A Simple Graphical Utilities Targeting Method for Heat Integration

Article pubs.acs.org/IECR

A Simple Graphical Utilities Targeting Method for Heat Integration between Processes Anita Kovac Kralj* Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, Maribor, Slovenia ABSTRACT: Heat integration between processes aims at identifying options for heat recovery and optimal energy conversion during industrial processes. This Article introduces a simple graphical utilities targeting method for heat integration between processes. The problem is formulated as a graphical utilities targeting problem for restricting only the heat flow regarding heating and cooling when using utilities. Therefore, the original process cannot be changed but the utilities themselves could attract savings. A mathematical formulation is presented for choosing optimal heat transfer technologies. The grand composite curves of different processes represent optimal heat transfers between processes. This method is very general; it can be used within new designs and within existing processes’ integrations for utility savings. Bandyopadhyay et al.8 presented a new concept for total site integration by generating a site level grand composite curve (SGCC). The proposed SGCC targeted the maximum possible indirect integration as it incorporated assisted heat transfer. A methodology was proposed for estimating the cogeneration potential at the total site level, utilizing the concept of multiple utility targeting on the SGCC. The proposed methodology for estimating the cogeneration potential was simple and linear, as well as utilized a rigorous energy balance at each steam header. Indirect heat transfer between plants and its extension to industrial zones containing several process plants was presented by Stijepovic and Linke.9 The utility system was mainly optimized, and only waste heat could be transferred between process plants. Rodera and Bagajewicz3 developed mathematical models for direct and indirect integration within the special case of two plants. They presented a methodology for designing multipurpose heat exchanger networks that could realize those savings, and functioned in two modes, integrated and nonintegrated.10,11 Sahu and Bandyopadhyay12 presented linear programming formulations, complemented by concept-based pinch analysis results, that were developed to target the minimum energy requirements within heat integrated fixed flow rate water allocation networks. These formulations could be applied for the cases of heat integration through isothermal and nonisothermal mixing in water allocation networks involving single as well as multiple contaminants. This article presents a simple graphical utilities targeting method, which is based on pinch analysis to estimate heat integrations between processes.

1. INTRODUCTION Pinch analysis is a promising tool for optimizing the energy efficiencies of industrial processes, for realizing maximum heat recovery, and for the optimal integration of utilities for supply process heating and cooling requirements. The paper by Ahmad and Hui1 instigated a new understanding of such problems, and revealed how to maximize heat recovery using a few interconnections between process regions, whether by using direct or indirect heat transfers. When considering an overall plant consisting of several processes, their paper described a method that leads to “total siteintegration” where heat recovery from one process to another occurs by combining their utilities. It developed a new understanding of such problems and revealed how to maximize heat recovery with a few interconnections between processes. This method focused on minimum energy usage but ignored exchanger capital costs and, therefore, did not normally lead to optimal designs within the total cost. Later, Hui and Ahmad2 studied those self-sufficient zones that are not always suppressed. More directly, Rodera and Bagajewicz3 pointed out that skipping the self-sufficient pocket can significantly reduce the opportunities for heat recovery and present a transshipment model that calculates the heat to be transferred between two process plants. An extension to several plants was proposed later by the same authors.4,5 Bagajewicz and Rodera6 proposed a single heat belt, which exchanged heat between process plants using an intermediate fluid. However, only for special cases (three process plants) could this problem be solved using a MILP formulation. The method of total site integration for several processes by Dhole and Linnhoff7 used “source and sink profiles” for optimizing the overall site utility system. These source and sink profiles represent the net heat source and heat-sink of the overall site after maximized heat recovery within the processes. Bagajewicz and Rodera performed their previous work by using mathematical programming (MILP) for more plants.4 However, the system of equations is difficult to converge for simultaneous process optimization and heat integration, when processes are complex and energy intensive because the number of variables increases with the number of combinations. © 2012 American Chemical Society

Received: Revised: Accepted: Published: 14171

July 4, 2012 September 21, 2012 October 9, 2012 October 9, 2012 dx.doi.org/10.1021/ie301769e | Ind. Eng. Chem. Res. 2012, 51, 14171−14177

Industrial & Engineering Chemistry Research

Article

2. THE PROPOSED SIMPLE GRAPHICAL UTILITIES TARGETING METHOD Heat integration between processes would be targeted regarding options for heat recovery and optimal energy conversion to reduce carbon emissions and the usage of fossil fuels within industrial processes. An existing process could be operated as an independent plant consisting of heat integration within a minimum utility system. In the petrochemical industry, many individual plants operate at one location, yet could be mutually integrated. An analysis of possible integration would include only those streams that are heated or cooled by using utilities; therefore, this would not change the basic operations. These processes include the available heat flow rate, and this heat flow rate requirement would be analyzed progressively. All of the characterized hot and cold streams would be inserted within the GCC (grand composite curve). First, all of the higher and lower temperatures’ self-sufficient pockets (i = 1, 2, 3...I) within the GCC would be analyzed (Figure 1). The maximum length

TH = TC

(4)

QH = Q C

(5)

Q H = (nC − nH)/(kH − k C) = Q i

(6)

Figure 2. Triangular self-sufficient pockets of GCC.

An analogy would be carried out for all of the other pockets (eqs 2−6). So that in this way the numbers of hot and cold streams would be reduced, and so integration would be successful. Estimation of real possible heat integration between processes could be determined quickly. The real possible heat integration would be about 0.1% less than the maximum possible integration at the same ΔminT. A small error was noticed when checking the accuracy of the data from the graph. The maximum possible integration between the processes would be estimated by using the fraction of maximum possible heat integration between the processes from a grand composite curve (GCC) at defined by ΔminT (eq 7; Figure 313):

Figure 1. Flow diagram for integration between processes by using the simple graphical utilities targeting method.

fMPI = (min ∑ Q − Q HU)/min ∑ Q

of pocket i denotes a real possibility for an integrated heat flow rate (Qi). The sum of all of the maximum pockets’ lengths would provide the real total for a possible integrated heat flow rate (Qt): Qt = ∑Qi

i = 1, 2, 3...I

(7)

f MPI is the fraction of the maximum possible heat integration between processes, min ∑Q is the minimum sum of the heat flow rates for hot (∑QH) or cold (∑QC) streams, whichever is

(1)

Progressive analysis could be better for analyzing the higher and lower temperature pockets, which could determine the heat transfer between the exact hot and cold streams within the pockets, and, therefore, the heat integration would be more effective. The triangular pocket would additionally confirm the real heat integration between hot and cold streams within this pocket. The hot and cold streams from the triangular pocket would be linearized separately (eqs 2 and 3). By assuming that both lines could intersect at point P, so that both the independent and the dependent parameters were the same (eqs 4 and 5), the heat flow rate would be estimated (eq 6), which is the same as the real possible integrated heat flow rate (Qi) (Figure 2).

TH = kHQ H + nH

(2)

TC = k CQ C + nC

(3)

Figure 3. A flow diagram for more efficient heat integration. 14172

dx.doi.org/10.1021/ie301769e | Ind. Eng. Chem. Res. 2012, 51, 14171−14177


Article

Figure 4. Solvent process flow diagram.

Figure 5. Formalin process flow diagram.

110, and bottoms. The bottoms’ exchange heat flows within the heat exchangers E208, E210, E206, and E203 before distillation in T401 produced the products SB 110/140 and SB 140/190. The formalin is produced from methanol and air (Figure 5). They are first mixed in C4 and then within column C3/C5 heated in heat exchangers E2 and E4, and reactivated within the reactor RE. The gaseous formalin produced is absorbed into water within C1, and produced as C2 bottoms. The internal streams are recycled. The low-pressure Lurgi methanol process is composed of three subsystems (Figure 6): • production of synthesis gas • production of crude methanol • purification of methanol In the first subsystem, natural gas is desulphurized (R1), and synthesis gas is produced from natural gas and steam within a steam reformer (R2). The purge gas and expansion gas are burnt in the reformer. The hot stream of the synthesis gas is cooled in a boiler B1, in heat exchangers E1, E2, E3, in an air

smaller, and QHU is the hot utility flow rate (of the highest level) in kW. The maximum possible integrated heat flow rate QMPI in kW could be calculated using eqs 7 and 8: Q MPI = fMPI ·min ∑ Q

(8)

3. CASE STUDY The suggested method was tested using three existing complex processes: • solvent production (S) from a primary oil • formalin production (F) from methanol and air • low-pressure Lurgi methanol production (M) There were all operated within one location, and, therefore, a simple graphical utilities targeting method could be used between them. The solvent is produced from a primary oil (Figure 4), which is heated in heat exchangers E401, E208, and E204, distilled in T402 into products: distillates SB 35/70 (the boiling point ranges from 60 to 80 °C) and SB 60/80, side stream SB 80/ 14173



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Figure 6. Methanol process flow diagram.

+ 4356.51 kW Table 1) of 5 bar steam as utility requirements. All of the hot and cold streams from Table 1 would be inserted into the GCC with ΔminT = 10 °C (Figure 7). First, all of the higher and lower temperatures’ self-sufficient pockets (i = 1, 2) would be analyzed (Figure 7). The maximum length of pocket i = 1 would denote the real possible integrated heat flow rate (Qi=1), being 350 kW. The second length of the pocket would be 4432.6 kW (Qi=2). The sum of all of the maximum pockets lengths would provide the total real possible integrated heat flow rate (Qt), being 4782.6 kW. The heat transfers were determined between the exact hot and cold streams within the pockets. Within the first selfsufficient triangular pocket (i = 1), the heat was transferred from the hot (M-ES1) to the cold (S-E202 and S-E404; Table 1, Figure 7) streams. In the second self-sufficient pocket (i = 2), the heat was transferred from the hot (M-ES2) to the cold (FE10, S-E204, F-E5, F-E6; Table 1) streams. The triangular pocket can additionally confirm the real heat integration between the hot and cold streams in this pocket. For example, the first triangular pocket (i = 1) consisted of the hot (M-ES1) and cold (S-E202, S-E404, Table 1) streams. Both were represented by linear functions (Figure 8; eqs 9−11), which were intersected at point P and the coordinates of the points were 350 kW and 170 °C; therefore, the real possible integrated heat flow rate (Qi=1) was 350 kW.

cooler AC1, and in water coolers C1, C2. In the second subsystem, methanol is produced by catalytic hydrogenation of carbon monoxide and/or carbon dioxide in a reactor R3. The outlet stream of crude methanol is cooled within its inlet stream in the heat exchanger E4, in the air-cooler AC2, and in the water-cooler C3. The methanol is flashed in F6. In the third subsystem, crude methanol is refined to pure methanol by distillation in the purification section (D1−D3) of the process, to remove water and a variety of other impurities. 3.1. A Simple Graphical Utilities Targeting Method. The available processes and the required heat flow rate would be analyzed progressively. Available energy during methanol production would be at higher (stream M-ES1) and lower (instead of an air-cooler; streams M-ES2) temperature levels (Table 1). The required heat flow rates would be needed during the solvent and formalin production processes (Table 1). Both processes would be operated using 4786.8 kW (430.29 Table 1. Hot and Cold Streams of Processes Ts/oC

Tt/oC

I/kW

Existing Methanol Plant M-ES1 − hot 180.0 M-ES2 − hot 160.0

175.0 120.0

350.00 4720.00 ∑5070.00

Existing Solvent Plant S-E204 − cold S-E202 − cold S-E404 − cold

69.2 149.5 152.1

114.6 164.0 160.0

83.49 158.50 188.30 ∑430.29

15.0 53.3 94.0

54.0 55.1 94.5

75.27 139.24 4142.00 ∑4356.51

stream

Existing Formalin Plant F-E6 − cold F-E5 − cold F-E10 − cold

TH = kHQ H + nH = −0.0143Q H + 175

(9)

TC = k CQ C + nC = 0.0292Q H + 159.77

(10)

Q H = (nC − nH)/(kH − k C) = Q i = 350 kW

(11)

Estimation of the real possible heat integration between processes could be determined quickly. The maximum possible integration between the processes would be estimated by using 14174



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Figure 7. Self-sufficient pockets of integration between the existing processes.

Figure 8. One triangular self-sufficient pocket of the existing case.

(13)

progressive analysis. The hot (M-ES1) and cold (S-E202 and S-E404; Table 1, Figure 9) streams were integrated within the first self-sufficient triangular pocket (i = 1). The hot (M-ES2) and cold (F-E10, S-E204, F-E5, F-E6; Table 1, Figure 9) streams were integrated within the second self-sufficient triangular pocket (i = 2). The implementation of integration between processes would only require an additional insulation piping without the necessity of new heat exchangers (Figure 10). 4786.9 kW of 5 bar steam could be saved by heat integration between processes. The cost of insulation piping was 2000 USD/a regarding one transfer (Cpip, Table 2). Integration was transferred for six insulation piping systems (Figure 10); therefore, the total cost was 12 000 USD/a. The saving regarding heat integration was 335 080 USD/a; therefore, the price of 5 bar steam was 70 USD/a (C5, Table 2). The additional profit was generated from integration 323 080 USD/a.

The maximum possible integrated heat flow rate QMPI was 4786.9 kW, and the total real possible integrated heat flow rate (Qt) was 4782.6 kW. The result only deviated by 0.08%. A small error was noticed when checking the accuracy of the data from the graph. 3.2. Integration by Using a Simple Graphical Utilities Targeting Method. The results were taken from the

4. CONCLUSIONS We have applied a simple graphical utilities targeting method for heat integration between processes. This method only analyzes streams that are being heated or cooled using a utility, and when using them the operation of a process remains unchanged. This method is based on all of the higher and lower

the fraction of maximum possible heat integration between the processes with ΔminT = 10 °C (eq 12; Figure 3): fMPI = (min ∑ Q − Q HU)/min ∑ Q = (4786.9 − 0)/4786.9 =1

(12)

f MPI is the fraction of the maximum possible heat integration between processes, and min ∑Q is the minimum sum of the heat flow rates for hot (∑QH = 5070 kW) or cold (∑QC = 430.29 + 4356.51=4786.8 kW) streams, whichever is smaller. The heat flow rate was smaller for the cold streams. QHU, the hot utility flow rate, was 0 kW. The maximum possible integrated heat flow rate QMPI in kW can be calculated using eqs 12 and 13: Q MPI = fMPI ·min ∑ Q = 1· 4786.9 = 4786.9 kW

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Figure 9. Integrated matches of the existing case.

Figure 10. The totally integrated system between the processes.

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Table 2. Cost Data for Example Processes cost of 5 bar steam (C5): cost of insulating one piping system (Cpip): time fraction of operation:

AUTHOR INFORMATION

Corresponding Author

70 USD/kWa 2000.0 USD/a 8000.0 h/a

*Tel.: +386 02 2294454. Fax: +386 02 22 77 74. E-mail: anita. [email protected]. Notes

The authors declare no competing financial interest.

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temperatures self-sufficient pockets of GCC, for determining the total real possible integrated heat flow rates between the processes. The maximum length of pocket i denotes the real possible integrated heat flow. The sum of all of the maximum

ABBREVIATIONS GCC = grand composite curve

Variables

f MPI = fraction of the maximum possible integration, 1 I = enthalpy flow rate, W T = temperature, K Ts = supply temperature, K Tt = target temperature, K Q = heat flow rate, W QHU = hot utility flow rate, W

pockets’ lengths provides the total real possible integrated heat flow rate. This method is very quick, as it is based on simple principles and provides the necessary high-speed characteristics of integration between processes from the GCC. 14176



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REFERENCES

(1) Ahmad, S.; Hui, C. W. Heat recovery between areas of integrity. Comput. Chem. Eng. 1991, 15, 809−832. (2) Hui, C.; Ahmad, S. Total site heat-integration using the utility system. Comput. Chem. Eng. 1994, 18, 729−742. (3) Rodera, H.; Bagajewicz, M. Targeting procedures for energy savings by heat-integration across plants. Am. Inst. Chem. Eng. J. 1999, 45, 1721−1742. (4) Bagajewicz, M.; Rodera, H. Energy savings in the total site heatintegration across many plants. Comput. Chem. Eng. 2000, 24, 1237− 1242. (5) Bagajewicz, M.; Rodera, H. Multiple plant heatintegration in a total site. Am. Inst. Chem. Eng. J. 2002, 48, 2255−2270. (6) Bagajewicz, M.; Rodera, H. On the use of heat belts for energy integration across many plants in the total site. Can. J. Chem. Eng. 2001, 79, 633−642. (7) Dhole, V. R.; Linnhoff, B. Total site targets for fuel, cogeneration, emissions and cooling. Comput. Chem. Eng. 1992, 17, S101−S109. (8) Bandyopadhyay, S.; Varghese, J.; Bansal, V. Targeting for cogeneration potential through total site integration. Appl. Therm. Eng. 2010, 30, 6−14. (9) Stijepovic, M.; Linke, P. Optimal waste heat recovery and reuse in industrial zones. Energy 2011, 36, 4019−4031. (10) Rodera, H.; Bagajewicz, M. Accomplish profitable savings by realistically heat integrating a crude fractionation unit and an FCC unit. Paper 45c. Proceedings of the 2nd International Conference on Refinery Processing; AIChE, 1999b. (11) Rodera, H.; Bagajewicz, M. Multipurpose heat exchanger networks for heat integration across plants. PRESS 99 Meeting; Budapest, June 1999. (12) Sahu, G. C.; Bandyopadhyay, S. Energy integration across multiple water allocation networks with negligible contaminant effects. Asian Pacific J. Chem. Eng. 2011, 527−536. (13) Kovac Kralj, A. Estimating the maximum possible internal heat integrations of individual processes. Energy (Oxford) 2009, 34, 1372− 1377.

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A Simple Graphical Utilities Targeting Method for Heat Integration

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