Environment and economy based optimization of CWS featured air coolers

Journal of Cleaner Production 233 (2019) 793e807

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Environment and economy based optimization of CWS featured air coolers Zefeng Qi a, 1, Yifan Ma a, 1, Chuang Chen a, Yufei Wang a, *, Xiao Feng b a b

State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, 102249, China School of Chemical Engineering & Technology, Xi'an Jiaotong University, Xi'an, 710049, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 January 2019 Received in revised form 6 June 2019 Accepted 8 June 2019 Available online 13 June 2019

Cooling water system (CWS) is a very common system that discharges industrial waste heat. For the existing researches about industrial CWSs, most of them focus on economic performance, ignoring the environmental impact. Based on the industrial foundation, this work proposes a methodology to optimize and evaluate the economic performance and environmental impact. This paper integrates several components, such as cooler network, pump network, cooling tower and air cooler to optimize the total annual cost. Carbon footprint and water footprint are introduced as environmental indicators. The whole system is integrated into Mixed Integer Nonlinear Programming (MINLP) model and solved by GAMS. Through case study, an improved CWS structure chart is obtained, and the relationship between carbon footprint, water footprint and economic performance is drawn, showing the trade-off between the three. The result shows that total annual cost (TAC) can be reduced by 9.84% by economic optimization design. The result also gives a guideline on how to improve environmental performance according to water footprint and carbon footprint. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Jiri Jaromir Klemes Keywords: CWS Water footprint Carbon footprint MINLP model

1. Introduction In large industrial facilities, CWSs are extensively applied to fulfill cooling duty. In a CWS, a cooler network consists of a number of air coolers and water coolers are used to take away waste heat from hot process streams, and a pump network consists of several pumps is used to delivery water to coolers. The pump network consumes huge power every year, lead to certain greenhouse emissions. Evaporation and blowdown in cooling towers also brings a certain burden to water resources. Therefore, it is important to optimize CWS from both economic and environmental views. In past studies, different subsystems in a CWS were initially studied separately. For the study of cooler network, Kim and Smith (2001) pioneered the optimization of cooler networks, making it more than just a flat configuration, adding a series configuration to better fit the cooling tower, and ultimately reducing total water flow. Feng et al. (2005) presented a new configuration for CWS using an intermediate cooling water manifold cycle. By using the

* Corresponding author. E-mail address: [email protected] (Y. Wang). 1 First Authors. https://doi.org/10.1016/j.jclepro.2019.06.081 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

methodology, the flow rate of cooling water can be decreased and the efficiency of cooling tower can be improved through the recirculation of water. However, in actual production, the reduction of cooling water does not necessarily mean the reduction of total cost. In the subsequent research process, in order to reduce the economic cost of industrial systems, Esen et al. (2006) used the annualized life cycle cost method to conduct economic analysis of ground source heat pump (GSHP) system, and then they made technical and economic comparisons between ground-coupled heat pump (GCHP) system and air-coupled heat pump (ACHP) system (Esen et al., 2007). The ACHP system is originally used for space cooling. Muller and Craig (2017) reduced power consumption, considering the cost of utilities in petrochemical plants. For CWS, Ponce-Ortega et al. (2007) developed a superstructure based optimization methodology with total cost as the objective function. Capital cost including cooler investment and operation cost including utility cost were considered. Stage-wise superstructure was used to describe the different parallel-series structures for the cooler network. Based on the foundation of the MINLP model, more elements and situations were taken into account, for example, Serna-Gonzlez et al. (2010) also reduced the annual total cost of cooling towers by optimizing parameters such as cooling tower size. Rubio-Castro et al. (2013) proposed a model for simultaneous

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optimization of the cooler network and cooling tower network, developed the upper structure, searched for different alternatives and potential connections of heat flow data to optimize the total cost. Zhu et al. (2017) comprehensively describes the performance of industrial circulating CWS considering the diversity of actual pipe network equipment. Subsequently, the optimization method of integrated operation of water and energy is put forward to reduce the operation cost of the system (Zhu et al., 2019). Wang et al. (2014) transformed the traditional parallel cooling water network into a series arrangement through a two-step method without investing in the new heat transfer area. Song et al. (2018) synthesized the non-linear programming formulas of circulating CWS and chemical process operation optimization to optimize the whole system. The works mentioned above only considered the optimization of water cooler arrangement. Air cooler is also a widely used cooling method. Compared with water cooler, it does not consume water resources but it requires more power and larger heat transfer area. Doodman et al. (2009) studied the application of global sensitivity analysis and harmony search algorithm in air cooler design optimization from an economic point of view. Alinia Kashani et al. (2013) balanced temperature and minimum total annual cost (TAC) by developing thermodynamic modeling and optimal design of air coolers. Manassaldi et al. (2014) solved seven parameters such as fan diameter by branch and bound method, and got the model of minimizing investment and total annual cost. Kang et al. (2017) simulated the performance of air cooler by controlling the change of air flow and water flow. Zhang et al. (2018) obtained the TAC for different dry bulb temperatures and relative humidity by comparing different water coolers and air coolers, which helped to determine the optimal cooling method. Water coolers and air coolers have great difference on power and water consumption, as well as capital cost in a CWS. Reasonable allocation of air and water coolers in cooler network can affect the economic and environmental performance of the system. However, works about CWS with both air coolers and water coolers are rare. Ma et al. (2018) established an MINLP model to optimize the duty distribution among air coolers and water coolers in a CWS. The impact of water and power cost on overall performance was analyzed. However, it was optimized separately without combining pump network. Compared to the parallel structure in a cooler network, higher pumping power is required for series structure due to larger pressure drop. In response to this problem, Pettersson and Westerlund (1997) optimized the pump configuration by solving the local optimal solution. Then the global optimal solution is used to design and optimize the pump configuration (Westerlund et al., 1994). Souza et al. (2016) involved the cost of the pipeline, pressure drop and the layout of the equipment in the optimization of TAC. Gololo and Majozi (2012) considered the pressure drop and the associated power costs by simultaneously optimizing the design of the integrated network of water resources and energy. PonceOrtega et al. (2010) developed an optimization method to consider chiller network, pump network and cooling tower. But during the optimization process, the pumping cost only measured the pressure drop of coolers. The works mentioned above only considered pumping related aspect in the optimization, but the structure of pump network was fixed. Sun et al. (2014) firstly introduced the concept of main-auxiliary pump structure. By installing auxiliary pumps in branches with high head requirement, the overall power consumption can be reduced. Then, the interaction between cooler network and pump network were considered, and the relative optimization framework was proposed (Sun et al., 2015). Later, some other pump network structure was proposed to reduce the pump power in CWS (Ma et al., 2017). Zheng et al. (2018) optimized the CWS by considering the configuration of main and

auxiliary pumps and the location of cooling towers. With the development of technology and industry, the requirement for environmental protection is also increasing. In recent years, many methods are used to describe environmental impact for cooling or heating systems. For example, Esen and Yuksel (2013) used alternative energy and renewable energy to replace fossil energy with relatively high heat load and price. Cooling towers in refrigeration systems were studied through a multi-objective optimization considering environmental impacts and thermodynamic effects by Sayyaadi and Nejatolahi (2011). Isafiade et al. (2017) optimized the multi-cycle heat exchanger network by integrating renewable energy. Vaskan et al. (2012) introduced the life cycle theory to study the heat exchanger network and its environmental impact. Doroti c et al. (2019) developed a multi-objective optimized district heating and cooling model based on total system cost and carbon dioxide emissions. Keshtkar and Talebizadeh (2017) optimized the compression refrigeration system in the refinery, taking CO, CO2 and NOx emissions as environmental objective functions, combined with thermodynamics and total cost. As a precious resource, a large amount of water is consumed in the cooling water system, and the consumption of resources and energy such as power and equipment operation also has an impact on environment. Chen and Chen (2016) studied the relationship between energy consumption and water use, making it possible to evaluate the whole industrial system by water consumption. Moreover, according to Zhang and Choi (2013), by 2010, 48% of China's total carbon emissions came from fossil fuel power generation. Therefore, the study of the environmental impact of cooling water system can be transformed into the study of the impact of water consumption and carbon dioxide emissions on the environment. Water footprint refers to the sum of water consumption and net virtual water input. Water footprint can be evaluated according to water resources consumption in different systems and different scales. Jeswani and Azapagic (2011) used life cycle method to assess water footprint, which is also an important method in the process of water footprint research. In subsequent studies, water footprint has been increasingly used to indicate the impact of agricultural water use (Herath et al., 2013). Hoekstra (2015) explored the impact of water footprints on managing water scarcity and pollution from an industrial perspective, improving water resources assessment in industrial systems. Gu et al. (2015) developed a method to calculate water footprint for measuring steel industry. Krishna Priya and Bandyopadhyay (2017) used water footprint as an environmental indicator in multi-objective optimization for fossil fuel power generation. For the calculation of carbon dioxide emissions, carbon footprint is a common quantitative system. Carbon footprint is a quantitative system for calculating greenhouse gas emissions (Balaguera et al., 2018). Herrmann and Hauschild (2009) used the inputeoutput analysis of environment to calculate the carbon footprint in a bilateral trade. Life cycle assessment is also a common method for calculating carbon footprint, Laurent et al. (2010) used carbon footprint based on life cycle assessment to obtain the environmental impact of manufacturing industry. Life cycle assessment of carbon footprint is also widely used in the field of architecture. Pal et al. (2017) demonstrated a life cycle simulation to calculate the carbon footprint of buildings. In the industrial system, Qi et al. (2018) analyzed the carbon footprint of the steel industry. Yang (2018) used the carbon footprint to optimize the total cost of the power system. Shaikh et al. (2017) estimated water and carbon emissions from electricity production through the establishment of a water-carbon footprint framework. However, no water footprint and carbon footprint related research is reported on CWS. In CWS, the interaction between cooler network and pump network, and the configuration of water cooler and air cooler has

Z. Qi et al. / Journal of Cleaner Production 233 (2019) 793e807

great impacts on the total annual cost of the system. Considering the difference of water footprint and carbon footprint for different equipment, it is unreasonable to consider only economic benefits. Therefore, this paper presents a model for optimizing the characteristic CWS with air cooler from both economic and environmental aspects separately, taking the total annual cost as the economic objective and the water footprint and carbon footprint as the environmental objective. Aiming at the case in literature, the water cooler network, pump network, air cooler and cooling tower are optimized simultaneously. The best configuration of system under different objective is analyzed according to water footprint, carbon footprint and TAC.

795

Assumption 1 is because shell and tube heat exchangers are the most common heat exchangers in refineries and other large chemical processes (Kakaç et al., 2012). For assumption 2, the purpose is to avoid serious scaling of the cooler due to excessive cooling water temperature. As for assumptions 3 and 4, the model is simplified on the basis of conforming to industrial reality.

3. Model formulation 3.1. Equipment formulation 3.1.1. Formulation of cooler network Fig. 1 is applied to show the basic structure for cooling a single hot stream. The cooling method of a hot stream has three possibilities.

2. Problem statement Given a set of hot steams required for cooling, the supply temperatures, target temperatures, flowrates and heat transfer coefficients of these streams are known. The structure of cooler network is optimized by applying series-parallel arrangement. Pump network optimization through applying main-auxiliary pump structure is carried out to reduce the pump power consumption. Water footprint and carbon footprint are applied to analyze the environmental impact. The optimal heat load distribution between air coolers and water coolers is determined by considering both environmental and economic impacts. The new model allows the system to find the optimal duty distribution based on the hot streams’ properties. In this study, the optimal structure is obtained by optimization with TAC and environmental impact objectives as objective functions. The MINLP model is used for this study. The DICOPT solver in the software GAMS is used to solve this problem. The solver DICOPT cannot obtain the global optimal solution, only the local optimal solution can be obtained, but the value is near the global optimal solution. Therefore, the solution can still be used for revealing physical insights of the work. The assumptions for the methodology are shown below: 1) The water coolers are 1-1 countercurrent shell and tube heat exchangers. 2) The outlet temperature of water coolers has upper bound. 3) The maximum number of air cooler and water cooler is both one for each hot stream. 4) The film transfer coefficients and the specific heat capacities of all the streams are constants.

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25

1) Hot stream is cooled by a combination of air and water cooling. 2) Hot stream is completely cooled by water cooling. 3) Hot stream is completely cooled by air cooling. Ew ðiÞ represents the water cooler for cooling hot stream i. Ea ðiÞ represents the air cooler for cooling hot stream i. Th;in ðiÞ represents the inlet temperature of hot steam i. Th;aout ðiÞ represents the outlet temperature of hot stream i of Ea ðiÞ and the inlet temperature of hot stream i of Ew ðiÞ. Th; out ðiÞ represents the target temperature of hot steam i: Eq. (1) shows the heat balance of an air cooler. Qa ðiÞ represents the heat load of Ea ðiÞ. Ta;in ðiÞ and Ta;out ðiÞ represent the inlet and outlet air temperature of Ea ðiÞ. fh ðiÞ represents the heat capacity flowrate of hot stream i. fa ðiÞ represents the heat capacity flowrate of air.



 Q a ðiÞ ¼ Th;in ðiÞ  Th;aout ðiÞ  fh ðiÞ ¼ Ta;out ðiÞ  Ta;in ðiÞ  fa ðiÞ (1) Eq. (2) and Eq. (3) are the temperature difference constraints in the model. DTh;a ðiÞ and DTc;a ðiÞ represent temperature different between hot and cold streams in air cooler Ea ðiÞ, and it is not allowed to less than the minimum temperature difference DTmin .

DTh;a ðiÞ ¼ Th;in ðiÞ  Ta;out ðiÞ  DTmin

(2)

DTc;a ðiÞ ¼ Th;aout ðiÞ  Ta;in ðiÞ  DTmin

(3)

Eq. (4) is employed to calculate the heat exchanger area (Chen,

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%UHDNSRLQW

7KLQL $LU FRROHU L

6HQG EDFN WR FRROLQJ WRZHU Fig. 1. The coupling structure of coolers.

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Z. Qi et al. / Journal of Cleaner Production 233 (2019) 793e807

1987). In the equation, hðiÞ is the film transfer coefficient of hot stream i: ha is the film transfer coefficient of air. Aa ðiÞ represents the heat exchanger area of air cooler Ea ðiÞ.

  1 1 #1=3  hðiÞ þ h a ðDTh;a ðiÞþDTc;a ðiÞÞ

Qa ðiÞ

Aa ðiÞ ¼ "

DTh;a ðiÞ  DTc;a ðiÞ 

Zði; jÞ ¼ 0; i ¼ j

2

(4) Eq. (5) shows the actual face velocity of air cooler is influenced by ambient temperature, Vf and Vaf represent the face velocity and the actual face velocity respectively.

Vaf Vf ¼ 293:15 273:15 þ Ta;in

(6)

Eqs. (7)e(9) are applied to calculate the air pressure drop Dpa of air cooler. Nb is the number of bundles. fr represents the friction factor. G is the air mess velocity. Gmax is the maximum air mass velocity. ra is the density of air.

(13)

In Eq. (14) and Eq. (15), Tfw is the fresh water temperature of cooling tower. f ðiÞ is the mass flowrate of cooling water in Ew ðiÞ, fw ðiÞ is the mass flowrate of fresh water to Ew ðiÞ, Tw;in ðiÞ and Tw;out ðiÞ represent the inlet and outlet cooling water temperature of Ew ðiÞ. When Zðj; iÞ equals 1, Ew ðiÞ receives only the used water from Ew ðjÞ. Eq. (14) and Eq. (15) are used to express the constraints above. n n
h i X X  Tw;in ðiÞ ¼ 1  Zðj; iÞ  Tfw þ Zðj; iÞ  Tw;out ðjÞ

(5)

Eq. (6) is employed to show the relationship between the air film transfer coefficient and the actual face velocity.

ha ¼ 218:9  V 0:718 af

For each water cooler, the inlet cooling water cannot be the outlet cooling water of the same water cooler. When i equals j, the value of Zði; jÞ is 0. Eq. (13) is used to express the constraint above.

j¼1

(14)

j¼1

n n i h X X Zðj; iÞ  fw ðiÞ þ ðZðj; iÞ  f ðjÞÞ f ðiÞ ¼ 1  j¼1

(15)

j¼1

Eq. (16) shows the heat balance of a water cooler. Qw ðiÞ represents the heat load of water cooler Ew ðiÞ. cpw is the specific heat capacity of water.


 Qw ðiÞ ¼ Th;aout ðiÞ  Th;out ðiÞ  fh ðiÞ 
¼ Tw;out ðiÞ  Tw;in ðiÞ  cpw  f ðiÞ

(16)

G ¼ Vf  ra

(7)

Gmax ¼ 2  G

(8)

In Eq. (17) and Eq. (18), DTh;w ðiÞ and DTc;w ðiÞ represent temperature differences between the two streams in a water cooler. The temperature differences should larger than minimum temperature difference DTmin . Constrains are expressed by Eq. (17-19).

(9)

DTh;w ðiÞ ¼ Th;aout ðiÞ  Tw;out ðiÞ  DTmin

(17)

DTc;w ðiÞ ¼ Th;out ðiÞ  Tw;in ðiÞ  DTmin

(18)

Tw;out ðiÞ  Tw;in ðiÞ

(19)

Dpa ¼ 9:8  fr 

Nb  G2max 2g ra

Eq. (10) is used to calculate the power consumption of air cooler, Va is the volumetric flowrate of air, hf ;a is the air cooler fan efficiency, Pf ;a is the air cooler fan power consumption.

Pf ;a

Dpa  Va ¼ hf ;a

(10)

In Eq. (20) hw is the cooling water side film transfer coefficient. Aw ðiÞ represents the contact area of water cooler Ew ðiÞ.

Aw ðiÞ ¼ " 3.1.2. Series-parallel structure of water cooler In this study, a hot stream can be cooled by at most one water cooler. In the series-parallel structure model, the cooling water in a cooler can come from either cooling tower or another cooler. And the outlet cooling water of a cooler can go to either cooling tower or another cooler for reuse. Several constraints are used to show the relationship above. In Eq. (11) and Eq. (12), a binary variable Zði; jÞ is employed to denote whether Ew ðjÞ uses the outlet cooling water from Ew ðiÞ. When the value of Zði; jÞ is 1, the outlet cooling water of Ew ðiÞ is the inlet cooling water of Ew ðjÞ. Eq. (11) shows the relationship that for each water cooler, the outlet cooling water can be only sent to another water cooler or cooling tower. Eq. (12) represents that for each water cooler, the inlet cooling water can only come from another water cooler or the fresh water from the cooling tower. n X

Zði; jÞ  1

(11)

i¼1 n X j¼1

Zði; jÞ  1

(12)

Qw ðiÞ

DTh;w ðiÞ  DTc;w ðiÞ 

  1 1 #1=3  hðiÞ þ h w ðDTh;w þDTc;w Þ 2

(20) In Eq. (21), ft represents the total cooling water mass flowrate.

ft ¼

n X

fw ðiÞ

(21)

i¼1

The pressure drops of tube side among water coolers, represented by Dpw ðiÞ, is related to a model parameter Kw ðiÞ, heat exchanger area Aw ðiÞ, and the film transfer coefficient (Soltani and Shafiei, 2011). The parameter Kw ðiÞ can be calculated through Eq. (23). And it is a function of cooling water viscosity mw, viscosity correction factor Fw, tube internal and external diameter di and do , density of water rw, conductivity kt , and specific heat capacity cpt . The relation is shown in Eq. (22) and Eq. (23).

Dpw ðiÞ ¼ Kw ðiÞ  Aw ðiÞ  ht 3:5 Kw ðiÞ ¼

11=6 0:5 F4:5  mw d w  di  i 7=3 7=6 do 0:0232:5  f ðiÞ  rw  kt  cpt

(22)

(23)

Z. Qi et al. / Journal of Cleaner Production 233 (2019) 793e807

In a cooler network, total pressure drop Dpt equals to the maximum pressure drop among all the branches. The pressure drop of a single branch is related to detailed pipe and cooler situations. Eq. (24) is employed to show the relationship mentioned above.

Dpt  Dpw ðiÞ þ

n n X X ½Zðj; iÞ  Dpw ðjÞ þ ½Zði; jÞ  Dpw ðjÞ j¼1

(24)

dH 0 ði; jÞ ¼ H0 ðiÞ  H 0 ðjÞ

(25)

dH 0p ði; jÞ ¼ maxf0; dH 0 ði; jÞg

(26)

The required head of a branch is related to the height and the head loss (due to pressure drop) of the branch. Equation (27) is used to indicate the calculation of minimum pressure head requirement HðiÞ for cooler Ew ðiÞ. rw is the density of water.

HðiÞ ¼

n  X

n   X  Zði; jÞ  dH 0p ðj; iÞ þ Zðj; iÞ  dH 0p ðj; iÞ þ H0 ðiÞ

j¼1

þ

In a cooling tower, the air flowrate fa;t can be determined through Eq. (30). Win and Wout are the inlet and outlet air humidity. Evop is used to represent the evaporation capacity of water. The relationship is expressed by Eq. (30).

Evop Win  Wout

fa;t ¼

j¼1

3.1.3. Pump network formulation In the structure where the pump exists only on the main pipe, the supply pressure head must be greater than the maximum pressure head requirement among all branches. In some branches, the installation height of the cooler can be very high, results in a very high supply head of main pump, and further leads to a high power consumption. In this work, main-auxiliary pump structure are applied. The auxiliary pumps are implemented on the branch with high head requirement to reduce the overall power consumption of the pump network (Sun et al., 2014). When series-parallel structure for cooler network is considered. More than one coolers may install on the branch. The relative height of them should be considered to determine the head requirement of the branch. Eq. (25) and Eq. (26) are used to calculate the relative height. dH 0 ði; jÞ is the height difference between Ew ðiÞ and Ew ðjÞ. H 0 ðiÞ is the installation height of Ew ðiÞ. dH 0p ði; jÞ represents the positive value of dH 0 ði;jÞ. When the value of dH0 ði; jÞ is negative, dH 0p ði; jÞ is set to zero.

797

(30)

Eq. (31) is used to calculate the evaporation capacity of water. It is a function of cooling water flowrate and the temperature difference (DTwt ) between the inlet temperature (Tc;out ) and outlet temperature (Tc;in ) of cooling water in the tower.

Evop ¼ 0:00153  ft  DTwt

(31)

DTwt ¼ Tc;in  Tc;out

(32)

During the working process, part of the cooling water is evaporated. Some insoluble substances in the water precipitate during the working process. Therefore, to avoid fouling and some other negative phenomenon, a certain percentage of water needs blowdown. fb is the flowrate of water blowdown. Fresh water is provided to maintain the water amount. The amount of makeup water is represented by fm. Eq. (33) and Eq. (34) are employed to calculate fb and fm . pc is the cycle of concentration.

fb ¼

Evop

(33)

pc  1

fm ¼

Evop  pc pc  1

(34)

The outlet air humidity of cooling tower depends on the vapor pressure. The local air humidity is used as inlet air humidity. In Eq. (35), the water and air molecular weight are represented by Mw and Ma . The vapor pressure and the local atmospheric pressure are represented by ps and pa . ps is a function of mean temperature Tm in cooling tower (Kim and Smith, 2003).

Wout ¼

Mw ps  Ma pa  ps

(35)

j¼1

Dpt rw  g

Lnps ¼ 23:1 

4; 111 Tm þ 237:7

(36)

(27) To determine whether the auxiliary pump exists on a branch, a binary variable yðiÞ is applied. To fulfill the head requirement of a branch, supply head of main pump and auxiliary pump is combined. Hm is the pressure head of the main pump. Each branch has at most one auxiliary pump. Ha is the pressure head that an auxiliary pump provided. Eq. (28) and Eq. (29) are used to express the relationship above. n n o X Hm þ Ha ðiÞ  1  Zðj; iÞ  HðiÞ

(28)

j¼1 n X j¼1

yðiÞ  n 

n X n X

Zði; jÞ

Tm ¼

Tc;out þ Tc;in 2

(37)

In Eq. (38), Twb represents the air wet bulb temperature, DTtb is the temperature difference between outlet temperature of cooling tower and the temperature of air wet bulb.

DTtb ¼ Tc;out  Twb

(38)

In Eq. (39), Qt is the total heat load of cooling tower. The outlet temperature is related to the inlet temperature, Qt , and ft . Because makeup water temperature Tmw and water outlet temperature from tower is different, calculation of cooling water supply temperature is required, as shown in Eq. (40).

(29)

i¼1 j¼1

3.1.4. Cooling tower formulation To formulate the cooling tower, many factors need to be considered.

Tc;out ¼

Tfw ¼

Qt þ Tc;in cpw  ft

Tc;out  ðft  fm Þ þ Tmw  fm ft

(39)

(40)

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Z. Qi et al. / Journal of Cleaner Production 233 (2019) 793e807

3.2. Economic formulation

TAC ¼ CCw þ CCa þ OCf ;a þ CCm;p þ CCa;p þ OCm;p þ OCa;p þ OCt

When the objective is to minimize TAC, the relative capital cost and operation cost of all the network elements should be involved in objective function. Eq. (41) and Eq. (42) are used to determine the investment of water cooler (CCw ) and investment of air cooler (CCa ). aw , bw and cw are model parameters for water cooler cost. ba and ca are basic parameters to calculate the investment of air cooler. Af represents the annualized factor.

CCw ¼ Af 

n  X

aw þ bw  Acww ðiÞ



(41)

i¼1

CCa ¼ Af 

n  X  ba þ Acaa ðiÞ

(42)

i¼1

The operation cost of the air cooler is the electricity expense of fan expressed by OCf ;a. In Eq. (43), e is the electricity price, t is the plant operation time.

OCf ;a ¼ e  t  Pf ;a

(43)

In Eq. (44) and Eq. (45) CCm;p indicates the capital cost of main pump and CCa;p indicates the capital cost of auxiliary pump. ap , bp and g are model parameters.

  CCm;p ¼ Af  ap þ bp  ðft  Hm  gÞg

CCa;p ¼ Af 

n  X

ap  yðiÞ þ bp  ðfw ðiÞ  Ha ðiÞ  gÞg

(44) 

(45)

i¼1

The operation cost of pumps is power cost. Eq. (46) and Eq. (47) show the calculation of operation cost of main (OCm;p ) and auxiliary (OCa;p ) pumps. hp is the pump efficiency.

OCm;p ¼

OCa;p ¼

Hm  ft  g

hp

et

3.3. Environmental impact CWS consumes huge power and water. To analyze the environmental impact of different CWS design, water footprint and carbon footprint is used. 3.3.1. Water footprint To determine water footprint in CWS, the life cycle method is used to evaluate water footprint consumption, including the real water consumption in the operation and the virtual water consumption in the development and operation of iron and steel facilities (Scherer and Pfister, 2016). In operation stage, the water footprint of the system is the supplement of cooling water. The calculation of equipment such as coolers and pumps is converted to the water footprint of the steel used. When the equipment is working, the power consumption is directly converted into water footprint. For calculating the water footprint of steel, local water environmental effects and energy consumption need to be considered (Gu et al., 2015). The source of raw materials and the supply chain in the steel industry are difficult to obtain for the actual production process, and the water footprint of end-use and recycling process account for a small proportion in the decade-long steel, so it is negligible. Therefore, the steel water footprint assessment in this work is only for the steel production process. In Eq. (52) Swf is the water footprint per ton of steel, HEwf is the water footprint calculation of water cooler and air cooler. rst represents the density of the steel, and S represents the thickness of the heat exchanger.

hp

et

(47)

e  t  Cf  fa;t

hf

OCt ¼ OCf ;t þ 110  ft þ w  t  fm þ 1; 138  fb

(48)

(49)

In Eq. (50) CCt is the cooling tower capital cost. CCt is related to many factors.

n X i¼1

(46)

The cooling tower operational cost includes the cost of fan, the cost of make-up cooling water, and the water treatment cost. In Eq. (48) OCf ;t is the cost of fan in a cooling tower. Cf is the fan factor. hf is the fan efficiency. In Eq. (49) OCt is the cooling tower operational cost. w is the fresh water price.

OCf ;t ¼

(51)

HEwf ¼

X n  Ha ðiÞ  fw ðiÞ  g i¼1

þ CCt

Aw ðiÞ þ

n X

 Aa ðiÞ  rst  S  Swf

(52)

i¼1

For the water footprint of power, water footprint is based on standard coal firepower generation process. For the calculation of water footprints from different sources, Krishna Priya and Bandyopadhyay (2017) have performed detailed calculations, our method is based on their results.

ELwf ¼

OCm;p þ OCa;p þ OCf ;t þ OCf ;a  Ewf e  1; 000

(53)

3.3.2. Calculation of carbon footprint For CWS, industrial recycling water is directly used. The carbon footprint of water is calculated according to the life cycle method. In the system, water does not produce carbon emissions, so the carbon emissions of water are not included in the calculation of carbon footprint (Gu et al., 2015). Qi et al. (2018) analyzed the carbon footprint of steel companies by establishing a material flow model for the steel production process, the carbon footprint of steel per ton in each process is represented by Scf .



2:447 0:57 0:9924 CCt ¼ 746:74  f 0:79  D T  D T þ ð0:022  T þ 0:39Þ wb t wt tb

(50)

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Table 1 Hot stream data. Stream

Tin ( C)

Tout ( C)

Fcp (kW/h)

H (kW/m2$ C)

Installation height of coolers (m)

1 2 3 4 5 6 7 8 9 10

150 80 105 185 90 135 75 150 195 80

85 55 40 65 55 65 50 85 50 65

200 150 60 100 80 120 100 80 90 55

0.854 1.743 0.720 1.352 0.750 0.785 0.542 0.782 0.121 0.974

4 9 16 32 19 5 11 7 10 10

HEcf ¼

n X

Aw ðiÞ þ

i¼1

n X

Table 3 Environmental parameters of case study.



Aa ðiÞ  rst  S  Scf

(54)

i¼1

Water footprint of steel (m /kg) (Gu et al., 2015) Water footprint of power (m3/kWh) (Krishna Priya and Bandyopadhyay, 2017) Carbon footprint of steel (kg/kg) (Qi et al., 2018) Carbon footprint of power (kg/kWh) (Yang, 2018)

5,040 2.56  103 3,980 0.65

4.1. Optimization of CWS with economic objective

OCm;p þ OCa;p þ OCf ;t þ OCf ;a  Ecf e

(55)

In summary, In Eq. (56), WF is water footprint of the system, y is the number of auxiliary pumps. The water footprint of this system is calculated in Eq. (56).

WF ¼ HEwf þ 3:50 þ 2:98  y þ ELwf þ

Data 3

Yang (2018) proposed the decision model to realize the optimization of green power system based on the total cost of power system, on-grid price and carbon footprint. This includes a carbon footprint that consumes activity type electrical energy represented by Ecf , which is 0.65 kg/kWh. In Eq. (55), ELcf represents the carbon footprint of electricity.

ELcf ¼

Items

fm  t

(56)

rw

Eq. (57) is the calculation of the carbon footprint of the system represented by CF.

CF ¼ HEcf þ 2; 276:1 þ 2; 356:16  y þ ELcf

(57)

4. Case study A case study of a CWS from literature by Ma et al. (2018) is adopted to show the superiority of the proposed model. The case involves ten hot streams. The hot stream data is given in Table 1. Most physical and economic parameters of the case study can be found in Papers (Ma et al., 2018). Some physical and economic parameters of auxiliary pumps are selected from this document (Ma et al., 2017). The additional data and parameters are listed in Table 2. The environmental parameters of the case study are listed in Table 3. The DICOPT solver in the software GAMS is used to solve this problem.

Table 2 Physical parameters of case study. Items

Data

Density of cooling water rw Thickness of stainless steel of exchanger S Density of stainless steel rst The model of main pump The model of auxiliary pump

990 kg/m3 5.0  103m 7,900 kg/m3 250-200-315/37 250-200-315/55

For comparing, two optimization strategies are applied in the case. Firstly, a conventional CWS with air coolers is optimized and studied. In this configuration, the cooler networks are arranged in parallel and only main pump is used to deliver cooling water. Cooling water flow, air flow, cooler contact area and pressure drop are optimized to find out the best configuration and duty distribution. The optimal structure and operation condition are shown in Fig. 2. However, because cooling water is not reused, the water flow rate and the power consumption of CWS is high, leading to high cost of cooling towers, pumps and water coolers. Therefore, in order to reduce the flow rate of CWS and the TAC, it is necessary to optimize the cooler network. At the same time, by introducing the main and auxiliary structures, the pump network can be further improved to make it more in line with the actual industrial production. The optimal structure and operation condition are shown in Fig. 3. By comparison of the two structures, it can be found that the hot streams with air coolers remain unchanged. By comparing the data of hot streams, it can be found that the inlet temperatures of the five hot streams with air cooler are all greater than 135  C, and the heat load is relatively larger than other hot streams. Therefore, it can be seen that when the inlet temperature of the hot stream is high and the heat load is large, air coolers are preferred. On the basis of the original structure, the optimized structure is changed from ten branches to five branches, and auxiliary pumps are added to three of them. The detail results for the two designs are shown in Table 4 and Fig. 4. From the results, it can be found that, by using series-parallel structure and main-auxiliary pump structure, the water cooler related cost is reduced. The first reason is that, by applying series-parallel structure, the flow rate of cooling water is reduced. In this work, for both parallel and series-parallel structure, cooling tower inlet temperature has reached its upper bound (55  C to avoid fouling). Therefore, under the fixed total cooling load, the decrease of cooling water flow rate represents the decrease of heat load in water cooler and the increase of total heat load in air cooler, as shown in Fig. 5. The above

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Fig. 2. Original parallel configuration with air cooler diagram.

reason leads to a reduction in pump, cooling tower and water cooler related cost. The second reason for the cost reduction is that the overall power consumption of the pump can be reduced by applying mainauxiliary pump structure. In parallel structure, the main pump supply pressure head is 33.95 m. In series-parallel structure with main-auxiliary pumps, 3 auxiliary pumps are added. The auxiliary pump head on the branch in series with the cooler Ew-3 and the cooler Ew-1 is 6.00 m. The auxiliary pump head on the branch in series with the cooler Ew-5 and the cooler Ew-8 is 9.00 m. The auxiliary pump head on the branch in series with the cooler Ew-7 and the cooler Ew-4 is 22.00 m. The main pump head is 11.25 m. By using auxiliary pumps, the head requirement of each branch can be precisely matched, leading to a reduction in total pump power. From Fig. 4, it can be seen that a reduction of 9.84% in TAC is achieved, indicating the effectiveness of the proposed method based on economic performance. At the same time, through literature research, it can be seen that the current research and model

establishment of CWS cannot cover all the individual components. Compared with Liu's model (2018), for the whole circulating water system, this model carefully considers the components of each part. Under the condition of introducing air cooler, the economic cost can be reduced by 9.84%. At the same time, compared with the models of Sun et al. (2015) and Zheng et al. (2018), the model takes the existence of air cooler as the precondition of circulating water system, avoiding unnecessary economic costs caused by scaling of water cooler during operation. Adding more thermal logistics and considering series-parallel structure makes the model more extensive and universal, and the model is more in line with industrial reality. 4.2. Optimization of CWS with environmental objective As mentioned, CWS consumes both power and water, besides economic performance, it should also be optimized with the objective of environmental impact. However, the environmental

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801

Fig. 3. Optimization of the cooler network and pump network diagram.

Table 4 Comparison of the physical data of the different optimization methods.

Total flow rate (kg/s) Total area of the cooling exchangers (m2) Total area of the air cooling exchangers (m2) Heat load of cooling tower (MW) Heat load of air coolers (MW) Pressure drop (kPa) Main pump pressure head(m)

Original parallel configuration

Series-parallel structure with main-auxiliary pumps

297.11 2,610.02 4,007.38 43.47 21.96 18.90 33.95

246.15 2,191.48 5,776.97 36.01 29.41 12.16 11.25

study of CWS has not been involved in the existing research. For air cooler, it consumes power and requires larger heat transfer area. For water cooler, it consumes power and more water. Carbon footprint and water footprint are quantified systems representing environmental impacts by greenhouse gas emissions and water

consumption. In this study, carbon footprint and water footprint are used to evaluate the environmental impact, and the trade-off between carbon footprint and water footprint is made with the goal of minimum TAC. Water footprint and carbon footprint are studied under different cooling load distribution in coolers.

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Cost of water coolers

Original parallel configuration

143.90

Series-parallel structure with main-auxiliary pumps

125.91

Cost of air coolers

272.72

Cost of main pump

Cost of auxiliary pump

101.66

Cost of cooling tower

651.57

339.77

1,169.85

1,054.76

540.46

17.86

30.76

Fig. 4. Comparison of economic costs of the two structures (k$).

Original parallel configuration

Series-parallel structure with main-auxiliary pumps

Air 33.56% 21.96 MW

Air 44.96%

Water 55.04%

29.41 MW

36.01 MW

Water 66.44% 43.67 MW

195

136.18

117.20

50

195

115.72

106.44

50

Air cooling Water cooler heat load Water cooling Air cooler heat load

Mixed

Fig. 5. Comparison of heat load and breakpoint temperature distributions in two structures.

Fig. 6 is used to shows the water footprint distribution when the system structure is at the minimum TAC. It can be clearly seen that when the water footprint is used as an environmental impact

Electricity 1.00% Steel 0.31%

Makeup water 98.69%

Fig. 6. Water footprint with minimum TAC.

indicator, the resource consumption of supplementary water accounts for 98.69%. Other elements such as electricity and steel resources of equipment are almost negligible. It can be conjectured that the water footprint is the smallest when only air coolers are used. For further verification, an optimization is carried out with the objective to minimize water footprint. Fig. 7 shows the structure with the lowest water footprint. When the objective equation is to minimize the water footprint, no water coolers exist in the whole CWS and therefore no main and auxiliary pumps, all the heat streams are cooled only by air coolers. This is because the water footprint mostly comes from the cooling water evaporation and blowdown, so when the water footprint is the smallest, the heat load of the water cooler is zero. However, in the practical application of industry, this case is virtually nonexistent. Therefore, the relationship between heat load distribution of water cooler and water footprint is studied for further discussion. Fig. 8 shows the relationship between the heat load distribution between water cooling and air cooling and water footprint under the objective equation of minimum TAC. Since the heat load of

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803

Fig. 7. Structure with the lowest water footprint.

Minimum TAC

Minimum TAC

Fig. 8. The relationship between heat load distribution of water coolers and water footprint.

Fig. 9. The relationship of water footprint and TAC.

water cooler dominates the change of water footprint, the curve is basically a linear relationship with positive correlation. In Fig. 8, each point in the diagram represents the water footprint with the minimum TAC as the objective equation under a fixed water cooling load. The water footprint increases with the increase of the heat load distribution of water cooler. Therefore, when it is necessary to reduce the discharge of water resources, the load distribution of water coolers can be appropriately reduced to achieve the goal of cleaner production. Fig. 9 shows the relationship between TAC and water footprint, with the horizontal coordinates representing the water footprint and the ordinate representing the TAC. It can be seen that as the water footprint increases, the TAC decreases at the beginning and then increases. It can be seen that when all the cooling duty is cooled down by air cooler, water footprint tends to be very low. The trend of the curve also shows that it is uneconomical to use too many air coolers due to very high requirement of heat transfer area. This diagram helps to trade off the relation between water footprint and TAC. The design decision and adjustment can be made according to the chart, and the TAC of the system can be increased

appropriately with a reduction in the consumption of water resources. At the same time, the chart can better guide the establishment of CWS in the follow-up industry, that is, not only to pursue the minimum structure of TAC, but also to synthesize the situation of water resources in different regions. Establishing a CWS with relatively low water consumption and relatively low economic cost, the purpose of clean and economic production can be achieved. Fig. 10 is used to show the case when the system structure is at the minimum TAC. When carbon footprint is taken as an environmental indicator, the impact of steel resource consumption and electricity consumption accounts for about 50%. In terms of the impact of steel resource consumption, the proportion of steel resources consumed by pumps can be neglected. The carbon footprint ratio of air cooler to water cooler is about 3:1 based on the consumption of steel resources. This is because the area of air cooler is usually much larger than that of water cooler. In terms of the impact of electricity consumption, the proportion of electricity consumption of pumps, air coolers and cooling towers is quite similar. From the results, water cooler also has higher power consumption and power based carbon footprint. However, from the

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Air cooler Cooling tower 35.00% Air cooler 38.85%

Electricity 50.96%

Cooling tower Pump

Pump 26.15%

Electricity

Steel

Steel 49.04%

Water cooler 27.50% Air cooler 72.40%

Air cooler Pump 0.10%

Water cooler

Pump

Fig. 10. Carbon footprint with minimum TAC.

view of total carbon footprint, air coolers contributes approximate 55% due to the very high heat transfer area. Therefore, considering the consumption of steel resources and electricity, the heat load of air cooler has a greater impact on carbon footprint. Fig. 11 shows the structure with the lowest carbon footprint. Compared with the TAC optimal results, the number of air cooler is reduced, and the auxiliary pump is widely applied. The reason is that the air cooler contributes about 55% to the carbon footprint. And cooling the hot stream of the same heat load requires a larger area of the air cooler, resulting in a larger carbon footprint. Therefore, in the process of optimization, the model will choose to reduce the use of air coolers. The effect of the auxiliary pump on the carbon footprint is very small and almost negligible. At the same time, as the heat load of the air cooler decreases and the water flow increases, auxiliary pumps will be used as much as possible. The heat load of water cooler accounts for 97.69%. Therefore, based on the above two points, duty distribution of water coolers can be increased when it is required to reduce greenhouse gas emissions. Based on the above analysis, Fig. 12 shows the relationship between the heat load distribution and carbon footprint from different structure. Because the heat load of air cooler has a great influence on the carbon footprint, the carbon footprint decreases with the increase of the heat load distribution of water cooler until the minimum carbon footprint is reached, as shown in Fig. 12. Each point in the diagram represents the carbon footprint at the minimum TAC under a fixed water cooling load. According to Fig. 12, in areas where carbon dioxide emissions need to be reduced, the heat load of water coolers can be increased appropriately in a newly built CWS to ensure cleaner production, reduce carbon dioxide emissions and avoid greenhouse effects, while ensuring the minimum TAC. Fig. 13 shows a schematic diagram of the relationship between TAC and carbon footprint, with horizontal coordinates representing the carbon footprint and ordinate representing TAC. From the figure, as the carbon footprint increases, the TAC decreases first and then increases. This diagram helps to trade off the relation between carbon footprint and TAC. Design decision can be made according to this figure when greenhouse emission problem is crucial in local. TAC can be increased appropriately to reduce greenhouse gas emissions. The chart can help policy makers to balance the environmental impact of greenhouse gas emissions with economic costs. In order to achieve cleaner production, the newly built CWS

should be mainly taken from the left of the minimum TAC, which fully combines environmental and economic objectives. To illustrate the trade-off between carbon footprint and water footprint, the relationship is given in Fig. 14. In the figure, the horizontal coordinate represents water footprint and the vertical coordinate represents carbon footprint. The points are calculated through carbon footprint changes at the lowest annual total cost under different water footprints. Because each point on the chart is with minimum TAC under different conditions, this chart can also help to establish the lowest cost CWS under the condition of fixed water footprint and carbon footprint. Carbon footprint and water footprint of the system are directly related to the duty distribution among air coolers and water coolers. As can be seen in Fig. 14, the CWS with only air coolers has the lowest water footprint and the highest carbon footprint. The CWS using only water coolers has the highest water footprint and lowest carbon footprint. The figure clearly shows the relation between water footprint and carbon footprint. It can be used to help the decision-making of CWS based on local water and carbon situations. When the greenhouse emission is the bottleneck, the duty of water coolers should be increased to keep greenhouse emission at a lower level. When the local water resource is limited, duty of air coolers should be increased to reduce water consumption. At the same time, on both sides of the optimal TAC point, the slope of the curve has changed, and the breakpoint appears near the minimum point of TAC. In summary, the minimum of TAC can be used as a compromise point. The chart also provides guidance for a new type of CWS. It not only considers the waste of water resources and greenhouse gas emissions, but also determines the appropriate structure of CWS at the best TAC point on both sides of the point in accordance with the local actual situation.

5. Conclusion In this study, a CWS optimization framework is proposed considering water cooler network, pump network, cooling tower and air coolers all together. Economic costs and environmental impacts are considered as objective functions to optimize the whole system separately. Water footprint and carbon footprint is used as environmental indicators. The proposed model allows the system to automatically determine the structure of cooler network, the installation of auxiliary pumps, and the duty distribution among water and air coolers.

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Fig. 11. Structure with the lowest carbon footprint.

From the results based on economic performance, it is found that TAC can be further reduced by considering air cooler, pump and water coolers all together. In the case study, compared with the original parallel configuration, the series-parallel structure with main-auxiliary pumps reduces TAC from 1,170k$ to 1,055k$, with a

reduction of 9.84%. And the proportion of the heat load of water coolers decreases from 66.44% to 55.04%. When considering water footprint in optimization, the resource consumption of supplementary water accounts for 98.69%, showing that the structure with only air coolers is the design with

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the lowest water footprint. However, by adding more water coolers, the optimum design with minimum carbon footprint can be realized. At the same time, due to the low carbon footprint contribution of the pump, the increase of water cooling load and water flow, the design tends to use more auxiliary pumps. In this paper, the relationship between economic performance and water footprint and carbon footprint is given. The negative correlation between water footprint and carbon footprint is also given. It can help decision makers to balance the relationship between water resources and greenhouse gas emissions in different regions. In order to achieve the goal of cleaner production, the load of air cooler should be increased appropriately to reduce water consumption when local water resources are limited. Conversely, when greenhouse gas emission becomes a bottleneck, the load of water cooler should be increased to keep the greenhouse gas emission level at a low level.

Minimum TAC

Acknowledgements Fig. 12. The relationship between heat load distribution of water coolers and carbon footprint.

Financial support from the National Natural Science Foundation of China under Grant No. 21576286 and Science Foundation of China University of Petroleum, Beijing (No. 2462017BJB03 and 2462018BJC004) are gratefully acknowledged. References

Minimum TAC

Fig. 13. The relationship of carbon footprint and TAC.

Air coolers

Minimum TAC

Water coolers

Fig. 14. The curve of water footprint and carbon footprint.

Alinia Kashani, A.H., Maddahi, A., Hajabdollahi, H., 2013. Thermal-economic optimization of an air-cooled heat exchanger unit. Appl. Therm. Eng. 54 (1), 43e55. Balaguera, A., Carvajal, G.I., Albertí, J., Fullana-i-Palmer, P., 2018. Life cycle assessment of road construction alternative materials: a literature review. Resour. Conserv. Recycl. 132, 37e48. Chen, J.J.J., 1987. Comments on improvements on a replacement for the logarithmic mean. Chem. Eng. Sci. 42 (10), 2488e2489. Chen, S., Chen, B., 2016. Urban energyewater nexus: a network perspective. Appl. Eng. 184, 905e914. Doodman, A.R., Fesanghary, M., Hosseini, R., 2009. A robust stochastic approach for design optimization of air cooled heat exchangers. Appl. Eng. 86 (7), 1240e1245. Doroti c, H., Puksec, T., Dui c, N., 2019. Multi-objective optimization of district heating and cooling systems for a one-year time horizon. Energy 169, 319e328. Esen, H., Inalli, M., Esen, M., 2006. Technoeconomic appraisal of a ground source heat pump system for a heating season in eastern Turkey. Energy Convers. Manag. 47 (9), 1281e1297. Esen, H., Inalli, M., Esen, M., 2007. A techno-economic comparison of groundcoupled and air-coupled heat pump system for space cooling. Build. Environ. 42 (5), 1955e1965. Esen, M., Yuksel, T., 2013. Experimental evaluation of using various renewable energy sources for heating a greenhouse. Energy Build. 65, 340e351. Feng, X., Shen, R., Wang, B., 2005. Recirculating cooling-water network with an intermediate cooling-water main. Energy Fuel. 19 (4), 1723e1728. Gololo, K.V., Majozi, T., 2012. Pressure Drop Consideration in Cooling Water Systems with Multiple Cooling Towers. Computer Aided Chemical Engineering, pp. 690e694. Gu, Y., Xu, J., Keller, A.A., Yuan, D., Li, Y., Zhang, B., Li, F., 2015. Calculation of water footprint of the iron and steel industry: a case study in Eastern China. J. Clean. Prod. 92, 274e281. Herath, I., Green, S., Singh, R., Horne, D., van der Zijpp, S., Clothier, B., 2013. Water footprinting of agricultural products: a hydrological assessment for the water footprint of New Zealand's wines. J. Clean. Prod. 41, 232e243. Herrmann, I.T., Hauschild, M.Z., 2009. Effects of globalisation on carbon footprints of products. CIRP. Anna. 58 (1), 13e16. Hoekstra, A.Y., 2015. Chapter 7 - the water footprint of industry. In: Klemes, J.J. (Ed.), Assessing and Measuring Environmental Impact and Sustainability. Butterworth-Heinemann, Oxford, pp. 221e254. Isafiade, A.J., Short, M., Bogataj, M., Kravanja, Z., 2017. Integrating renewables into multi-period heat exchanger network synthesis considering economics and environmental impact. Comput. Chem. Eng. 99, 51e65. Jeswani, H.K., Azapagic, A., 2011. Water footprint: methodologies and a case study for assessing the impacts of water use. J. Clean. Prod. 19 (12), 1288e1299. Kakaç, S., Liu, H.J.T., Technology, L., 2012. Heat Exchangers Selection, Rating and Thermal Design. Kang, Z., Dong, J., Zhang, Y., Zhou, X., Feng, G., 2017. Performance simulation of surface air cooler with bypass valve. Procedia. Eng. 205, 3019e3026. Keshtkar, M.M., Talebizadeh, P., 2017. Multi-objective optimization of cooling water package based on 3E analysis: a case study. Energy 134, 840e849. Kim, J.-K., Smith, R., 2001. Cooling water system design. Chem. Eng. Sci. 56 (12), 3641e3658.

Z. Qi et al. / Journal of Cleaner Production 233 (2019) 793e807 Kim, J.-K., Smith, R., 2003. Automated retrofit design of cooling-water systems. AIChE J. 49 (7), 1712e1730. Krishna Priya, G.S., Bandyopadhyay, S., 2017. Multi-objective pinch analysis for power system planning. Appl. Eng. 202, 335e347. Laurent, A., Olsen, S.I., Hauschild, M.Z., 2010. Carbon footprint as environmental performance indicator for the manufacturing industry. CIRP. Anna. 59 (1), 37e40. Liu, F., Ma, J., Feng, X., Wang, Y., 2018. Simultaneous integrated design for heat exchanger network and cooling water system. Appl. Therm. Eng. 128, 1510e1519. Ma, J., Wang, Y., Feng, X., 2017. Simultaneous optimization of pump and cooler networks in a cooling water system. Appl. Therm. Eng. 125, 377e385. Ma, J., Wang, Y., Feng, X., Xu, D., 2018. Synthesis cooling water system with air coolers. Chem. Eng. Res. Des. 131, 643e655. Manassaldi, J.I., Scenna, N.J., Mussati, S.F., 2014. Optimization mathematical model for the detailed design of air cooled heat exchangers. Energy 64, 734e746. Muller, C.J., Craig, I.K., 2017. Economic hybrid non-linear model predictive control of a dual circuit induced draft cooling water system. J. Process Control 53, 37e45. Pal, S.K., Takano, A., Alanne, K., Siren, K., 2017. A life cycle approach to optimizing carbon footprint and costs of a residential building. Build. Environ. 123, 146e162. Pettersson, F., Westerlund, T., 1997. Global optimization of pump configurations using binary separable programming. Comput. Chem. Eng. 21 (5), 521e529. lez, M., Jime nez-Gutie rrez, A., 2007. MINLP synPonce-Ortega, J.M., Serna-Gonza thesis of optimal cooling networks. Chem. Eng. Sci. 62 (21), 5728e5735. lez, M., Jime nez-Gutie rrez, A., 2010. Optimization Ponce-Ortega, J.M., Serna-Gonza model for re-circulating cooling water systems. Comput. Chem. Eng. 34 (2), 177e195. Qi, Z., Gao, C., Na, H., Ye, Z., 2018. Using forest area for carbon footprint analysis of typical steel enterprises in China. Resour. Conserv. Recycl. 132, 352e360. Rubio-Castro, E., Serna-Gonz alez, M., Ponce-Ortega, J.M., El-Halwagi, M.M., 2013. Synthesis of cooling water systems with multiple cooling towers. Appl. Therm. Eng. 50 (1), 957e974. Sayyaadi, H., Nejatolahi, M., 2011. Multi-objective optimization of a cooling tower assisted vapor compression refrigeration system. Int. J. Refrig. 34 (1), 243e256. Scherer, L., Pfister, S., 2016. Global water footprint assessment of hydropower. Renew. Energy 99, 711e720. lez, M., Ponce-Ortega, J.M., Jime nez-Gutie rrez, A., 2010. MINLP optiSerna-Gonza mization of mechanical draft counter flow wet-cooling towers. Chem. Eng. Res. Des. 88 (5), 614e625. Shaikh, M.A., Kucukvar, M., Onat, N.C., Kirkil, G., 2017. A framework for water and

807

carbon footprint analysis of national electricity production scenarios. Energy 139, 406e421. Soltani, H., Shafiei, S., 2011. Heat exchanger networks retrofit with considering pressure drop by coupling genetic algorithm with LP (linear programming) and ILP (integer linear programming) methods. Energy 36 (5), 2381e2391. Song, F., Zhang, N., Smith, R., Zeng, Y., Li, J., Xiao, X., 2018. Simultaneous Optimization for Integrated Cooling Water System with Chemical Processes. Computer Aided Chemical Engineering, pp. 477e482. Souza, R.D., Khanam, S., Mohanty, B., 2016. Synthesis of heat exchanger network considering pressure drop and layout of equipment exchanging heat. Energy 101, 484e495. Sun, J., Feng, X., Wang, Y., 2015. Cooling-water system optimisation with a novel two-step sequential method. Appl. Therm. Eng. 89, 1006e1013. Sun, J., Feng, X., Wang, Y., Deng, C., Chu, K.H., 2014. Pump network optimization for a cooling water system. Energy 67, 506e512. n-Gosa lbez, G., Jime nez, L., 2012. Multi-objective design of heatVaskan, P., Guille exchanger networks considering several life cycle impacts using a rigorous MILP-based dimensionality reduction technique. Appl. Eng. 98, 149e161. Wang, Y., Chu, K.H., Wang, Z., 2014. Two-step methodology for retrofit design of cooling water networks. Ind. Eng. Chem. Res. 53 (1), 274e286. Westerlund, T., Pettersson, F., Grossmann, I.E., 1994. Optimization of pump configurations as a MINLP problem. Comput. Chem. Eng. 18 (9), 845e858. Yang, C.-H., 2018. An optimization portfolio decision model of life cycle activitybased costing with carbon footprint constraints for hybrid green power strategies. Comput. Oper. Res. 96, 256e271. Zhang, H., Feng, X., Wang, Y., 2018. Comparison and evaluation of air cooling and water cooling in resource consumption and economic performance. Energy 154, 157e167. Zhang, N., Choi, Y., 2013. Total-factor carbon emission performance of fossil fuel power plants in China: a metafrontier non-radial Malmquist index analysis. Energy Econ. 40, 549e559. Zheng, C., Chen, X., Zhu, L., Shi, J., 2018. Simultaneous design of pump network and cooling tower allocations for cooling water system synthesis. Energy 150, 653e669. Zhu, X., Niu, D., Wang, F., Guo, Y., Jia, M., 2019. Operation optimization research of circulating cooling water system based on superstructure and domain knowledge. Chem. Eng. Res. Des. 142, 285e297. Zhu, X., Wang, F., Niu, D., Zhao, L., 2017. Integrated modeling and operation optimization of circulating cooling water system based on superstructure. Appl. Therm. Eng. 127, 1382e1390.