Improved Ternary Diagram Approach for the Synthesis of a Resource

Article pubs.acs.org/IECR

Improved Ternary Diagram Approach for the Synthesis of a Resource Conservation Network with Multiple Properties. 2. Regeneration Reuse/Recycle Chun Deng,*,† Zengkun Wen,‡ Dominic Chwan Yee Foo,§ Denny Kok Sum Ng,§ and Xiao Feng∥ †

State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum, 18 Fuxue Road, Changping, Beijing, 102249 China ‡ COOEC-ENPAL Engineering Company, Ltd., 197 Songling Road, Laoshan, 266100 Qingdao, Shandong, China § Department of Chemical and Environmental Engineering, University of Nottingham Malaysia, Broga Road, 43500 Semenyih, Selangor, Malaysia ∥ New Energy Institute, China University of Petroleum, 18 Fuxue Road, Changping, Beijing, 102249 China S Supporting Information *

ABSTRACT: Part 1 of this series of papers proposed an improved ternary diagram for designing a resource conservation network (RCN) with multiple properties with a direct reuse scheme. In this part of the series, an RCN with a regeneration scheme is synthesized via the improved ternary diagram approach. The first literature example presented in part 1 of this series of papers is revisited to illustrate the improved approach. In addition, a sour water system of an operating refinery is illustrated to show the feasibility and effectiveness of the improved approach. For an RCN with three properties, El-Halwagi et al.18 addressed the general problem of allocation of sources and sinks with the interception unit (which will be reviewed in greater detail in section 3). In addition, Qin et al.19 proposed an algebraic approach to locating rigorous targets for an RCN with more than three properties. In addition, mathematical programming approaches have been considered to be powerful tools for the synthesis of RCN with multiple properties involving regeneration reuse/recycle. Ponce-Ortega et al.20 developed a mathematical programming approach to optimize direct recycle/reuse networks with multiple properties (i.e., toxicity, ThOD, pH, color, and odor). The integrated wastewater treatment processes are utilized to meet environmental regulations, and the regeneration reuse/recycle scheme is not explored. Later, the mathematical programming model is updated by the incorporation of property interceptors,21 transformed to be a multiobjective optimization model with economic and environmental objectives,22 and then extended to address the property-based interplant water integration problem.23 Chen et al.24 proposed a general model for the synthesis of property-based RCNs involving regeneration units with batch or continuous processes. In this part of this series of papers, the improved ternary diagram approach is utilized for the synthesis of an RCN with a regeneration reuse/recycle scheme. The literature example introduced in part 1 of this series of papers is revisited to illustrate the improved approach for the synthesis of an RCN with a regeneration reuse/recycle scheme. In addition, the

1. INTRODUCTION Part 1 of this series of papers introduced an improved ternary diagram approach to achieving resource conservation with a direct reuse/recycle scheme. To reduce the rate of consumption of resources further, interception devices are typically introduced to upgrade the quality of process sources with a regeneration reuse/recycle scheme, so that the purified sources may be further utilized. Previously, many approaches have been proposed for the synthesis of a resource conservation network (RCN) with a regeneration reuse/recycle scheme for both water and hydrogen systems. The conventional approaches for the synthesis of a water regeneration network for a single contaminant include both pinch1 and optimization-based approaches.2 However, for a water regeneration network with multiple contaminants, most works are based on the mathematical optimization technique.3−8 The same applies to a hydrogen regeneration network with multiple impurities.9−11 For a property-based RCN with a single property, Kazantzi and El-Halwagi12 introduced the interceptor to reduce the value of the property operator through changes in the operation conditions, and insights into the process modification are illustrated in the property-based materiel reuse pinch diagram. Recently, Deng et al.13 extended the composite table algorithm proposed by Agrawal and Shenoy14 and presented an improved problem table (IPT) to locate the targets of a property-based water network with regeneration reuse/recycle. In addition, Ng et al.15,16 proposed the automated targeting approach to determine the target of conventional and bilateral propertybased RCN16 and the total RCN.15 In addition, an automated targeting model for a single-impurity RCN with a single-pass and partitioning waste interception scheme17 is presented. © XXXX American Chemical Society

Received: April 21, 2014 Revised: August 14, 2014 Accepted: October 16, 2014

A

dx.doi.org/10.1021/ie501628x | Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Industrial & Engineering Chemistry Research

Article

synthesis of a sour water network of an operating refinery is illustrated to show the feasibility and effectiveness of the improved approach.

2. PROBLEM STATEMENT The original problem as described in part 1 of this series of papers remains valid here. However, to reduce the rate of consumption of fresh resources, an interception device is utilized to upgrade the quality of the process sources so that they may be reused/recycled to the process sinks (see Figure 1). Part 2 of this series of papers aims to extend the improved Figure 2. Task identification for the interception system.18

Then the dimensionless property operators (ΩSRi,p) and property operators (ψSRi,p) for SRint can be calculated by solving eqs 4 and 5 in sequence. βSR = i

βSR =

arm for SR i total arm connecting SR i to SR (i + 1) xSR i AUPSR i

i

AUP

∀ i ∈ NSR

(1)

(2)

NSR

AUP =

Figure 1. Schematic representation of the property-based RCN with a regeneration reuse/recycle scheme.

∑ xSR AUPSR i

i=1

CSR i ,p =

ternary diagram approach for the synthesis of an RCN with a regeneration reuse/recycle scheme. The main objective is to determine the minimal extent of regeneration duty needed for the interception device.

ΩSR i ,p =

3. IDENTIFICATION OF THE REGENERATION FLOW RATE VIA AN IMPROVED TERNARY DIAGRAM APPROACH18 To reduce the rate of consumption of fresh resources, properties of the internal source can be adjusted by using an interception device (e.g., separation, reaction, etc.) so that the intercepted sources may be reused/recycled to the sinks. The immediate questions that come to mind are the properties of the process source that the interception device should be altering (and, consequently, the cluster values) and the extent to which they are. As stated in the literature,18 the mixing point (point O in Figure 2) is predefined and the augmented property index (AUP) for point O is unique. The process source (i.e., broke) is totally allocated to the interceptor, and thus, the flow rate of the intercepted process source (SRint) is known. The intercepted process stream (SRint) is mixed with a fresh source and then fed to the process sink. The flow rate of the fresh source can be determined by subtracting the flow rate of SRint from the total flow rate of the process sink. The contribution fraction for fresh source (xFR) can be calculated by the flow rate of the fresh source by dividing the total flow rate of the process sink. Next, the value of fresh arm (βFR) can be determined by solving eq 2. The location of the intercepted process stream (SRint) is determined via drawing the fresh arm on the extended line FRO. The AUP for SRint can be determined by solving eq 3, and cluster values (CSRi,p) for SRint can be read from Figure 2.

i

∀ i ∈ NSR

(3)

ΩSR i ,p AUPSR i

ψSR

(4)

i ,p

ψpref

(5)

However, it is much more common that the properties for SRint be given in advance and the optimal mixing point be determined to achieve the minimal flow rate of the fresh resource. Similarly, the post-regeneration concentrations or intercepted concentrations of contaminants for the wastewater regeneration process are fixed for a typical water network. In this paper, we determine the optimal mixing point and fresh arm (βFR) graphically on the ternary diagram, to synthesize an optimal RCN with a regeneration reuse/recycle scheme.

4. LITERATURE EXAMPLE The literature example18 is revisited to illustrate the procedure for the improved ternary diagram for an RCN with a regeneration reuse/recycle scheme, with its data reported in Tables 6 and 7 in part 1 of this series of papers. When steps 1− 5 in section 5 of part 1 of this series of papers are repeated, the FSR of SK2 is determined as a straight line that is located at the bottom of the ternary diagram, while the internal source SR3 (broke) is out of the FSR of SK2, as shown in Figure 3. Thus, SR3 cannot be reused as SK2 directly. On the other hand, the mixture of SR1 and SR2 (fibers I and II, respectively) would provide the possibility for recovery to SK2. Alternatively, SR3 may be regenerated by an interception device to improve its quality to fulfill the requirement of SK2. However, the intercepted SR3 must be located along the zero-OM cluster line. In other words, the OM content of SR3 should be removed completely. However, it is very costly to remove OM B



Article

Figure 3. Determination of source−sink matching for SK2 via the ternary diagram feasible region (FR) of SK2, the available region (AR) of SR1−SR3, and the feasible solution region (FSR) for SK2.

completely. Hence, the regeneration reuse/recycle scheme is not considered for SK2. The network representation of source−sink matching for SK2 is identical to that in Figure 7b in part 1 of this series of papers. Similarly, we can repeat steps 1−4 to determine the cluster values for vertices of the BFR for SK1. The FR of SK1 and the AR for SR1−SR3 (fiber I−fiber II−broke) can be plotted on another ternary diagram as shown in Figure 4a. Point O as shown in Figure 4a is identified as the optimal mixing point for SR1 (fiber I) and SR3 (broke) for direct reuse/recycle. The flow rates of fresh sources (SR1 and SR2) can be reduced further once the internal source [SR3 (broke)] can be intercepted. To compare the results with those reported in the literature,18 the properties of intercepted source (intercepted SR3) are set to be (0.067, 0.0013, 0.9) (Scheme 1). When steps 3 and 4 are repeated, the clusters for intercepted SR3 are determined to be (0.453, 0.273, 0.274) and are marked on the ternary diagram in Figure 4a. Step 5 next determines that point O is the optimal mixing point (shown in Figure 4a), which is identical to that with a direct reuse/recycle scheme. Moreover, the fractional contribution of SR1 for SK1 is determined to be 0.7. Next, the flow rate of SR1 allocated to SK1 is determined to be 70 t/h (=100 t/h × 0.7), and that of intercepted SR3 that is allocated to SK1 can be determined to be 30 t/h (=100 t/h − 70 t/h). That means all the SR3 (broke) is intercepted. The source−sink allocation structure for SK1 is identified as shown in Figure 4b. The results are identical to those reported in the literature.18 However, the operating conditions for the interceptor can be adjusted, and the properties of the intercepted source would vary. If the properties of SR3 (broke) are interpreted as being the upper bounds of the properties of SK1, the intercepted properties are as follows: OMIntercepted = 0.02, kIntercepted = 0.00125, and R∞Intercepted = 0.90. When steps 3 and 4 are repeated, the intercepted SR3 can be marked in Figure 5a, which is named as Scheme 2. When step 5 is repeated, the flow rates for SR1, SR3, and intercepted SR3 are determined to be 70, 23.33, and 6.67 t/h, respectively. Note that, compared with Scheme 1, the intercepted flow rate for Scheme 2 is reduced

Figure 4. Determination of source−sink matching for SK1 via the ternary diagram with the interception reuse/recycle scheme (Scheme 1). (a) Feasible region (FR) of SK1, available region (AR) of SR1, and intercepted SR3 and feasible solution region (FSR) for SK1. (b) Network representation of source−sink matching for SK1.

from 30 to 6.67 t/h. The source−sink allocation structure for SK1 of this scheme is constructed as shown in Figure 5b. According to the optimal network representation for SK2 (Figure 7b in part 1 of this series of papers) and SK1 (Figure 5b), the optimized papermaking schematic can be illustrated as shown in Figure 6. Via comparison of the original network18 and the optimized network (Figure 6), the total flow rate of the fresh sources (fibers I and II) is reduced from 140 t/h (116.4 t/ h + 23.6 t/h) to 110 t/h (102.4 t/h + 7.6 t/h). This means that a much lower cost for raw materials is achieved, i.e., $24544/h (=102.4 t of fiber I × $210/t + 7.6 t of fiber II × $400/t), instead of $33884/h (=116.4 t of fiber I × $210/t + 23.6 t of fiber II × $400/t) in the original network.18 The flow rates of fresh sources and the total cost of raw materials are identical with the results with the regeneration reuse/recycle scheme reported in the literature.18 Besides, the intercepted flow rate is reduced from 30 to 6.67 t/h, and the operating cost for the interception unit would be sharply reduced. However, the intercepted properties (OMIntercepted = 0.02, kIntercepted = 0.00125, and R∞Intercepted = 0.90) are slightly better than those in the literature (OMIntercepted = 0.067, kIntercepted = 0.0013, and R∞Intercepted = 0.90).18 An interception unit with a greater investment cost would be installed. C



Article

Table 1. Flow Rates and Properties for SW1 and SW2 intercepted water SW1 SW2

nonhydrogenationstripped water hydrogenation-stripped water

wH2S (ppm)

wNH3−N (ppm)

pH

flow rate (t/h)

40

20

7.5

135

90

80

7.2

95

Figure 7. FRs for SK2 and SK3 and points for SR1−SR3 and the blending source.

type of generating processes, the sour water sources shown in Table 13 in part 1 of this series of papers are categorized into two types: nonhydrogenation sour water (SR1−SR3) and hydrogenation sour water (SR4−SR6). The former type of sour water is commonly discharged from nonhydrogenation processes, such as discharge from the crude distillation unit (CDU), fluid catalytic cracking (FCC), and discharge from the delayed coking unit (DCU). The later water is typically generated from hydrotreating (HT) processes, such as those from the diesel hydrotreating unit (DHT), the gasoline hydrotreating unit (GHT), and the wax oil hydrotreating unit (WHT). Typically, two types of sour water are upgraded in two separate strippers and two types of stripped water are generated: nonhydrogenation-stripped water (SW1) and hydrogenation-stripped water (SW2). The maximal flow rates and the qualities for SW1 and SW2 are listed in Table 1. Those two types of stripped water would be reutilized by different

Figure 5. Determination of source−sink matching for SK1 via a ternary diagram with an interception reuse/recycle scheme (Scheme 2). (a) Feasible region (FR) of SK1, available region (AR) of SR1 and SR2, and intercepted source and feasible solution region (FSR) of SK1. (b) Network representation of source−sink matching for SK1.

5. INDUSTRIAL CASE STUDY The industrial case in part 1 of this series of papers is revisited to illustrate the synthesis of an RCN with a regeneration reuse/ recycle scheme. The regeneration reuse/recycle scheme is widely applied through the reutilization of stripped water. The stripping tower is commonly used as the interception device to upgrade the quality of sour water. According to the quality and

Figure 6. Optimized papermaking schematic with an intercepted reuse/recycle scheme. D



Article

Figure 8. FRs for SK2 and SK3 and points for the blending source and SW1.

Figure 10. FRs for SK4−SK7 and points for SR4−SR6 and the blending source.

sinks as shown in Table 14 of part 1 of this series of papers. The sour water system would be divided into two subsystems: nonhydrogenation system (SR1−SR3 and SK1−SK3) and hydrogenation system (SR4−SR6 and SK4−SK7). Two subsystems would be analyzed separately. First, the nonhydrogenation type of sour water network is analyzed. Because SK1 is fulfilled by the mixture of DW and SR1, the stripped water can be recycled to dilute the SR1 and the rate of consumption of DW can be reduced further. However, considering safety and avoiding the buildup of unexpected contaminant in SK1, it is not favorable to recycle stripped water to reduce the rate of consumption of DW. Thus, SK1 is out of scope of reutilization of stripped water. For SK2 and SK3, the available sources are SR1−SR3. The mean values for properties (H2S, NH3−N, and pH) of the available sources (DW, SW1, and SR1−SR3) and the bound properties (H2S, NH3−N, and pH) of SK2 and SK3 are calculated as 1161.43, 665.71, and 7.94, respectively. They are selected as the reference properties, and the following values of their property operators are determined: ψH2S = 1161.43, ψNH3−N = 665.71, and ψpH = 107.94−14. When steps 1−4 are repeated, the FRs for

Figure 11. FRs for SK4−SK7 and points for the blending source and SW2.

Figure 9. Optimal network for the nonhydrogenation sour water system. E



Article

Figure 12. Determination of source−sink matching for SK6 via the ternary diagram. (a) Feasible region (FR) of SK6 and point for SW2. (b) Network representation of source−sink matching for SK6.

Figure 14. Determination of source−sink matching for SK7 via the ternary diagram. (a) Feasible region (FR) of SK7, available region (AR) of DW-SW2, and feasible solution region (FSR) of SK7. (b) Network representation of source−sink matching for SK7.

system. The schematic diagram for the nonhydrogenation sour water system is illustrated in Figure 9. Next, the hydrogenation type of sour water network is analyzed. For SK4−SK7, the available sources are SR4−SR6. The mean values for properties (H2S, NH3−N, and pH) of the available sources (DW, SW2, and SR4−SR6) and the bound properties (H2S, NH3−N, and pH) of SK4−SK7 are calculated to be 1941.11, 1975.71, and 7.89, respectively. They are selected as the reference properties, and the following values of their property operators were determined: ψH2S = 1941.11, ψNH3−N = 1975.71, and ψpH = 107.89−14. When steps 1−4 are repeated, the FRs for SK4−SK7 and points for SR4−SR6 are plotted in the ternary diagram as shown in Figure 10. Obviously, SR4−SR6 are outside of the FRs for SK4−SK7. That means the mixture for those sources cannot fulfill the property requirements of the sinks. The blending source is marked in Figure 10. On the basis of the properties of SW2 listed in Table 1, when steps 1−4 are repeated, the cluster values can be calculated and SW2 can be marked in the ternary diagram as shown in Figure 11. As shown, SW2 is outside of the FRs for SK4, SK5, and SK7, and it is necessary to mix with DW to fulfill their property requirements. As shown in Figure 12a, SW2 is located inside the FR for SK6 and can fulfill its property requirement. Thus, 45 t/h of SW2 is allocated to SK6, and the source−sink configuration is determined as shown in Figure 12b. There are three sinks left for matching. As reported in part 1 of this series of papers, the quality ranking of sinks can be determined via the analytic hierarchy process (AHP) (see section 4 of part 1 of this series of papers). Following design

Figure 13. AHP hierarchy diagram.

Table 2. Total Weights for Sinks weight factor SK4 SK5 SK7

H2S

NH3−N

pH

weight

0.2297 0.2969 0.1635 0.5396

0.1220 0.2493 0.1571 0.5936

0.6483 0.4000 0.2000 0.4000

0.3579 0.1864 0.4557

SK2 and SK3 and points for SR1−SR3 are plotted in the ternary diagram as shown in Figure 7. Obviously, SR1−SR3 are located outside of the FRs for SK2 and SK3. That is to say, no matter how the sources mix, the mixture cannot fulfill the property requirements of those two sinks. In addition, 48.92 t/ h of SR1 and all SR2 (13.6 t/h) and SR3 (78.5 t/h) are mixed, and the blending source is shown in Figure 7. On the basis of the properties of SW1 (see Table 1), when steps 1−4 are repeated, the cluster values can be calculated and SW1 can be marked in the ternary diagram as shown in Figure 8. SW1 is located inside the FRs for SK2 and SK3, and it can fulfill the property requirement of SK2 and SK3. Thus, 120 and 8.5 t/h of SW1 are allocated to SK2 and SK3, respectively. The surplus 6.5 t/h of SW1 is discharged to the final treatment F



Article

Figure 15. Optimal network for the sour water system.

Figure 16. Optimal sour water system for the refinery plant.

flow rate of DW should be conserved as much as possible according to design rule No. 1 (see the discussion in part 1 of this series of papers). The flow rate matching for SK7 is performed first. When steps 1−4 are repeated, the FR of SK7 can be plotted in the ternary diagram as shown in Figure 14a. In addition, SW2 and DW can be marked in the ternary diagram and the line connecting SW2 and DW forms the available region. The line

rule No. 1, the matching sequence of sinks is recommended to be the decreasing order of quality ranking of sinks. Figure 13 shows the AHP hierarchy diagram for SK4, SK5, and SK7. According to the AHP procedure, the total weights for the three sinks are calculated as shown in the last column of Table 2. According to design rule No. 2, the recommended matching sequence for sinks is SK7, SK4, and SK5. In addition, the quality of DW is apparently better than that of SW2, and the G



Article

Flim SRi = limiting flow rate for the ith process source ψmax = maximal property operator of the pth property p ψmin = minimal property operator of the pth property p ψref p = reference property operator Ωi,smax = maximal normalized, dimensionless operator for the ith property of source s Ωi,smin = minimal normalized, dimensionless operator for the ith property of source s

segment located in the FR of SK7 is considered as the FSR for SK7. To reduce the flow rate of DW, its arm should be maximized and point O shown in Figure 14a is determined to be the optimal mixing point. When step 5 is repeated, the flow rate from DW to SK7 is determined to be 7.28 t/h and 6.72 t/h of SW2 is distributed to SK7. The configuration of source−sink matching for SK7 is shown in Figure 14b. Similarly, the flow rate matches for SK4 and SK5 are performed as illustrated in the Supporting Information. Moreover, the optimal network for the sour water system as shown in Figure 15 can be constructed by combining the optimal network for the hydrogenation sour water system (shown in Figure S3 of the Supporting Information) and nonhydrogenation sour water system (shown in Figure 9). Next, the current sour water network for the refinery plant (Figure 13 of part 1 of this series of papers) is improved and shown in Figure 16. The total flow rate for DW is 260.5 t/h, and the optimized flow rate is reduced to 52.32 t/h. It is worth mentioning that the current data are extracted from the preliminary design data. That is how such a huge reduction ratio (79.92%) for DW is achieved.

Variables

CSRi,p = cluster of property p in SRi Pmin SKj,p = minimal value for the pth property for the jth process sink Pmax SKj,p = maximal value for the pth property for the jth process sink xSRi = fractional contribution of the ith process SR1 into the total flow rate of the mixture βSRi = fractional lever arm for the ith process source λmax = maximal eigen value of the judgment matrix ω = eigenvector Subscripts/Superscripts

lim = limiting value max = maximum min = minimum SRi = ith source SKj = jth sink

6. CONCLUSION In this part of the series, the synthesis of a property-based RCN with a regeneration reuse/recycle scheme is investigated with the improved ternary diagram procedure outlined in part 1 of this series of papers. The first literature example in part 1 of this series of papers is revisited to illustrate the systematic approach for the synthesis of an RCN with a regeneration reuse/recycle scheme. In addition, a sour water network of an operating crude oil refinery is illustrated to show the feasibility and effectiveness of the proposed approach.

■

Abbreviations

ASSOCIATED CONTENT

* Supporting Information S

Figures S1−S3. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS Financial support provided by the National Basic Research Program of China (2012CB720500) and the National Natural Science Foundation of China united with China National Petroleum Corp. (U1162121) are gratefully acknowledged. The research is also supported by the Science Foundation of the China University of Petroleum (YJRC-2011-08).

■

■

AHP= analytic hierarchy process AUP= augmented property index AR= available region BFR= boundaries of the feasibility region CDU= crude distillation unit DCU= delayed coking unit DHT= diesel hydrotreating unit DW= desalted water FCC= fluid catalytic cracking FSR= feasible solution region FR= feasibility region GHT= gasoline hydrotreating unit HT= hydrotreating IPT= improved problem table MNA= mean normalization approach NNA= nearest neighbor algorithm PGA= process-based graphical approach RCN= resource conservation network SW1= nonhydrogenation-stripped water SW2= hydrogenation-stripped water SWS= sour water stripper WHT= wax oil hydrotreating unit

REFERENCES

(1) Foo, D. C. Y. State-of-the-Art Review of Pinch Analysis Techniques for Water Network Synthesis. Ind. Eng. Chem. Res. 2009, 48 (11), 5125−5159. (2) Jezowski, J. Review of Water Network Design Methods with Literature Annotations. Ind. Eng. Chem. Res. 2010, 49 (10), 4475− 4516. (3) Doyle, S. J.; Smith, R. Targeting water reuse with multiple contaminants. Process Saf. Environ. Prot. 1997, 75 (B3), 181−189. (4) Hu, N.; Feng, X.; Deng, C. Optimal design of multiplecontaminant regeneration reuse water networks with process decomposition. Chem. Eng. J. 2011, 173 (1), 80−91.

NOTATIONS

Sets and Indices

p = index for property i = index for sources j = index for sinks NSR = set of process sources NFR = set of fresh resources NSK = set of process sinks Parameters

Flim SKj = limiting flow rate for the jth process sink H



Article

(5) Teles, J.; Castro, P. M.; Novals, A. Q. LP-based solution strategies for the optimal design of industrial water networks with multiple contaminants. Chem. Eng. Sci. 2008, 63 (2), 376−394. (6) Savelski, M.; Bagajewicz, M. On the necessary conditions of optimality of water utilization systems in process plants with multiple contaminants. Chem. Eng. Sci. 2003, 58 (23−24), 5349−5362. (7) Li, Y.; Du, J.; Yao, P. J. Design of water network with multiple contaminants and zero discharge. Chin. J. Chem. Eng. 2003, 11 (5), 559−564. (8) Alva-Argáez, A.; Vallianatos, A.; Kokossis, A. A multi-contaminant transhipment model for mass exchange networks and wastewater minimisation problems. Comput. Chem. Eng. 1999, 23 (10), 1439− 1453. (9) Zhou, L.; Liao, Z.; Wang, J.; Jiang, B.; Yang, Y.; Hui, D. Optimal design of sustainable hydrogen networks. Int. J. Hydrogen Energy 2013, 38 (7), 2937−2950. (10) Zhou, L.; Liao, Z.; Wang, J.; Jiang, B.; Yang, Y. Hydrogen sulfide removal process embedded optimization of hydrogen network. Int. J. Hydrogen Energy 2012, 37 (23), 18163−18174. (11) Jia, N.; Zhang, N. Multi-component optimization for refinery hydrogen networks. Energy 2011, 36 (8), 4663−4670. (12) Kazantzi, V.; El-Halwagi, M. M. Targeting material reuse via property integration. Chem. Eng. Prog. 2005, 101 (8), 28−37. (13) Deng, C.; Feng, X. Targeting for Conventional and PropertyBased Water Network with Multiple Resources. Ind. Eng. Chem. Res. 2011, 50 (7), 3722−3737. (14) Agrawal, V.; Shenoy, U. V. Unified conceptual approach to targeting and design of water and hydrogen networks. AIChE J. 2006, 52 (3), 1071−1082. (15) 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 (5), 825−845. (16) 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 (1−3), 87− 101. (17) 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 (16), 7647−7661. (18) 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 (8), 1854−1869. (19) Qin, X.; Gabriel, F.; Harell, D.; El-Halwagi, M. M. Algebraic techniques for property integration via componentless design. Ind. Eng. Chem. Res. 2004, 43 (14), 3792−3798. (20) 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 (9), 2329−2344. (21) 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 (15), 4363−4377. (22) Ponce-Ortega, J. M.; Mosqueda-Jiménez, F. W.; Serna-González, M.; Jiménez-Gutiérrez, A.; El-Halwagi, M. M. A property-based approach to the synthesis of material conservation networks with economic and environmental objectives. AIChE J. 2011, 57 (9), 2369− 2387. (23) Rubio-Castro, E.; Ponce-Ortega, J. M.; Serna-González, M.; ElHalwagi, M. M.; Pham, V. Global optimization in property-based interplant water integration. AIChE J. 2013, 59, 813−833. (24) Chen, C.-L.; Lee, J.-Y.; Ng, D. K. S.; Foo, D. C. Y. A unified model of property integration for batch and continuous processes. AIChE J. 2010, 56 (7), 1845−1858.

I


Improved Ternary Diagram Approach for the Synthesis of a Resource

Recommend Documents