Targeting for Conventional and Property-Based Water Network with

ARTICLE pubs.acs.org/IECR

Targeting for Conventional and Property-Based Water Network with Multiple Resources Chun Deng† and Xiao Feng*,‡ † ‡

Department of Chemical Engineering, Xi’an Jiaotong University, Xi'an, 710049, P.R. China College of Chemical Engineering, China University of Petroleum, Beijing, 102249, P.R. China ABSTRACT: Water scarcity and stringent emission legislation have been motivating process industries to emphasize water conservation. Pinch analysis has been accepted as one of the powerful approaches to locate targets of wastewater minimization. In this paper, the improve problem table (IPT) is presented to target conventional and property-based water networks with multiple resources on the basis of the composite table algorithm (CTA) (Agrawal, V.; Shenoy, U. V. Unified conceptual approach to targeting and design of water and hydrogen networks. AIChE J. 2006, 52, 1071-1082). The corresponding limiting composite curve and water supply line are plotted to facilitate the analyzing and understanding of basic physical insights. The minimum operating cost subject to availability of the water resources is also addressed. In addition, two strategies are proposed to handle flow rate constraints for certain resources, and the corresponding wastewater stream identification techniques are introduced successively. To illustrate the applicability of IPT, two conventional so-called concentration-based water networks with different scenarios, i.e., direct reuse/ recycle and regeneration reuse/recycle, are synthesized. Moreover, the targets for a property-based water network with a pretreatment system are determined via the proposed IPT.

’ INTRODUCTION In recent years, stringent environmental regulations and also the escalating cost of freshwater as well as wastewater treatment have motivated the process and manufacturing industries to emphasis waste minimization in their daily operations. In particular, water network synthesis has gained much attention in both industrial and research communities. Over the past decades, massive studies have been conducted for the synthesis of water networks, ranging from both conceptual and mathematical optimization approaches. The basic principles and variety of applications of water network synthesis have been reviewed in articles2-4 and described in the textbooks.5,6 Pinch analysis is widely accepted as a promising tool in addressing water network synthesis problems for the process industries as reviewed in the literature.3 In general, water network synthesis may be classified into two main categories:7,8 fixed contaminant load (FC) and fixed flow rate (FF) problems. In the former, water-using processes (e.g., washing, scrubbing, and extraction) are characterized by mass transfer operations where a fixed amount of contaminant is transferred from a contaminant-rich stream to water, which acts as a mass separating agent. In contrast, water-using processes (e.g., boilers, cooling towers, reactors) are characterized as water sinks/sources that consume/generate a fix amount of water in the FF problems. Hence, the primary concern of this latter problem is the water flow rate. As pointed out by Foo,3 the limiting water data for the FC problem and FF problem are interchangeable. In the seminal work of the FC problem, Wang and Smith9 specialized the mass exchange network (MEN) proposed by ElHalwagi and Manousiouthakis10 into water network synthesis. They proposed limiting composite curves to locate minimum fresh water and wastewater targets for a water system involving mass transfer processes. However, the approach proposed by Wang and Smith9 was not applicable to nonmass transfer-based r 2011 American Chemical Society

FF operations . To overcome the limitation of the FC problem, the FF problem is explored in the literature.1,7,8,11-25 In addition, there are several fresh water sources or resources with different qualities available for usage in various processes. Targeting for multiple resources are not exactly the same as that for a single resource. Wang and Smith26 first addressed the procedure for targeting the FC problem with multiple resources. Later, Foo18 extended the water cascade analysis (WCA)8 to target multiple fresh water feeds for the FF problem. Almutlaq et al.27 extended the noniterative algebraic procedure presented by Almutlaq and El-Halwagi28 to target a material reuse/recycle network with impurity fresh usage. On the basis of the extension of material recovery pinch diagram,13 Alwi and Manan17 determined the minimum flow rate targets for multiple resources for the FF problem. Recently, Shenoy and Bandyopadhyay22 developed a methodology to target multiple resources through the source composite curve (SCC)15 in order to minimize the operating cost of the overall process. After maximizing the water recovery potential via direct reuse/ recycle, the freshwater consumption of a water network may be further reduced via regeneration or interception. Wang and Smith9 extended the use of the limiting composite curve to determine the water flow rate targets for regeneration reuse/ recycling schemes in the FC problem. Several studies29-35 have been conducted on targeting the FC water network with regeneration recycling or regeneration reuse. On the other hand,

Special Issue: Water Network Synthesis Received: June 1, 2010 Accepted: January 21, 2011 Revised: December 23, 2010 Published: February 16, 2011 3722

dx.doi.org/10.1021/ie1012008 | Ind. Eng. Chem. Res. 2011, 50, 3722–3737

Industrial & Engineering Chemistry Research

ARTICLE

several approaches have also been developed to handle water regeneration placement in the FF problems.1,7,16,20,21,24,36,37 It is worth mentioning that the previous proposed approaches are limited to address problems that are “chemo-centric” or concentration-based. However, many design problems are controlled by properties instead of chemical constituency.38 For instance, the selection of solvents typically relies on properties such as equilibrium distribution coefficients, viscosity, and volatility. In addition, effluent legislation is often defined in term of properties (e.g., pH, turbidity, toxicity, color) apart from pollutant concentration (e.g., COD, suspended solids, etc.).38 Those factors lead to the development of property integration; the framework of property integration for material recovery was established by El-Halwagi and co-workers.38-50 Property integration is defined as a functionality-based, holistic approach to the allocation and manipulation of streams and processing units, which is based on the tracking, adjustment, assignment, and matching of functionalities throughout the process.38 Several graphical,40,44,45 algebraic,41 and mathematical optimizationbased37,43,46-50 approaches have been developed for reuse/ recycle and interception network. However, few works have been conducted to locate the targets for a regeneration reuse/recycling water network with multiple resources. In addition, the property-based water network with multiple resources is essential to be explored. This paper proposes an improved1 problem table (IPT) approach based on the extension of the CTA to target the conventional and property-based water network with multiple resources. The proposed IPT is applicable to several scenarios, such as direct reuse/ recycle, regeneration reuse/recycle, and placement of pretreatment system. The corresponding limiting composite curves and water supply lines are constructed to show the targeted results vividly. In addition, the operating cost is minimized via introducing the concept of prioritized cost.22 Three case studies are used to illustrate the applicability of the proposed method.

’ PROBLEM STATEMENT The problem for synthesis of concentration or property-based water network may be stated as follows: Given a set of process units, their outlets can be defined as set of process sources (NSR) and inlets can be defined as set of process sinks (NSK). Each process source with a specified outlet concentration (CSRi) or property (PSRi) and limiting outlet flow rate (Flim SRi) may be considered for reuse/recycle or discharge. Each process sink can accept sources to meet the limiting inlet flow rate (Flim SKj) and an allowable inlet concentration (CSKj) or property (PSKj), which complies with the predetermined allowable concentration or property constraints as follows max Cmin SKj e CSKj e CSKj

ð1Þ

min max PSKj e PSKj e PSKj

ð2Þ

max min max where Cmin SKj and CSKj or PSKj and PSKj are the specified lower and upper bounds of the acceptable concentrations or properties to jth process sink. To avoid the nonlinearities related to mixing rules for certain properties,50 the linear property mixing rule based on the property operator is defined by eq 339,40

ψðPÞ ¼

∑i xSRi ψðPSRi Þ

ð3Þ

Table 1. Water Sinks and Sources for Example 126 water

flow rate

concentration

water

flow rate

concentration

sinks

(t/h)

(ppm)

sources

(t/h)

(ppm) 100

1

20

0

1

20

2

100

50

2

100

100

3

40

50

3

40

800

10

80

4

170

160

Table 2. Resource Specifications for Example 122 maximum

a

water

concentration

flow rate

costa

resources

(ppm)

(t/h)

($/t)

FW1

0

¥

1

FW2

25

¥

0.5

FW3

60

100

0.2

The cost data are assumed values.

where ψ(PSRi) and ψ(P) are property operators on the source property (PSRi) and the mixture property (P), respectively. The term xSRi corresponds to the fractional contribution of SRi in the total mixture flow rate. Thus, the property constraint defined in eq 2 can be converted to the constraint specified by property operator, as shown in eq 4 ψmin ðPSKj Þ e ψðPSKj Þ e ψmax ðPSKj Þ

ð4Þ

where ψ (PSKj) and ψ (PSKj) specifies the minimum and maximum property operators on the sink property (PSKj). Besides, a set of external sources or resources (NFW) may be purchased to fulfill the requirement of the sinks. Each source with the fixed unit cost (CostFWm) has specified concentration (CFWm) or property (PFWm), and its flow rate targets need to be determined. The objective of this work is to locate the minimum operating cost for concentration or property-based water networks prior to detailed design. Different scenarios are explored. They cover direct water reuse/recycle with multiple resources, regeneration reuse/recycle with multiple resources, as well as the placement of pretreatment system. Three case studies taken from the literature on the basis of the concentration or property are implemented to illustrate the applicability of the proposed approach. min

max

’ IMPROVED PROBLEM TABLE The improved problem table (IPT) is extended on the basis of the composite table algorithm (CTA).1 It can be applied to target conventional or property-based water networks for several scenarios, i.e., multiple water resources, regeneration reuse/ recycle, and pretreatment placement. Three examples are solved to illustrate the applicability of the improved approach. Example 1—Conventional Water Network with Multiple Resources. To illustrate IPT for targeting conventional water

network with multiple resources, example 126 with its process data in Table 1 and resource specifications in Table 2 is solved and the detail calculation steps are illustrated as follows. Step 1: Tabulate all concentrations (of all process sources, sinks, and resources) in an increasing order in the first column. Do not repeat the same concentration if one particular value occurs more than once. Add one more arbitrary value (Carbitrary,

3723

dx.doi.org/10.1021/ie1012008 |Ind. Eng. Chem. Res. 2011, 50, 3722–3737


ARTICLE

Table 3. Improved Composite Table (IPT) and Targeting for Example 1 with Multiple Water Resources

in parentheses) at the bottom of the column such that it is the largest value, e.g., 850 ppm in Table 3. The arbitrary value is used to provide only an end point and assist constructing the last segment of the limiting composite curve. Without loss of generality, the concentration for the kth row is denoted as Ck such that C1 < C2 < ::: < Ck < ::: < Carbitrary

ð5Þ

rows are summed to obtain the cumulative load for the kth row. The limiting composite curve as shown in Figure 1 can be constructed via plotting the concentration column against the cumulative load column in Table 3 8 cum > "k ¼ 1 < Δmk ¼ 0 t¼k ð8Þ > ¼ Δmt "k > 1 : Δmcum k

∑

t ¼1

Step 2: Tabulate the net flow rates (Fkþ1) in the second column. The sum of the flow rates of the process sources is subtracted from the sum of the flow rates of the sinks present in each concentration interval. To clearly determine the streams (process sources or sinks) presented in each concentration interval, the streams are demonstrated by vertical arrows (as shown in Table 3) which start from their specified concentrations and end at the assumed arbitrary concentration with given flow rates labeled below them. Thus, all the sources and sinks with their specified concentrations less than Ckþ1 will present in the (k þ 1)th concentration interval (Ck,Ckþ1). The net flow rate in the concentration interval can be calculated by eq 6 Fk þ 1 ¼

∑j FSKj - ∑i FSRi

" CSKj , CSRi < Ck þ 1

ð6Þ

For instance, all the sinks (SK1, SK2, SK3, and SK4) and two process sources (SR1 and SR2), whose specified concentrations are less than 800 ppm (seventh row), present in the concentration interval (100 ppm, 800 ppm). On the basis of the given flow rates, the net flow rate in the seventh row in Table 3 is calculated as 50 t/h via solving eq 6. In addition, the net flow rate corresponds to the inverse slope of the segment of limiting composite curve. Note that the last value of this column in Table 3 is obtained by subtracting the sum of all process sources from the sum of all sinks, which determines the net system flow rate. It is a constant for a given problem (10 t/ h for Example 1 in Table 1). Step 3: Tabulate the net loads (Δmk) in the third column. Multiply the net flow rate by the concentration difference of the corresponding interval to obtain the net load (as shown in eq 7) ( Δmk ¼ 0 "k ¼ 1 ð7Þ Δmk ¼ Fk ðCk - Ck - 1 Þ "k > 1 Step 4: Tabulate the cumulative load (Δmcum k ) in the fourth column by eq 8. The cumulative load for the first row is set to zero because of no load cumulated. The loads for all the previous

where t specifies the index for the tth row (t e k). Step 5: Tabulate the possible water supply flow rates for the first water resource (FW1) at each concentration Ck(CFW1 < Ck e Carbitrary) in the fifth column via eq 9 FFW1 ¼

Δmcum k Ck - CFW1

" CFW1 < Ck e CFW2

ð9Þ

where CFW1 denotes the concentration of first water resource (FW1). For example 1, CFW1 is assumed to be zero. The maximum value in the fifth column (92 t/h) in Table 3 reveals the flow rate target for FW1 without considering other water resources, and the corresponding concentration (100 ppm, Cpinch) locates the pinch point. Note that the concentration of second water resource (FW2,CFW2) is 25 ppm, which is less than 100 ppm (pinch). Thus, the consumption of FW1 can be reduced by introducing FW2. However, the flow rate reduction of FW1 cannot guarantee the cut of the operating cost. Next, the prioritized cost22 is calculated by solving eq 10 to evaluate whether the introduction of FW2 is beneficial or not CostPrioritized ¼ FWm

CostFWm Cpinch - CFWm

ð10Þ

where CostPrioritized denotes the prioritized cost for mth water FWm resource. The calculated prioritized costs for FW1 and FW2 are 0.01 and 0.007 $/t respectively as shown in Table 4. Thus, the introduction of FW2 is cost advantageous because its prioritized cost is less than that of FW1. With the introduction of FW2, the maximum value when 0 < Ck e 25 ppm in the fifth column (20 t/h) in Table 3 determines the target of FW1, and the corresponding concentration (25 ppm) is the pinch for FW1 (Point A in Figure 1). The pinch specifies the bottleneck for FW1. The flow rate for FW1 cannot be less than 20 t/h or else certain water-using processes cannot be fulfilled, and it will be unfeasible. As shown in Figure 1, the first segment of the water supply line can be constructed within the concentration intervals of 0 ppm and 25 ppm with its inverse 3724



ARTICLE

Figure 1. Limiting composite curve and targeting water network with multiple resources (example 1).

Table 4. Prioritized Costs for Water Resources pinch water

concentration

concentration

cost

prioritized

resources

(ppm)

(ppm)

($/t)

cost ($/t)

FW1

0

100

1

0.01

FW2

25

100

0.5

0.007

FW3

60

100

0.2

0.005

slope corresponds to 20 t/h. The section of the limiting composite curve above it is considered as Region 1, where only FW1 is allocated to fulfill the requirement. Step 6: Tabulate all the possible water supply flow rates for the second resource (FW2) in the sixth column (CFW2 < Ck e Carbitrary) in Table 3 via eq 11. In this column, the maximum value in the sixth column (96 t/h) locates the flow rate target for FW2 without considering other resources with lower quality (i.e., third resource, FW3), and the corresponding concentration (100 ppm, Cpinch) is the pinch concentration. FFW2 ¼

Δmcum - FFW1 ðCFW2 - CFW1 Þ k - FFW1 Ck - CFW2 " CFW2 < Ck e CFW3

ð11Þ

Note that the concentration of third water resource (CFW3) is 60 ppm, which is less than 100 ppm (pinch). Thus, the consumption of FW2 can be reduced by introducing FW3. Similarly, by solving eq 10, the prioritized costs for FW2 and FW3 are calculated as 0.007 and 0.005 $/t, respectively, as shown in Table 4. Thus, the introduction of FW3 is cost advantageous because its prioritized cost is less than that of FW2. With the introduction of FW3, the maximum value when 25 ppm < Ck e 60 ppm in the sixth column (40 t/h) in Table 3 determines the target of FW2, and the corresponding concentration (60 ppm) is

the pinch for FW2 (Point B in Figure 1). As shown in Figure 1, the second segment of the water supply line can be constructed with the concentration intervals of 25 ppm and 60 ppm with its inverse slope corresponding to 60 t/h (= 20 t/h þ 40 t/h). The section of the limiting composite curve above it is considered as Region 2, where FW1 and FW2 are used to remove the load of this region. The physical meaning of eq 11 can be explained as follows. The numerator of the first term in eq 11 denotes the load between concentration CFW2 and Ck. The first term in eq 11 is solved to get the summation flow rate of FW1 and FW2. Thus, the residual flow rate is determined as the target of FW2 with the subtraction of FW1. Next, tabulate the possible water supply flow rates for the third resource (FW3) in the seventh column via eq 12. The maximum value in the seventh column gives the minimum flow rate of FW3 (105 t/h) as shown in Table 3, and the corresponding concentration (100 ppm) locates the pinch of FW3 (Point P in Figure 1). As shown in Figure 1, the third segment of water supply line can be constructed with the concentration intervals of 60 and 850 ppm with its inverse slope corresponding to 165 t/h (20 t/h þ 40 t/h þ 105 t/h). The section of the limiting composite curve above it is identified as Region 3, where FW1, FW2, and FW3 are used to remove the load of this region FFW3 ¼

Δmcum - FFW1 ðCFW3 - CFW1 Þ - FFW2 ðCFW3 - CFW2 Þ k Ck - CFW3 - FFW1 - FFW2

" CFW3 < Ck e Carbitrary

ð12Þ

Note that, the concentration Ck in eq 12 is moved from the third resource concentration CFW3 to the arbitrary concentration Carbitrary because there is no other available resources above CFW3. Generally, the flow rate of mth (m g 2) resource (FWm) can be calculated via eq 13, and the maximum value between concentration intervals of CFWm and CFW(mþ1) determines the 3725



ARTICLE

Table 5. Targeting for Multiple Water Resources with Flow Rate Constraint for FW3 Based on Strategy 1 (Example 1)

Figure 2. Water supply line with flow rate constraint for FW3 based on strategy 1 (example 1).

target for FWm n¼m - 1

FFWm ¼

Δmcum k

∑

n¼1

FFWn ðCFWm - CFWn Þ

Ck - CFWm

n¼m - 1

-

∑

n¼1

FFWn

" CFWm < Ck e CFWðm þ 1Þ ð13Þ

where n denotes index for nth water resources (n < m). Step 7: Meet the flow rate constraints for fresh resources, i.e., certain resources have limited supply flow rates, and resources have certain flow rate relationships with each other. For example 1, the targeted flow rate for FW3 is 105 t/h, which exceeds the maximum available value (100 t/h for FW3 according to the data in Table 2). Hence, the flow rates of FW1 and/or FW2 should be increased to reduce the flow rate of FW3 to the

limit. To minimize the operating cost, the flow rate of FW2 other than that of FW1 is increased because the prioritized cost of FW2 is lower than that of FW1. Thus, we can keep the minimum flow rate of FW1 and increase a little flow rate of FW2. There are two alternative strategies to handle the flow rate constraints. Strategy 1. To meet the supply flow rate limit (100 t/h) of FW3, it is considered as a new water source, 100 t/h with the concentration 60 ppm. Thus, a new vertical arrow for FW3 is added in Table 5. Then only two resources FW1 and FW2 are left for targeting. Repeating steps 1-6, the targets for FW1 and FW2 are determined as 20 t/h and 42.67 t/h, respectively (Table 5). As shown in Figure 2, the limiting composite curve can be constructed by plotting the concentration column against the cumulative mass load column in Table 5 because FW3 is assumed to be a new water source (100 t/h, 60 ppm) added into water system. The net flow rate above 60 ppm is sharply reduced from 160 t/h to 60 t/h as shown in Table 5 and becomes negative 3726



ARTICLE

Figure 3. Water supply line with flow rate constraint for FW3 based on strategy 2 (example 1).

above 100 ppm. The corresponding limiting composite curve points left, which means the surplus water source exists above 100 ppm, and the systems do not need resources to meet the requirement. The first segment of water supply line is constructed between the concentration intervals of 0 ppm and 25 ppm with the inverse slope of 20 t/h. Only FW1 is supplied to fulfill the requirement of Region 1. In Region 2, FW1 and FW2 are allocated to remove the mass load, and the second segment of water supply line is constructed between the concentration intervals of 25 ppm and 100 ppm with its inverse slope of 62.67 t/h (20 t/h þ 42.67 t/h). Above 100 ppm, the supply of the water resource is not necessary because of the surplus water source, and only wastewater streams leave the water system. Strategy 2. Supposing the increment flow rate of FW2 is ΔFFW2, which is a variable to be determined. At each concentration interval above CFW3 (i.e., 80 ppm, 100 ppm, 800 ppm, 850 ppm), the left side of eq 14 is fixed to be 100 t/h, and the only variable on its right side is ΔFFW2. Note that ΔFFW1 and ΔFFW2 are equal to 20 t/h and 40 t/h, respectively. Next a series of ΔFFW2 values (i.e., 0 t/h, 2.67 t/h, -99.1 t/h, and -102.2 t/h) can be back-calculated via solving eq 14 FFW3 ¼

Δmcum - FFW1 ðCFW3 - CFW1 Þ - ðFFW2 þ ΔFFW2 ÞðCFW3 - CFW2 Þ k Ck - CFW3 - FFW1 - ðFFW2 þ ΔFFW2 Þ

" CFW3 < Ck e Carbitrary

ð14Þ

The maximum value 2.67 t/h locates the minimum increment flow rate ΔFFW2. If ΔFFW2 is less than 2.67 t/h, the limited 100 t/ h of FW3 is not sufficient for fulfilling the requirement at the Region 3. However, if ΔFFW2 is more than 2.67 t/h, the targeted flow rate of FW3 will be less than the limited value, and it will not be cost effective because the prioritized cost for FW2 is more than that for FW3. Adding the minimum increment flow rate 2.67 t/h, the target for FW2 is increased from 40 t/h to 42.67 t/h. As shown in Figure 3, the water supply line with multiple resources considering the flow rate constraint for FW3 based

on strategy 2 is constructed. It is worth mentioning that the second segment of the water supply line with its inverse slope equal to 62.67 t/h (20 t/h þ 42.67 t/h) moves right horizontally from the original pinch point B in Figure 1 to point B0 in Figure 3. The targeted values are 20 t/h for FW1, 42.67 t/h for FW2, and 100 t/h for FW3 (as shown in Table 6), which are in agreement with those reported in the literature.22 On the basis of the assumed cost, the operating costs (Costopt) for several scenarios are calculated by solving eq 15 as shown in Table 7 Costopt ¼

∑m CostFWm FFWm

ð15Þ

If only FW1 is used to fulfill the system, FW1 is targeted as 92 t/h, and the related operating cost is 92 $/h. With the introduction of FW2 (96 t/h), the target of FW1 is reduced sharply from 92 t/h to 20 t/h, and the operating cost is decreased from 92 to 68 $/h. The introduction of FW3 (105 t/h) would reduce the operation cost to 61 $/h, but the total flow rate is increased from 116 to 165 t/h. According to the flow rate limit for FW3 (no more than 100 t/h), the target of FW2 is increased a little from 40 to 42.67 t/h, and the related operating cost is increased slightly from 61 to 61.335 $/h. Step 8: Identify wastewater streams discharged from the system. After all the freshwater flow rates are targeted, wastewater streams need to be identified for further regeneration or treatment. The identification of individual waste streams serves as a good guideline in identifying streams for water regeneration (for reuse/recycle) as well as for final treatment (for discharge).19 For example 1, there are two available strategies for achieving the flow rates constraints on certain resources. Consequently, the approaches for identifying wastewater streams for strategies 1 and 2 are slightly different. However, the results are the same. Wastewater stream identification based on strategy 1: The accumulated flow rates for FW1 and FW2 is 62.67 t/h (20 t/h þ 42.67 t/h) at concentration 100 ppm. Above the concentration 100 3727



ARTICLE

Table 6. Targeting for Multiple Water Resources with Flow Rate Constraint for FW3 Based on Strategy 2 and Wastewater Stream Identification (Example 1) net flow

cumulative

flow rate

flow rate

flow rate

flow rate

flow rate

wastewater

concentration

rate

net load

load

for FW1

for FW2

for FW3

above pinch

above pinch

streams

(ppm)

(t/h)

(kg/h)

(kg/h)

(t/h)

(t/h)

(t/h)

P (t/h)

Q (t/h)

(t/h)

CFW2 = 25

20

0.5

0.5

50

20

0.5

1

CFW3 = 60 80

160 160

1.6 3.2

2.6 5.8

100

170

3.4

9.2

FFW3 = 100

800

50

35

44.2

-6.58

FP = 50

(850)

(10)

(0.5)

(44.7)

(-9.49)

47.33

CFW1 = 0

0 FFW1 = 20 FFW2 = 42.7 92.67 FWW1 = 112.67 FWW2 = 40 FQ = 10

Table 7. Operating Costs for Different Scenarios scenarios

FW1 (t/h)

FW2 (t/h)

FW3 (t/h)

total Flow rate (t/h)

operating cost ($/h)

only FW1 FW1 þ FW2

92 20

96

-

92 116

92 68

FW1 þ FW2 þ FW3

20

40

105

165

61

FW1 þ FW2 þ FW3 (flow rate limit)

20

42.67

100

162.67

61.335

ppm, the net flow rate is -50 t/h as shown in Table 5, and the limiting composite curve in Figure 2 directs left, which means 50 t/ h of the surplus water sources exist at a concentration of 100 ppm. Hence, the wastewater stream at 100 ppm (WW1) is summed to be 112.67 t/h (62.67 t/h þ 50 t/h). At the concentration 800 ppm, 40 t/h of the process source (SR3) exists, which makes the net flow rate shift from -50 to -90 t/h as shown in the second column in Table 5. Then 40 t/h of the process source (SR3) at 800 ppm is identified as a wastewater stream (WW2). The details for wastewater streams are marked in Figure 2. Wastewater stream identification based on strategy 2: The formula (eq 16) for calculating the required flow rate above each pinch and the approach for determining the surplus flow rate at each pinch in the literature30 is adopted here for wastewater stream identification. Above the pinch (point P in Figure 3) related to FW3 (100 ppm), the accumulated flow rate is 162.67 t/h. It can be considered as an internal water source with a concentration of 100 ppm. Then, for each concentration above 100 ppm, the required flow rates can be calculated via eq 16 with instead of CPinch and Δmcum CP and Δmcum P Pinch. All possible flow rates for FP are listed in the eighth column of Table 6, and the maximum value (50 t/h) determines the target. Therefore, only 50 t/h of the water source at 100 ppm needs distributed to the system, and the residual flow rate 112.67 t/h (162.67 t/h - 50 t/h) is identified as the wastewater stream WW1 (100 ppm) FPinch ¼

Δmk cum - ΔmPinch cum Ck - CPinch

" CPinch < Ck e Carbitrary ð16Þ

Similarly, above the pinch (point Q in Figure 3), the required flow rates can be calculated via eq 16 with CQ and Δmcum Q instead of CPinch and Δmcum Pinch. All possible flow rates for FQ are listed in the ninth column of Table 6, and the maximum value (10 t/h) locates the target. Therefore, only 10 t/h of the internal water source (800 ppm) needs allocated to the system, and the residual flow rate 40 t/h (50 t/h - 10 t/h) is identified as the wastewater

stream WW2 (800 ppm). The identified wastewater streams are identical to those in the literature.22 The average concentration for two wastewater streams is calculated as 283.4 ppm [= (112.7 t/h 100 ppm þ40 t/h 800 ppm)/152.7 t/h)]. Figure 3 shows the water supply line with identified wastewater streams. Figure 4 shows an optimal water network that shows that the targets can be constructed via the proposed nearest neighbors algorithm (NNA).14 The synthesized water network is exactly the same as that in the literature.22 Example 2—Regeneration Reuse/Recycle Water Network. After the maximum water recovery potential is exhausted via reuse/recycle, the freshwater consumption can be further reduced by partially treating the process effluent (often known as regeneration or interception) for further recovery. According to Wang and Smith,9 regeneration processes may be extensively classified as fixed outlet concentration (CR,out) and fixed removal ratio (RR). For the former, the performance is rated on the basis of the outlet concentration of the regeneration unit (CR,out),31-33 while for the latter, the regeneration unit is evaluated on the basis of the percentage of mass load removal. In this work, only the regeneration unit with a fixed CR,out is taken into consideration. The regenerated water can be treated as a new water resource to fulfill the requirement of the water sinks. Example 2 in the literature26 is slightly modified, and the related data is given in Table 8. The water network with a regeneration reuse/recycle scheme is illustrated for the applicability of the improved problem table. As presented previously, the regenerated water (FR) can be considered as a new resource. With FR plus two other water resources, FW1 and FW2, the approach for targeting a water network with multiple resources can be applied for a regeneration reuse/recycle water network. Repeating steps 1-4, the cumulative load is calculated as shown in the forth column of Table 9. The concentrations in the first column can be plotted against the calculated cumulative load to construct the limiting composite curve as shown in Figure 5. In step 5, without considering FW2 and FR, the target for FW1 is determined as 3728



ARTICLE

Figure 4. Optimal water network for example 1 with three water resources (unit for the flow rate is t/h, and unit for contaminant concentration is ppm in parentheses).

Table 8. Process Source and Sink Data and Resource Specifications for Example 226 process

a

limiting inlet flow

limiting outlet flow

Flim SKj

Flim SRi

rate,

(t/h)

rate,

maximum inlet concentration, Cmax SKj

(t/h)

(ppm)

outlet concentration,

costa

CSRi (ppm)

($/h)

reactor cyclone

80 50

20 50

100 200

1000 700 100

filtration

10

40

0

steam system

10

10

0

10

cooling system

15

5

10

100

total

165

125

FW1

to be determined

0

1

FW2 FR

to be determined to be determined

20 200

0.5 0.15

The cost data are assumed values.

90.64 t/h, and the corresponding pinch concentration is 700 ppm. By solving eq 10, the calculated prioritized cost for FW2 is less than that for FW1 (0.0007 < 0.0014 $/t) as shown in Table 10. Thus, the introduction of FW2 is beneficial. Without considering FR, the flow rate for FW2 can be targeted as 70.147 t/h at 700 ppm. Table 10 shows the calculated prioritized cost for FR is less than that for FW2 (0.0003 < 0.0007 $/t). Thus, it is cost advantageous to introduce FR. After step 6, the targets for FW1, FW2, and FR are determined as 22.5, 21.94, and 65.56 t/h, respectively. The corresponding calculations are shown in Table 9 in detail. As shown in Figure 5, the segments of the water supply line are constructed on the basis of the targeted results, and the related Region 1, 2, and 3 restrict the flow rate targets for FW1, FW2, and FR, respectively. The operating costs for different scenarios are determined via solving eq 15 and are summarized in Table 11. With the introduction of FW2 (70.147 t/h), the operating cost is reduced from 90.64 to 57.57 $/h, and the total flow rate is increased slightly from 90.64 to 92.65 t/h. The introduction of FR decreased the total

flow rate for water resources to 44.44 t/h, and the corresponding operating cost is decreased to 43.31 $/h. Step 7 is skipped for example 2 because of no flow rate constraints for any resource. On the basis of step 8 for wastewater stream identification, the necessary flow rates above pinch P and pinch Q are determined via solving eq 16 as 60 and 40 t/h (as shown in Table 9), respectively. However, the accumulated flow rate at pinch P (700 ppm) is 110 t/h and the wastewater stream at 700 ppm (WW1) is identified as 50 t/h (110 t/h - 60 t/h). Similarly, the wastewater stream at 1000 ppm WW2) is located as 20 t/h (60 t/h - 40 t/h) as shown in Table 9. The identified wastewater streams are shown in Figure 5. Next, for the regeneration reuse/recycle water network, the identified wastewater streams must be sent to a regeneration unit for quality upgrading. Step 9: Determine the minimum regeneration load and the corresponding minimum regeneration concentration. For the fixed CR,out problem, the CR,out is a given value, and the minimum regeneration flow rate (FFR) has been determined 3729



ARTICLE

Table 9. Improved Problem Table for Regeneration Reuse/Recycle Water Network with Multiple Resources (Example 2)

concentration

net flow rate

(ppm)

net load

cumulative load

FW1

FW2

(t/h)

(kg/h)

(kg/h)

(t/h)

(t/h)

10

20

0.2

0.2

20

20

25

0.25

0.45

FFW1 = 22.5

flow rate

flow rate

FR

regeneration concentration

above pinch

above pinch

wastewater streams

(t/h)

(ppm)

P (t/h)

Q (t/h)

(t/h)

0

0

100

25

2

2.45

24.5

2.50

200 700

60 110

6 55

8.45 63.45

42.25 90.64

FFW2 = 21.94 70.15

FFR = 65.56

700

1000

60

18

81.45

81.45

60.15

46.81

CR,in = 771.2

FP = 60

(1200)

(40)

(8)

(89.45)

74.54

52.92

36.56

757.6

52

FWW1 = 50 FWW2 = 20 FQ = 40

Figure 5. Optimal water supply line with multiple resources and regeneration flow rate (example 2).

Table 10. Prioritized Costs for Water Resources pinch

Table 11. Operating Costs for Different Scenarios total Flow

prioritized

water

concentration

concentration

cost

cost

resources

(ppm)

(ppm)

($/t)

($/t)

rate for FW1

FW2

FR

water resources

operating

scenarios

(t/h)

(t/h)

(t/h)

(t/h)

cost ($/h)

FW1

0

700

1

0.0014

FW2

20

700

0.5

0.0007

only FW1

90.64

FR

200

700

0.15

0.0003

FW1 þ FW2

22.5

70.147

FW1 þ FW2 þ FR 22.50

21.94

before this step. The regeneration load (ΔmR) can be calculated by eq 17 ΔmR ¼ FFR ðCR, in - CR, out Þ

ð17Þ

where CR,in denotes the regeneration concentration. On the basis of eq 17, it is obvious that the lower regeneration concentration causes the lower regeneration load. For this example, 50 t/h of

65.56

90.64

90.64

92.65

57.57

44.44

43.31

WW1 (700 ppm) is first sent for regeneration. Because the flow rate of WW1 is less than the targeted regeneration flow rate, an extra 15.56 t/h (65.56 t/h - 50 t/h) of WW2 (1000 ppm) is required for regeneration. The mean inlet concentration for these streams is then determined as 771.2 ppm (CR,in), which leads to the minimum regeneration mass load of 37.446 kg/h 3730



ARTICLE

(ΔmR) determined by eq 17. The residual 4.44 t/h of WW2 (1000 ppm) is the final wastewater stream after considering regeneration scheme. Furthermore, the general formulas for calculating the minimum regeneration flow rate and minimum regeneration concentration in the literature32 are extended here for water networks with multiple resources. A series of regeneration flow rates can also be calculated by eq 18 as shown in the seventh column in Table 9, and the maximum value (65.65 t/h) locates the target, which is the same as that determined earlier. Note that at each concentration level (Ck), only resources with their concentrations below it can be included in eq 18 to make sure the term (Ck - CFWm) is positive. For this example, all the resources with their given concentrations lower than CR,out are included in eq 18 when CR,out < Ck e Carbitrary m

FFR ¼

Δmk cum -

∑ FFWm ðCk - CFWm Þ m¼1

as shown in Figure 8 is referred to illustrating the application of the improved problem table for targeting the property-based water network with a pretreatment system. As shown in Figure 8, 4082 t/h of municipal fresh water is treated with a pretreatment system to generate ultra pure water (UPW) for the use in the fabrication (FAB) process, which includes four sections, i.e., “wet,” “lithography,” “CMP” (combined chemical and mechanical processing), and “etc.” (other miscellaneous processes). The water pretreatment system consists of three main elements: ultrafiltration (UF), reverse osmosis (RO), and deionization (DI). UF is used to retain the solute of high molecular weight contaminants in the municipal fresh water, while RO and DI processes are used to remove the ions that solute in the water and deionize the water in order to generate ultra pure water (UPW). Besides, the cleaning process, cooling tower makeup, and scrubber need an additional supply of municipal fresh water. Note that the wet and CMP sections

Ck - CR, out

" CR, out < Ck e Carbitrary

ð18Þ

Similarly, a series of regeneration concentrations can be determined by eq 19 as shown in eighth column in Table 9, and the maximum value (771.2 ppm) represents the targeted regeneration concentration, which is identical to the mean regeneration concentration (771.2 ppm) obtained previously m

CR, in ¼

Δmk cum -

∑ FFWm ðCk - CFWmÞ m¼1 FFR

" CR, out < Ck e Carbitrary

þ CR, out ð19Þ

In order to provide a clear description on the proposed algorithm, a step-by-step procedure for the IPT approach is summarized as shown in Figure 6. On the basis of the targets, the optimal regeneration reuse/ recycle water network with multiple resources is constructed as shown in Figure 7 via the proposed NNA.14 Example 3—Placement of Pretreatment System. A pretreatment system is commonly used in the process industries to treat the fresh water for required purities before it may be used in the process (i.e., boiler). An industrial wafer fabrication process47

Figure 6. Detail flowchart for the overall IPT approach.

Figure 7. Optimal regeneration reuse/recycle water network for example 2 with multiple resources. 3731



ARTICLE

Figure 8. Schematic diagram for water fabrication process47 (example 3).

Table 12. Limiting Data for Example 347 property operator, ψ (MΩ-1)

process

flow rate (t/h)

sink SK1

wet

500

SK2

lithography

SK3

CMP

SK4

etc.

SK5

cleaning

SK6

cooling tower makeup

450

0.02

0.05

20

50

SK7

scrubber

300

0.01

0.02

50

100

SR1

wet I

250

1

1

SR2

wet II

200

2

0.5

SR3

lithography

350

3

0.3333

SR4

CMP I

300

0.1

10

SR5

CMP II

200

2

0.5

SR6

etc.

280

0.5

2

SR7 SR8

cleaning scrubber

180 300

0.002 0.005

500 200

FW1

ultra pure water

to be determined

2

18

0.0556

FW2

municipal fresh water

to be determined

1

0.02

50

FW3

UF reject

30% inlet flow rate of UF

0.01

100

FW4

ro reject

30% inlet flow rate of RO

0.005

200

costa ($/t)

resistivity, R (MΩ) lower bound 7

upper bound 18

lower bound 0.0556

upper bound 0.1429

450

8

15

0.0667

0.1250

700

10

18

0.0556

0.1

350

5

12

0.0833

0.2

200

0.008

0.01

100

125

source

a

Note: The cost data are adopted from the literature.37

generate two wastewater streams with different water quality levels. In this example, the resistivity (R) is determined as the most significant water quality factor, and it constitutes an index of the total ionic content of aqueous streams. The general mixing rule for resistivity is defined as below38 1 ¼ R

∑i RxSRi

SRi

ð20Þ

where RSRi and R denotes the resistivity properties on the source (SRi) and the mixture, respectively. The property operator for resistivity can be expressed as follows 1 ð21Þ R According to eq 21, the highest operator demonstrates the lowest resistivity corresponding to the lowest quality level. The resistivity values of all sources, sinks, and resources are transformed ψðRÞ ¼

3732



ARTICLE

Table 13. IPT for Targeting Property-Based Water Network with Pre-Treatment System and Wastewater Stream Identification property

net flow

net

cumulative

operator, Ψ (MΩ-1)

rate (t/h)

load (t h-1 MΩ-1)

load (t h-1 MΩ-1)

0.1

0

0

0

0

0.1250

700

17.5

17.50

252.16

0.1429

1150

20.585

38.09

436.25

0.2

1650

94.215

132.30

916.20

0.3333 0.5

2000 1650

266.6 275.055

398.90 673.96

1436.44 FFW1 = 1516.55

1

1250

625

1298.96

1375.43

FP = 1249.99

FW1 (t/h)

flow rates

flow rate

flow rate

flow rate

wastewater

for FW2, FW3, FW4 (t/h)

above pinch P (t/h)

above pinch Q (t/h)

above pinch H (t/h)

streams (t/h)

0

0.0556

FWW1 = 266.56 FWW2 = 207.79

2

1000

1000

2298.96

1182.35

1083.33

10

720

5760

8058.96

810.40

777.37

751.11

50

420

16800

24858.96

497.73

FFW2 = 3095

488.59

480.82

100

870

43500

68358.96

683.97

FFW3 = 928.5

680.25

677.37

125

1170

29250

97608.96

781.22

200 500

1370 1070

102750 321000

200358.96 521358.96

1002.07 1042.83

(550)

(890)

(44500)

(565858.96)

(1028.94)

FFW4 = 649.95

1000.00

778.59

776.69

1000.93 1042.41

1000.30 FQ =1042.2

(1028.54)

(1028.34)

FWW3 = 152.2 FH =890

Figure 9. Limiting composite curve and water supply line with wastewater stream identification (example 3).

to the related operator values. Those limiting data are summarized in Table 12, and the unit costs for municipal fresh water and UPW adopted from the literature37 are given as 1 and 2 $/t, respectively. In this example, the upper bounds of the property operator for resistivity are selected as the limiting values for process sink when a water recovery scheme is considered. Because these upper bounds correspond to the lowest stream quality that can be tolerated by the processes, the potential for reuse and recycling is maximized. According to the assumption in the literature, the permeation coefficient (R) for UF or RO is 70%. That means that 70% of the

inlet flow rate of UF or RO passes through the membrane as permeate, and the other 30% is rejected as wastewater with a constant water quality. The recovery of UR or RO reject may reduce the freshwater consumption. Hence, apart from UPW specified as first resource (FW1), the municipal freshwater, UR reject, and RO reject are denoted as second, third, and fourth resources (FW2, FW3, FW4), respectively. The corresponding property operator for each resource is specified in the Table 12. In addition, the flow rates for FW1, FW3, and FW4 meet the following relationships specified by eq 22 with the given 3733



Figure 10. Optimal water network for wafer fabrication process (example 3).

ARTICLE

3734



ARTICLE

Figure 11. Optimal water recovery scheme for wafer fabrication process (example 3) in the literature47.

permeation coefficient (R) FFW3 ¼

1-R FFW1 R2

ð22Þ 1-R FFW1 FFW4 ¼ R If no additional municipal freshwater is reused in the system and all the municipal freshwater is used to generate UPW, the relationship between FW1 and FW2 is specified by eq 23 FFW1 ð23Þ R2 Therefore, the targeting procedures for the water network with multiple resources in example 1 can be applied here. Repeating steps 1-6 with the property operator ψ replacing concentration C, the calculation procedures are shown in detail in Table 13. The maximum value in the fifth column in Table 13 locates the target for FW1 as 1516.55 t/h, and the corresponding pinch is 0.5 MΩ-1. However, the property operators for FW2, FW3, and FW4 are 50 MΩ-1, 100 MΩ-1, and 200 MΩ-1, respectively, which are above the pinch property operator 0.5 MΩ-1. The introduction of resources above the pinch does not reduce the purest resource.22 It implies that the use of additional FW2 and recovery of FW3 and FW4 will not reduce the consumption of FW1. Next, the flow rate relationships between FW1, FW2, FW3, and FW4 have to be considered. Going to step 7, on the basis of the specified relationships in eqs 22 and 23 with the permeation coefficient R equal to 70%, the flow rate targets for FW2, FW3, and FW4 are determined as 3095, 928.5, and 649.95 t/h, which are identical to the results in the literature.47 On the bais of the given cost data, the operating cost is calculated via eq 15 as 6128.1 $/h (1516.55 t/h 2 $/h þ 3095 t/h 1 $/h). As shown in Table 13, the required flow rate above 0.5 MΩ-1 is determined as 1249.99 t/h (the maximum value in the seventh FFW2 ¼

column). The accumulated flow rate of the supply source at 0.5 MΩ-1 is targeted as 1516.55 t/h. At 0.5 MΩ-1, the surplus wastewater 266.56 t/h (1516.55 t/h - 1249.99 t/h) is identified as a wastewater stream (WW1). Similarly, 207.79 t/h of the wastewater stream at 1 MΩ-1 (WW2) and 152.2 t/h of the wastewater stream at 500 MΩ-1 (WW3) are determined, and the details for wastewater streams are marked at points P, Q, and H in Figure 9. Without considering the recovery of FW3 and FW4, they are labeled as two other wastewater streams WW4 and WW5, respectively. Figure 10 shows the optimal water recovery scheme for water fabrication process without considering recovery of UF and RO rejects, obtained via the NNA.14 In addition, if the UF and RO rejects are considered in the network design along with reuse of all the flow rates of SR2 and SR5, another optimal water recovery scheme for the water fabrication process as shown in Figure 11 can be constructed on the basis of NNA,14 which is identical to the result in the literature.47

’ CONCLUSION 1 On the basis of the extension of CTA , this paper presents an improved problem table (IPT) approach to target the conventional and property-based water networks with multiple water resources. The constructed limiting composite curve and water supply line reveals basic ideas of IPT. In addition, the concept of prioritized cost22 is adopted to select the cost advantageous resources for minimizing the operating cost. The improved approach is applicable to several scenarios, i.e., direct reuse/ recycle, regeneration reuse/recycle, and placement of pretreatment systems. To handle flow rate constraints for certain resources, two strategies are presented, and the related wastewater stream identification techniques are introduced successively. Three literature examples are used to illustrate the applicability of IPT. In the first case study, the general steps for targeting a water network with multiple resources are proposed in example 1. The 3735


Industrial & Engineering Chemistry Research regenerated water in example 2 can be considered as a new resource, and the steps can be applied to example 2 directly. In addition, the general formulas32 for calculation of optimal regeneration flow rate and regeneration concentration are extended for water networks with multiple resources. Finally, for the property-based water network with a pretreatment system in example 3, UPW, municipal freshwater, UF reject, and RO reject are considered as multiple resources with flow rate constraints, and the corresponding targets can be determined via the general steps. In addition, the proposed IPT approach is not only restricted to water network targeting but also can be utilized to target other resource conservation networks (i.e., hydrogen network). It can be easily handled via Excel or other spreadsheet software.

’ AUTHOR INFORMATION Corresponding Author

*Tel.: þ86-10-8973 3991. Fax: þ86-10-6972 4721. E-mail: [email protected].

’ ACKNOWLEDGMENT Financial support provided by the National Natural Science Foundation of China under Grant 20936004 is gratefully acknowledged. Sponsorship from China Scholarships Council (2009628067) is also deeply appreciated. We thank Younas Dadmohammadi for his valuable comments. ’ NOTATION R = permeation coefficient of UF or RO process CFWm or CFWn = concentration of mth resource or nth resource CSRi = outlet concentration of ith process source CSKj = inlet concentration of jth process sink Cmin SKj = minimum inlet concentration of jth process sink Cmax SKj = maximum inlet concentration of jth process sink Ck or Ct = concentration for kth row or tth row Carbitrary = concentration for the arbitrary concentration in the final row CR,in = regeneration concentration or inlet regeneration concentration CR,out = postregeneration concentration or outlet regeneration concentration COD = chemical oxygen demand CostFWm = unit cost for mth resource = prioritized cost for mth resource CostPrioritized FWm Costopt = operating cost CTA = composite table algorithm FC = fixed contaminant load FF = fixed flow rate FFWm = targeted flow rate for mth resources FFR = regeneration flow rate Flim SRi = limiting flow rate of ith process source Flim SKj = limiting flow rate of jth process sink Fk = net flow rate for kth row FPinch = necessary flow rate beyond certain pinch, i.e., pinch points P, Q and H FWW = flow rate of wastewater stream IPT = improved problem table NFW = set of resources or external sources NSK = set of process sources NSR = set of process sources

ARTICLE

NNA = nearest neighbors algorithm PFWm = property of rth resource PSRi = property of ith process source PSKj = inlet property of jth process sink Pmin SKj = minimum property of jth process sink Pmax SKj = minimum property of jth process sink R = resistivity of water SCC = source composite curve xi = fractional contribution of ith source toward the total flow rate of mixture WCA = water cascade analysis ψ(PSKj) = property operator on the sink property (PSKj) ψmin(PSKj) = minimum property operator on the sink property (PSKj) ψmax(PSKj) = maximum property operator on the sink property (PSKj) ψ(PSKj) = property operator on the source property (PSRi) ψ(P) = property operator on the mixture property (P) ψ(R) = property operator on the resistivity Δmk = net load for kth row = cumulative load for kth row Δmcum k Δmcum Pinch = cumulative load at certain pinch ΔmR = load of regeneration process

’ REFERENCES (1) Agrawal, V.; Shenoy, U. V. Unified conceptual approach to targeting and design of water and hydrogen networks. AIChE J. 2006, 52, 1071–1082. (2) Bagajewicz, M. A review of recent design procedures for water networks in refineries and process plants. Comput. Chem. Eng. 2000, 24, 2093–2113. (3) Foo, D. C. Y. State-of-the-art review of pinch analysis techniques for water network synthesis. Ind. Eng. Chem. Res. 2009, 48, 5125–5159. (4) Jezowski, J. Review of water network design methods with literature annotations. Ind. Eng. Chem. Res. 2010, 49, 4475–4516. (5) Mann, J.; Liu, Y. Industrial Water Reuse and Wastewater Minimization; McGraw-Hill Professional: New York, 1999. (6) Smith, R. Chemical Process Design and Integration, 2nd ed.; Wiley: Hoboken, NJ, 2005. (7) Hallale, N. A new graphical targeting method for water minimisation. Adv. Environ. Res. 2002, 6, 377–390. (8) Manan, Z. A.; Tan, Y. L.; Foo, D. C. Y. Targeting the minimum water flow rate using water cascade analysis technique. AIChE J. 2004, 50, 3169–3183. (9) Wang, Y. P.; Smith, R. Wastewater minimisation. Chem. Eng. Sci. 1994, 49, 981–1006. (10) El-Halwagi, M. M.; Manousiouthakis, V. Synthesis of mass exchange networks. AIChE J. 1989, 35, 1233–1244. (11) Dhole, V. R.; Ramchandani, N.; Tainsh, R. A.; Wasilewski, M. Make your process water pay for itself. Chem. Eng. 1996, 103, 100–103. (12) Polley, G. T.; Polley, H. L. Design better water networks. Chem. Eng. Prog. 2000, 96, 47–52. (13) 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, 4319–4328. (14) Prakash, R.; Shenoy, U. V. Targeting and design of water networks for fixed flowrate and fixed contaminant load operations. Chem. Eng. Sci. 2005, 60, 255–268. (15) Bandyopadhyay, S.; Ghanekar, M. D.; Pillai, H. K. Process water management. Ind. Eng. Chem. Res. 2006, 45, 5287–5297. (16) Foo, D. C. Y.; Manan, Z. A.; Tan, Y. L. Use cascade analysis to optimize water networks. Chem. Eng. Prog. 2006, 102, 45–52. (17) Alwi, S. R. W.; Manan, Z. A. Targeting multiple water utilities using composite curves. Ind. Eng. Chem. Res. 2007, 46, 5968–5976. 3736


Industrial & Engineering Chemistry Research (18) Foo, D. C. Y. Water cascade analysis for single and multiple impure fresh water feed. Chem. Eng. Res. Des. 2007, 85, 1169–1177. (19) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R. Targeting for total water network. 1. Waste stream identification. Ind. Eng. Chem. Res. 2007, 46, 9107–9113. (20) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R. Targeting for total water network. 2. Waste treatment targeting and interactions with water system elements. Ind. Eng. Chem. Res. 2007, 46, 9114–9125. (21) 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, 1253–1267. (22) Shenoy, U. V.; Bandyopadhyay, S. Targeting for multiple resources. Ind. Eng. Chem. Res. 2007, 46, 3698–3708. (23) Alwi, S. R. W.; Manan, Z. A. Generic graphical technique for simultaneous targeting and design of water networks. Ind. Eng. Chem. Res. 2008, 47, 2762–2777. (24) Bandyopadhyay, S.; Cormos, C. C. Water management in process industries incorporating regeneration and recycle through a single treatment unit. Ind. Eng. Chem. Res. 2008, 47, 1111–1119. (25) Foo, D. C. Y. Flowrate targeting for threshold problems and plant-wide integration for water network synthesis. J. Environ. Manage. 2008, 88, 253–274. (26) Wang, Y. P.; Smith, R. Wastewater minimization with flowrate constraints. Chem. Eng. Res. Des. 1995, 73, 889–904. (27) 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, 294–305. (28) Almutlaq, A. M.; El-Halwagi, M. M. An algebraic targeting approach to resource conservation via material recycle/reuse. Int. J. Environ. Pollut. 2007, 29, 4–18. (29) Kuo, W. C. J.; Smith, R. Design of water-using systems involving regeneration. Process Saf. Environ. Prot. 1998, 76, 94–114. (30) Castro, P.; Matos, H.; Fernandes, M. C.; Nunes, C. P. Improvements for mass-exchange networks design. Chem. Eng. Sci. 1999, 54, 1649–1665. (31) Bai, J.; Feng, X.; Deng, C. Graphically based optimization of single-contaminant regeneration reuse water systems. Chem. Eng. Res. Des. 2007, 85, 1178–1187. (32) Feng, X.; Bai, J.; Zheng, X. S. On the use of graphical method to determine the targets of single-contaminant regeneration recycling water systems. Chem. Eng. Sci. 2007, 62, 2127–2138. (33) Deng, C.; Feng, X.; Bai, J. Graphically based analysis of water system with zero liquid discharge. Chem. Eng. Res. Des. 2008, 86, 165– 171. (34) Deng, C.; Feng, X. Optimal Water Network with Zero Wastewater Discharge in an Alumina Plant. In Proceedings of the 4th IASME/ WSEAS International Conference on Energy & Environment, World Scientific and Engineering Academy and Society (WSEAS): Cambridge, U.K., 2009; pp 109-114. (35) Bai, J.; Feng, X.; Deng, C. Optimal design of single-contaminant regeneration reuse water networks with process decomposition. AIChE J. 2010, 56, 915–929. (36) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R. Automated targeting technique for single-impurity resource conservation networks. Part 2: Single-pass and partitioning waste-interception systems. Ind. Eng. Chem. Res. 2009, 48, 7647–7661. (37) Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R.; El-Halwagi, M. Automated targeting technique for concentration- and property-based total resource conservation network. Comput. Chem. Eng. 2010, 34, 825– 845. (38) El-Halwagi, M. Process Integration; Elsevier: San Diego, 2006. (39) 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, 2081–2091. (40) El-Halwagi, M. M.; Glasgow, I. M.; Qin, X. Y.; Eden, M. R. Property integration: Componentless design techniques and visualization tools. AIChE J. 2004, 50, 1854–1869.

ARTICLE

(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) Eljack, F. T.; Abdelhady, A. F.; Eden, M. R.; Gabriel, F. B.; Qin, X. Y.; El-Halwagi, M. M. Targeting optimum resource allocation using reverse problem formulations and property clustering techniques. Comput. Chem. Eng. 2005, 29, 2304–2317. (43) Grooms, D.; Kazantzi, V.; El-Halwagi, M. Optimal synthesis and scheduling of hybrid dynamic/steady-state property integration networks. Comput. Chem. Eng. 2005, 29, 2318–2325. (44) Kazantzi, V.; El-Halwagi, M. M. Targeting material reuse via property integration. Chem. Eng. Prog. 2005, 101, 28–37. (45) Foo, D. C. Y.; Kazantzi, V.; El-Halwagi, M. M.; Manan, Z. A. Surplus diagram and cascade analysis technique for targeting propertybased material reuse network. Chem. Eng. Sci. 2006, 61, 2626–2642. (46) Ng, D. K. S.; Foo, D. C. Y.; Rabie, A.; Ei-Halwagi, M. M. Simultaneous synthesis of property-based water reuse/recycle and interception networks for batch processes. AIChE J. 2008, 54, 2624– 2632. (47) 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. (48) Ponce-Ortega, J. M.; Hortua, A. C.; El-Halwagi, M.; JimenezGutierrez, A. A property-based optimization of direct recycle networks and wastewater treatment processes. AIChE J. 2009, 55, 2329–2344. (49) Napoles-Rivera, F.; Ponce-Ortega, J. M.; El-Halwagi, M. M.; Jimenez-Gutierrez, A. Global optimization of mass and property integration networks with in-plant property interceptors. Chem. Eng. Sci. 2010, 65, 4363–4377. (50) Ponce-Ortega, J. M.; El-Halwagi, M. M.; Jimenez-Gutierrez, A. Global optimization for the synthesis of property-based recycle and reuse networks including environmental constraints. Comput. Chem. Eng. 2010, 34, 318–330.

3737


Targeting for Conventional and Property-Based Water Network with

Recommend Documents