (2) Olesen, S. G.; Polley, G. T. A simple methodology for the design of water networks handling single contaminants. Trans. Inst. Chem. Eng., Part A 1997, 75, 420-426. (3) Dhole, V. R.; Ramchandani, N.; Tainsh, R. A.; Wasilewski, M. Make your process water pay for itself. Chem. Eng. 1996, 103, 100103. (4) Sorin, M.; Be´dard, S. The global pinch point in water reuse networks. Trans. Inst. Chem. Eng., Part B 1999, 77, 305-308. (5) Polley, G. T.; Polley, H. L. Design better water networks. Chem. Eng. Prog. 2000, 96 (2), 47-52. (6) Prakash, R.; Shenoy, U. V. Targeting and design of water networks for fixed flowrate and fixed contaminant load operations. Chem. Eng. Sci. 2005, 60 (1), 255-268. (7) Mann, J. G.; Liu, Y. A. Industrial water reuse and wastewater minimization; McGraw Hill: New York, 1999. (8) Feng, X.; Seider, W. D. New structure and design method for water networks. Ind. Eng. Chem. Res. 2001, 40, 6140-6146. (9) Dunn, R. F.; Wenzel, H. Process integration design methods for water conservation and wastewater reduction in industry. Part 1: Design for single contaminant. Clean Prod. Process. 2001, 3, 307-318. (10) Hallale, N. A new graphical targeting method for water minimization. AdV. EnViron. Res. 2002, 6 (3), 377-390. (11) El-Halwagi, M. M.; Gabriel, F.; Harrel, D. Rigorous graphical targeting for resource conservation via material reuse/recycle networks. Ind. Eng. Chem. Res. 2003, 42, 4319-4328. (12) 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. (13) Takama, N.; Kuriyama, T.; Shiroko, K.; Umeda, T. Optimal water allocation in a petroleum refinery. Comput. Chem. Eng. 1980, 4, 251258. (14) Castro, P.; Matos, H.; Fernandes, M. C.; Nunes, C. P. Improvements for mass-exchange networks design. Chem. Eng. Sci. 1999, 54, 16491665. (15) Liu, Y. A.; Lucas, B.; Mann, J. Up-to-Date Tools for Water-System Optimization. Chem. Eng. Mag. 2004, 1, 30-41. (16) Kazantzi, V.; El-Halwagi, M. M. Targeting Material Reuse via Property Integration. Chem. Eng. Prog. 2005, 202 (8), 28. (17) Tan, Y. L. Development of New Systematic Techniques for Retrofit of Water Network. MSc. Thesis; Universiti Teknologi Malaysia, Johor, Malaysia, 2005.

ReceiVed for reView November 2, 2007 ReVised manuscript receiVed January 29, 2008 Accepted February 1, 2008 IE071487O

Generic Graphical Technique for Simultaneous Targeting and Design

Recommend Documents