Analysis and Optimization of Open Circulating Cooling Water System

Article Analysis and Optimization of Open Circulating Cooling Water System

Ziqiang Lv 1,2,3, Jiuju Cai 2, Wenqiang Sun 1,2,* and Lianyong Wang 1,2

1 Department of Thermal Engineering, School of Metallurgy, Northeastern University, Shenyang 110819, Liaoning, China; [email protected] (Z.L.); [email protected] (L.W.) 2 State Key Laboratory of Eco-Industry, Northeastern University, Shenyang 110819, Liaoning, China; [email protected] 3 School of Civil Engineering, University of Science and Technology Liaoning, Anshan 114051, Liaoning, China * Correspondence: [email protected]

Received: 22 September 2018; Accepted: 26 October 2018; Published: 7 November 2018

Abstract: Open circulating cooling water system is widely used in process industry. For a system with a fixed structure, the water consumption and blowdown usually change with the varying parameters such as quality and temperature. With the purpose of water saving, it is very important to optimize the operation strategy of water systems. Considering the factors including evaporation, leakage, blowdown and heat transfer, the mass and energy conservation equations of water system are established. On this basis, the quality and temperature models of makeup and blowdown water are, respectively, developed. The water consumption and discharge profiles and the optimal operating strategy of the open recirculating cooling water system under different conditions are obtained. The concept of cycles of temperature is proposed to evaluate the temperature relationship of various parts of the open circulating cooling water system. A mathematical relationship is established to analyze the influence of the water temperature on the makeup water rate of the system under the condition of insufficient cooling capacity of the cooling tower. In addition, the co-influences of quality and temperature parameters on the system are analyzed.

Keywords: cooling water system; operating strategy; mass and energy conservation; water saving

1. Introduction Open circulating cooling water system is widely used in process industry to cool off equipment and product, and to transfer waste heat into the environment [1,2]. Due to the evaporation and leakage of water, as well as the sewage of the system, this process brings about the consumption of water resources and the sewage drainage. As water resources decrease year by year and the serious environmental pollution increases [3], more and more attention has been paid to water conservation and emission reduction of the open cooling water system. At present, the research of industrial water saving mainly includes two aspects [4]: using water-saving equipment or water-alternative technology (such as air cooling instead of water cooling) and optimizing the structure and operating parameters of the water system as a whole, which is the focus of this paper. The optimization of industrial water systems mainly includes two categories. (1) Water pinch analysis [5–9] belongs to the graphic method and has the advantages of clearly physical meaning and simple solution. However, in general, it is only valid for a single impurity system, and cannot give the corresponding optimal structure of water consumption systems. (2) Mathematical programming method: Apart from water pinch analysis, more and more research employs the mathematical programming method [10–13]. For the optimization of water systems by mathematical programming,

Water 2018, 10, 1592; doi:10.3390/w10111592 www.mdpi.com/journal/water Water 2018, 10, 1592 2 of 16 it is necessary to establish an objective function and a set of appropriate equality and/or inequality constraint equations. Depending on task requirement, the objective function can generally be either the minimum fresh water consumption or investment, or a multi-objective function. This method can solve the relatively complicated water system optimization problem, but none or multiple global optimal solution could be obtained, which may lead to poor engineering feasibility [14]. In recent years, much attention has been paid to the optimization of open circulating cooling water systems, that is a particular kind of industrial water system. Castro et al. [15] developed the concept of multiple pinches for minimum water consumption of regeneration reuse system. Kim and Smith [16] presented a new method for the design of cooling water systems with lower water usage based on the interactions between the cooling tower performance and the cooling water users by combining mathematical model with pinch analysis method. Meanwhile, the water saving methods in debottlenecking situations are analyzed qualitatively. Kim et al. [17] discussed the design of a cooling water system under the condition of effluent temperature reduction by using pinch analysis method. Jiang et al. [18] developed a production scheduling optimization model to solve the excessive water consumption caused by product adjustment in the dyeing industry. The model can be used to reduce the production line cleaning time by optimizing the production schedules. Ponce-Ortega et al. [19] built a superstructure model for the whole cooling water system, and then translated it into a mixed-integer nonlinear programming problem. The objective is to find the minimized equipment investment and the total cost of filling water for the cooling water systems. The system structure parameter is determined by optimal computation. Sun et al. [20] presented a novel two-step sequential methodology for the optimization of cooling water system. The first step is to use a thermodynamic model to obtain the optimal cooler network. In the second step, the hydraulic model is established to obtain the optimal pump network. The proposed model can identify the optimal distribution of cooling water within the network and the optimal installation locations and pressure head of pumps required for CWS. Bu [21] analyzed the characteristics of water systems in steel industry, established mathematical models, and discussed the relationship between water recharge and the cycles of concentration. In the above-mentioned studies, the role of the system operating parameters (water quality and temperature) is to restrict the import and export limit parameters of each water flow and cooling tower as constraints, with the aim of obtaining an optimal system architecture. Few studies discussed the effects of different water quality and temperature parameters on water consumption and drainage. In [16], the trend of water consumption with the change in water temperature has been qualitatively discussed. The mathematical relationship between cooling capacity and makeup water temperature has not been reported in previous studies; thus, the influences of makeup water temperature and the cooling capacity on the system makeup rate are not analyzed. Conversely, for a fixed structure of an open circulating cooling water system, the system parameters (such as quality, temperature, etc.) and the operation strategies are variable, which inevitably causes changes of indices such as water consumption and blowdown. It is of great significance for water saving and drainage reduction to analyze the aforementioned relationships. The water quality and temperature will change in an open cooling water system and this change has a significant impact on the makeup, drainage and operating strategies of the system. In this work: (1) The mode and characteristics of the water that are used in the open cooling water system are analyzed, and then the physical and mathematical models are established to describe the relationship between water supply and drainage, water quality and water temperature. (2) The influence of water quality on water consumption and blowdown is obtained by solving the mathematical model. In addition, the influence of the actual factors such as water leakage and the installation of purification equipment are analyzed, and then the optimal operation strategies for the system under different leakage conditions are obtained. (3) The evaluation index used to evaluate the cooling capacity of the cooling tower and the water temperature of the system is established. The influence of water temperature parameters on water supply under the condition of insufficient cooling capacity of the cooling tower is proposed. Although the water, material and energy balance models have often Water 2018, 10, 1592 3 of 16 Water 2018, 10, x FOR PEER REVIEW 3 of 16

transfer are integrated into the traditional mass and energy conservation models, which is more been applied to cooling tower systems, in this paper, the water evaporation, leakage, blowdown consistent with the practice. Another novelty of this paper is the proposal of the concept of cycles of and heat transfer are integrated into the traditional mass and energy conservation models, which is temperature. It is proposed to evaluate the temperature relationship of various parts of the open more consistent with the practice. Another novelty of this paper is the proposal of the concept of circulating cooling water system. The novel mathematical model is established between the makeup cycles of temperature. It is proposed to evaluate the temperature relationship of various parts of the water rate and the cycles of temperature. In addition, the co-effect of quality and temperature open circulating cooling water system. The novel mathematical model is established between the parameters on the system is analyzed, which is different from previous studies considering only one makeup water rate and the cycles of temperature. In addition, the co-effect of quality and temperature parameter as the constraint. parameters on the system is analyzed, which is different from previous studies considering only one parameter as the constraint. 2. Methods 2. MethodsIn this part, the physical model of the open cooling water system is firstly established. Based on the principleIn this part, of energy the physical conservation model of and the mass open conservation, cooling water the system mathematical is ﬁrstly established. model is established Based on theto principledescribe of the energy relationship conservations between and mass water conservation, supply and the drainage, mathematical water model quality is established and water to describetemperature. the relationships between water supply and drainage, water quality and water temperature.

2.1.2.1. Physical Model FigureFigure1 1 shows shows a a typical typical open open circulating circulating cooling cooling water water system. system. The The waste waste heat heat that that exists exists in in the the coolingcooling networknetwork (CW)(CW) needsneeds toto bebe takentaken awayaway byby aa coolingcooling water.water. CoolingCooling waterwater entersenters thethe coolingcooling towertower (CT)(CT) toto releaserelease heatheat toto thethe environmentenvironment afterafter leavingleaving thethe coolingcooling network.network. WaterWater evaporationevaporation andand leakageleakage maymay happenhappen duringduring thisthis process.process. After cooling, the water returnsreturns toto the cooling network forfor reuse. reuse. Due to evaporation, evaporation, impurities impurities (such (such as as salt) salt) in in the the water water areare concentrated.concentrated. Excessive Excessive concentrationconcentration of coolingcooling waterwater wouldwould resultresult inin thethe damagedamage ofof heatheat transfertransfer equipment.equipment. Therefore, makeupmakeup waterwater isis usuallyusually usedused toto partlypartly replacereplace thethe circulatingcirculating waterwater in thethe system,system, andand toto keepkeep thethe impurityimpurity concentrationconcentration inin moderation.moderation. To maintainmaintain waterwater balance,balance, thethe systemsystem makeupmakeup waterwater shouldshould includeinclude all the the water water loss losseses caused caused by byevaporation, evaporation, leakage leakage and andblowdown blowdown.. The water The waterquantity, quantity, water watertemperature temperature and water and waterquality quality of the system of the system are represented are represented by different by different symbols, symbols, as shown as shownin Figure in Figure1. 1.

Evaporated water FV

Fi ti Ci CW

Inlet water CT l Network F tR CR QR Leakage water Qi

i F tn Ci W F t C R FP t C W W F tR CR R R Blowdown water Makeup water Outlet water

Figure 1. Physical model of open circulating cooling water system. Figure 1. Physical model of open circulating cooling water system. The open cooling water model is based on the following assumptions: TheThe systemopen cooling is in a water stable model state. is based on the following assumptions: TheThe heatsystem radiation is in a stable is ignored. state. TheThe speciﬁcheat radiation heat of is water ignored is supposed. independent of impurities and temperature. DriftThe specific from the heat cooling of water tower is supposed is negligible. independent of impurities and temperature. AllDrift dissolved from the constituents cooling tower of interestis negligible are conservative.. TheAll dissolved heat transfer constituents of other parts of interest except are the conservative cooling network. and the cooling tower is ignored. AllThe water heat transfer leakage of loss other of the parts system except is generatedthe cooling at network the outlet and of the cooling tower.tower is ignored. WithAll water regard leakage to the loss assumption of the system of the is open generated circulating at the cooling outlet of water the cooling system, tower. this paper refers to the relatedWith regard practices to the in the assumption literature of [16 the,17 ,open19,20 circulating,22]. A signiﬁcant cooling difference water system, is that, this instead paper ofrefers being to ignored,the related the practices water leakage in the lossliterature of the [16,17,19 system is,20,22 analyzed]. A significant as an important difference factor is that to be, instead closer to of reality. being ignored, the water leakage loss of the system is analyzed as an important factor to be closer to reality.

Water 2018, 10, 1592 4 of 16

2.2. Mathematical Models

2.2.1. Water Quantity, Impurity and Energy Balance Models The water quantity, impurity mass and heat conservation equation are established based on the heat and impurity exchange characteristics of the system. According to Kim et al. [17], the relationship between the total heat dissipation QR of the cooling tower and the latent heat of water evaporation QV can be expressed as: V CE·QR = QV = F ·r (1) where CE is the ratio of evaporation heat transfer and total heat transfer with the value of 0.75–1.0, and r is the latent heat of vaporization of water. For the cooling network, Water balance: FW + FR = Fi (2)

Impurity mass balance: W R i F ·CW + F ·CR = F ·Ci (3) Heat balance: Q FW ·t + FR·t + i = Fi·t (4) W R c i Q Fi·t + i = Fi·t (5) n c i where c is the constant pressure speciﬁc heat of water. For the cooling tower, Water balance: Fi = Fl + FV + FP + FR (6)

Impurity mass balance: i l P R F ·Ci = F ·CR + F ·CR + F ·CR (7) Heat balance: V i i rF c·F ·ti = c·F ·tR + (8) CE The control equations of the system can be obtained by the following: Water balance: FW = Fl + FP + FV (9)

Impurity mass balance: W l P F ·CW = F + F ·CR (10)

Heat balance: V W l P V F ·r c·F ·tW + Qi = c· F + F + F ·tR + (11) CE where f W, f l, f P and f V are deﬁned as the makeup rate, leakage rate, blowdown rate and evaporation rate, respectively, and thus FW = f W ·Fi, Fl = f l·Fi, FP = f P·Fi and FV = f V ·Fi. According to Equations (1) and (8), c f V = C · ·(t − t ) (12) E r i R where ti and tR are the inlet and outlet water temperatures of the cooling tower, respectively. Then, Equations (9)–(11) can be rewritten as: Water 2018, 10, 1592 5 of 16

Water balance: f W = f l + f P + f V (13)

Impurity mass balance: W l P f ·CW = f + f ·CR (14)

Heat balance: W l P V f ·tW + (tR − tn) = f + f + f ·tR (15)

2.2.2. System Makeup and Drainage Rate vs. Water Quality and Temperature Models Firstly, the mathematical relationship between water makeup rate and water quality parameters is established. There are many kinds of water quality parameters in the water system. According to Rahmani [23], the key factor in the design and operation of open circulating cooling water system is the cycles of concentration. The cycles of concentration (N) is deﬁned as the ratio of the concentration of a soluble component in the blowdown stream to that in the makeup stream. In this paper, the cycles of concentration is used as a representative indicator of water quality. Hereafter, the reference to water quality means the cycles of concentration. According to Equations (13) and (14),

C f W f V N = R = = + P l 1 P l (16) CW f + f f + f

According to Bu [21], for the open cooling water system, it is more appropriate to control the cycles of concentration within the range of 3–5. Low cycles of concentration can result in excessive makeup rate; on the contrary, it can lead to a large increase in the risk of equipment corrosion and scaling. Indeed, the system’s cycles of concentration is affected by many factors, especially the quality of makeup water. The scope described in this paper refers to the recommended range of general industrial open cooling water systems (the quality of makeup water meets the industrial water standards). Then, models of water makeup rate vs. cycles of concentration can be written as:

− f W = f V ·N·(N − 1) 1 (17)

The blowdown rate and the leakage rate of the cooling water system are related to the water consumption of the system itself, and also to the blowdown of the system to the environment. By Equations (12) and (13), the relationship between leakage rate, blowdown rate and cycles of concentration is obtained as below:

− f P = f V ·(N − 1) 1 − f l (18)

In practice, some cooling water systems target “zero emissions” to achieve water saving and emission reduction. In this work, “zero emissions” refers to a system that does not actively drain outside, i.e., f P = 0. However, for many “zero blowdown” systems, the cycles of concentration are not in a reasonable range; that is, the water-saving effect is not obvious. Under the premise of f P = 0, according to Equation (15), it is easy to get the relationship between the cycles of concentration of the system and the leakage rate and evaporation rate:

f V N = 1 + (19) f l

In practical operation, the purification device can be set up to purify a part of circulating water and improve the water quality to reduce the system makeup rate. At this time, the purification device Water 2018, 10, 1592 6 of 16 is generally installed on the outlet line of the cooling tower. It is assumed that the impurity removal rate of the filter is δ. Then, the system’s impurity mass balance equation is:

W l P W f ·CW = f + f ·CR + δ·CR· 1 − f (20)

By combining Equation (17), Equation (20) becomes:

N· f V − δ f W = (21) N·(1 − δ) − 1

As mentioned above, f P = 0, thus the operation strategy under different impurity removal rates can be analyzed. Combining Equations (15) and (21), it is easy to get:

f l + f V N = (22) f l + δ·1 − f l − f V

The cooling tower is assumed to be able to completely eliminate the equipment cooling load, and therefore the outlet temperature of the cooling tower tR is equal to that of tn. At this point, there is no requirement for the temperature of the makeup water. In practice, circulating water may not be completely cooled by the cooling tower for some reason, such as cooling tower fouling or excessive outdoor air temperature, resulting in tR > tn. When this happens, the inlet water temperature of cooling network will continue to rise. Theoretically, to increase the tower efﬁciency is more realistic. However, it should be noted that in some industrial applications the cooling tower cannot cool the circulating water of the system for some reasons, e.g., heat exchanging effect is deteriorated, or outdoor air temperature is too high. Rather than improve the cooling effect of the cooling tower, instead, users employ fresh water to replace part of the circulating water directly to reduce the temperature of the circulating water. At this time, it is necessary to use a certain proportion of new water with a lower temperature to replace the circulating water, to keep the inlet water temperature of cooling network at the required level. Because of the consumption of new water, it is necessary to establish the relationship between the makeup rate and the temperature of makeup water and the circulating water. Combining Equations (12) and (14), it is easy to get:

t − t f W = R n (23) tR − tW

To describe the cooling capacity of the cooling tower, the cooling index k is defined in this work as k = tn . If k = 1, the cooling tower can completely take away the heat load of the equipment, i.e., tR tn = tR. On the contrary, if k < 1, the tower cooling capacity is insufficient, i.e., tR > tn. To evaluate the influence of water temperature on the heat balance of the system, according to the definition of cycles of concentration, this paper puts forward the concept “cycles of temperature”. The value is the ratio of the circulating water temperature to the makeup water temperature and is written as:

tR 0 tn k·tR Nt = , Nt = = = k·Nt (24) tW tW tW

0 where Nt is the cycles of temperature of the cooling tower outlet, and Nt is the cycles of temperature of the cooling network inlet. Combining Equations (23) and (24), the relationship of makeup rate and temperature can be written as:

tR − tn W tR − tn tW tW Nt·(1 − k) f = = t = (25) tR − tW R − 1 Nt − 1 tW Water 2018, 10, 1592 7 of 16

This model is used to evaluate the inﬂuence of water temperature on the makeup rate under the condition of insufﬁcient cooling capacity.

3. Model Veriﬁcation To verify the accuracy of the proposed model, experimental data were contrasted with the simulation results from the model. The experimental results were from the ﬁeld test of four open cooling water systems. The data from the test were also used for the input parameters of the model calculation to ensure the effectiveness of the contrast. Based on the correlation between these models, only the two following models are validated.

3.1. Veriﬁcation of the Makeup Rate and the Cycles of Concentration Model Four open circulating cooling water systems were tested. The experimental data included f V, W W W N and fexp. fexp and fcal are the measured and calculated values of makeup rate, respectively. Based on the experimental data, Equation (17) was used to calculate the theoretical makeup rate. Table1 shows the comparison between experimental and calculated results. The results show that the error between the calculated and experimental results is less than 2%.

Table 1. Comparison of experimental and calculated results.

V W W Case f N fexp fcal Error (%) 1 0.0121 1.2512 0.0592 0.0603 −1.81 2 0.0133 2.2034 0.0247 0.0244 1.41 3 0.0125 2.6169 0.0206 0.0202 1.79 4 0.0164 1.8233 0.0362 0.0363 −0.33

3.2. Veriﬁcation of the Makeup Rate and the Temperature Model

Four open circulating cooling water systems were tested. tR was higher than tn. The experimental W data included tR, tn, tW and fexp. Based on the experimental data, k and Nt were calculated, and Equation (25) was used to calculate the theoretical makeup rate. Table2 shows the makeup rate comparison between experimental and calculated results. The results show that the error between the calculated and the experimental results is less than 2%.

Table 2. Comparison of experimental and calculated results.

W W Case tR tn tW k Nt fexp fcal Error (%) 1 31.0297 30.0586 20.2632 0.9687 1.5313 0.092 0.0902 –1.96 2 37.3506 35.1201 20.358 0.9479 2.0182 0.1052 0.1033 –1.83 3 43.5605 40.2203 22.334 0.945 2.3214 0.0956 0.0966 1.04 4 41.9361 40.7968 29.2768 0.9728 1.4324 0.0885 0.09 1.67

4. Results and Discussion Applying the proposed models, this section analyzes the effect of system water quality and water temperature parameters on water consumption and blowdown indicators. It is obvious that the evaporation rate of the CT is a key parameter for the established models. According to Zubair et al. [24], the evaporation rate is mainly affected by outdoor air parameters (dry bulb temperature and wet bulb temperature), inlet and outlet temperatures of cooling water, and the air-to-water ratio. In [25], 22 sets of measured evaporation rates of cooling towers are analyzed, and the values varied from 0.004 to 0.024. In the following discussions, evaporation rates are chosen from this range. Water 2018, 10, 1592 8 of 16

4.1. Relationships between the Makeup Rate and the Cycles of Concentration According to Equation (17), the relationship between the makeup rate and the cycles of concentration can be analyzed. Figure2 shows this relationship when the evaporation rate takes differentWater values. 2018, 10, x FOR PEER REVIEW 8 of 16

Water 2018, 10, x FOR PEER0.10 REVIEW 8 of 16

V 0.10 f = 0.01 V 0.08 fV = 0.015 f = 0.01 V f V= 0.02 0.08 f = 0.015 V

0.06 f = 0.02

W f 0.06

0.04

W f 0.04 0.02

0.02 0.00 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 0.00 N 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 . W W FigureFigure 2. 2The The relationship relationshipN between f f andand N. N. . W AccordingAccording to Figure to Figure2, lower 2, lowerFigure cycles cycles 2 T ofhe ofrelationship concentration concentration between lead lead f andto to higher N higher. makeup makeup rates rates.. This trend This trend becomes more apparent when the cycles of concentration is less than 2.0. It can be seen that increasing becomes moreAccording apparent to Figure when 2, the lower cycles cycles of concentrationof concentration is lead less to than higher 2.0. makeup It can berate seens. This that trend increasing the cycles of concentration is one of the important methods to reduce water consumption. In addition, the cyclesbecomes of concentration more apparent is when one ofthe the cycles important of concentration methods is less to than reduce 2.0. It water can be consumption. seen that increasing In addition, the makeup rate increases with the increasing of the evaporation rate. In other words, if the cooling the cycles of concentration is one of the important methods to reduce water consumption. In addition, the makeupload is rate constant, increases the freshwater with the consumption increasing ofwill the increase evaporation with the rate.increasing In other ratio words,of evaporation if the cooling the makeup rate increases with the increasing of the evaporation rate. In other words, if the cooling load is constant,heat transfer. the It freshwater can be seen consumption that if the cooling will tower increase is combined with the with increasing other equipment ratio of to evaporation cool the heat load is constant, the freshwater consumption will increase with the increasing ratio of evaporation transfer.cooling It can water be seen to reduce that if the the evaporation cooling tower of water, is combined the makeup with rate othercan be equipmentreduced. For toexample, cool the in cooling heat transfer. It can be seen that if the cooling tower is combined with other equipment to cool the water to[16], reduce a low the temperature evaporation seawater of water, heat exchanger the makeup is set rate at the can outlet be reduced. of the cooling For example, tower to reduce in [16 ], a low cooling water to reduce the evaporation of water, the makeup rate can be reduced. For example, in evaporation. temperature[16], a seawaterlow temperature heat exchanger seawater heat is setexchanger at the outletis set at of the the outlet cooling of the tower cooling to tower reduce to evaporation.reduce evaporation.4.2. Blowdown Rate and Leakage Control of the System 4.2. Blowdown Rate and Leakage Control of the System 4.2. BThelowdown water Rate loss and of Leakage the system Control includesof the System evaporation, sewage blowdown and leakage. The Theinfluence waterloss of water of the leakage system on includesmakeup rate evaporation, and blowdown sewage rate blowdownis often overlooked. and leakage. However, The the inﬂuence The water loss of the system includes evaporation, sewage blowdown and leakage. The of watereffect leakage of leakage on makeuprate on the rate system and is very blowdown important. rate According is often to overlooked.Equation (18), the However, blowdown the rate effect of influence of water leakage on makeup rate and blowdown rate is often overlooked. However, the leakageof rate the system on the is systemanalyzed is when very the important. leakage rate is According fixed. Figure to 3 Equationshows the relationshi (18), thep blowdown between the rate of effect of leakage rate on the system is very important. According to Equation (18), the blowdown rate blowdown rate and the cycles of concentration at the leakage rate of 0.003. the systemof the is system analyzed is analyzed when when the leakagethe leakage rate rate is is ﬁxed. fixed. Figure Figure 33 shows shows the the relationshi relationshipp between between the the blowdown rate and the cycles of concentration at the leakage rate of 0.003. blowdown rate and the0.08 cycles of concentration at the leakage rate of 0.003.

V 0.08 f = 0.01 V fV = 0.015 f = 0.01 0.06 V f V = 0.02 f = 0.015 0.06 V

f = 0.02

f 0.04

0.02

0.00 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 0.00 N 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 P 푙 Figure 3. The relationship betweeN n f and N (푓 = 0.003). FigureFigure 3. 3The. The relationship relationship betwee betweenn fP fandP and N (푓N푙 (=f l 0=.0030.003).).

Water 2018, 10, 1592 9 of 16 Water 2018, 10, x FOR PEER REVIEW 9 of 16

AsAs shown shown in in Figure Figure 3,3, if the leakage leakage rate of the the system system remains remains the same, with the increase increase of the cyclescycles of of concentration, concentration, the the blowdown blowdown rate rate gradually gradually decline decliness until until stopped. stopped. On On the the contrary, contrary, with with thethe decrease decrease of of cycles cycles of of concentration, concentration, the the blowdown blowdown rate rate of of the the system system in increasescreases rapidly. rapidly. When When the the valuevalue (c (cyclesycles of of concentration concentration)) is is less less than than 2.0 2.0,, this this trend trend becomes becomes more more evident evident.. AA key key factor factor in in the design and operationoperation ofof openopen circulatingcirculating cooling cooling water water system system is is the the cycles cycles of ofconcentration. concentration. As As mentioned mentioned previously, previously, for for the the openopen coolingcooling waterwater system, it is is more more appropriate appropriate toto control control the the cycles cycles of of concentration concentration within within the the range range of of 3 3–5.–5. According According to to Equation Equation (19 (19),), there there is is a a certaincertain relationship relationship between between leakage leakage and and cycles cycles of of concentration, concentration, which which often often determin determineses the the optimal optimal operatingoperating strategy strategy of of the the system. system. Figure Figure 4 is the optimal operation strategy under different leakage conditionsconditions when when the the evaporation evaporation rate rate is is 0.015. 0.015.

푃 FigureFigure 4. 4. TheThe relationship relationship betwee betweenn NN andand flf l(푓( f P == 00).).

AsAs shown shown in in Figure Figure 4,4 the, the cycle cycless of concentration of concentration decrease decrease with with the increase the increase of leakage of leakage rate under rate theunder strategy the strategy that the that system the system maintains maintains “zero “zero emissions”. emissions”. As As mentioned mentioned earlier, earlier, the the cycles cycles of of concentrationconcentration of of the the system system should be controlledcontrolled withinwithin thethe rangerange of of 3–5 3–5 to to save save freshwater freshwate asr as well well as asensure ensure the the safety safety of of heat heat exchange exchange equipment. equipment. TheThe curvecurve was divided into into three three parts parts,, left, left, middle, middle, andand right right,, by by the the po pointsints of of NN == 3 3 and and NN == 5 5 on on the the curve curve.. In In the the left left area, area, because because of of the the low low leakage leakage rate,rate, if if the the system system maintains maintains “ “zerozero emissions emissions”,”, the the cycles cycles of of concentration concentration will will be be too too high, high, and and then then causecause adverse adverse effects on thethe heatheat exchanger.exchanger. Thus, Thus this, this region region belongs belongs to to “active “active emission emission area”. area” In.this In thisregion, region, the the system system must must adopt adopt the strategythe strategy of “active of “active emission” emission” instead instead of “zero of “zero emissions” emission tos” stop to stopthe cycles the cycles of concentration of concentration from being from too being high. too The high. intermediate The intermediate region is truly region “zero is truly emissions”. “zero emissionsWhen the” system. When isthe in system the region, is in thethe cyclesregion, of the concentration cycles of concentr can stillation be controlled can still be within controlled a reasonable within arange reasonable even without range even active without blowdown. active Theblowdown. leakage rateThe onleakage the right rate side on the is too right large, side which is too leads large, to whicha low cycles leads ofto concentration, a low cycles of even concentration if the system, even achieves if the a “zero system emissions”. achieves a Therefore, “zero emission in thes left”. Therefore,area, measures in the should left area be, measures taken to control should the be leakagetaken to ofcontrol the system, the leakage and this of the area system, is named and as this the area“leakage is named control as area”.the “leakage To sum control up, the leakagearea”. To of sum an open up, circulatingthe leakage cooling of an open water circulating system needs cooling to be waterevaluated, system and needs then the to optimalbe evaluated operation, and strategy then the can optimal be obtained operation according strategy to the can leakage be obtained rate. according to the leakage rate. 4.3. The Impact of Water Puriﬁcation Equipment 4.3. TheAccording Impact of toWater Equation Purification (21), the Equipment relationship between the makeup rate and N, δ can be analyzed in FigureAccording5. to Equation (21), the relationship between the makeup rate and 푁, 훿 can be analyzed in Figure 5.

Water 2018, 10, 1592 10 of 16 WaterWater 20182018,, 1010,, xx FORFOR PEERPEER REVIEWREVIEW 1010 ofof 1616

0.0500.050 NN=1.5=1.5 0.045 0.045 NN=2.0=2.0 NN=2.5=2.5 0.0400.040 NN=3.0=3.0

0.0350.035

W f 0.0300.030

0.0250.025

0.0200.020

0.0000.000 0.0020.002 0.0040.004 0.0060.006 0.0080.008 0.0100.010 0.0120.012 δ δ W FigureFigure 5 5.5.. TTheThehe relationship relationship between between ffW andand δδ...

AsAs shown shown in in Figure Figure 5 5,5,, whenwhenwhen thethethe cyclescyclescycles ofofof concentrationconcentrationconcentration remainsremainremainss constant,constant,constant, withwithwith thethethe increaseincreaseincrease ofofof impurityimpurity removalremoval removal rate,rate, rate, thethe the waterwater water makeupmakeup makeup raterate rate willwill will decrease.decrease. decrease. ThatThat That isis to isto tosay,say, say, thethe the properproper proper purificationpurification puriﬁcation ofof circulatingofcirculating circulating waterwater water cancan can achieveachieve achieve thethe the purposepurpose purpose ofof of waterwater water savingsaving saving andand and emissionemission emission reduction.reduction. AsAs mentioned mentioned 푃 P above,above, toto analyzeanalyze analyze thethe the optimaloptimal optimal waterwater water useuse use strategystrategy strategy ofof of thethe the system,system, system, itit hashas it has 푓푓푃 f == = 00 and0andand EqEq Equationuationuation ((1919 (19))) isis selectedisselected selected... Then,Then, Then, the the the relationship relationship relationship between between between the the the cycles cycles cycles of of of concentration concentration concentration and and and the the the leakage leakage leakage rate rate under under differentdifferent impurity impurity removal removal rates rates isis shownshown inin FiguresFigures6 66 and andand7 .7.7.

3535

δδ=0=0 3030 δδ=0.001=0.001 δ=0.002 2525 δ=0.002 δδ=0.003=0.003

2020

N 1515

1010

00 0.0000.000 0.0020.002 0.0040.004 0.0060.006 0.0080.008 l f l f . ll FigureFigure 66 6.. TheTThehe relationshiprelationship betweenbetweebetweenn NN andand fffl..

Water 20182018,, 1010,, x 1592 FOR PEER REVIEW 1111 of of 16 16 Water 2018, 10, x FOR PEER REVIEW 11 of 16

0.010 0.010 leakage control 0 emissions active emission leakage control 0 emissions active emission

0.008 0.008

0.006

f 0.004 0.004

0.002 0.002

0.000 0.000 0.000 0.001 0.002 0.003 0.000 0.001 δ 0.002 0.003 δ Figure 7. Optimal operating strategy for systems with different impurity removal rates. FigureFigure 7 7.. OptimalOptimal operating operating strategy strategy for for systems systems with with different different impurity removal rates rates.. Figure 6 shows that, under the condition of invariable leakage rate, increasing the impurity FigureFigure 6 6 shows shows that, that, underunder the the condition condition of of invariable invariable leakage leakage rate, rate, increasing increasing the the impurity impurity removal rate will reduce the cycles of concentration of the system, which means the risk of fouling removalremoval rate rate will will reduce reduce the the cycles cycles of of concentration concentration of of the the system, system, which which means means the the risk risk of of fouling fouling and corrosion of the heat exchanger will decline. This trend will be more pronounced in areas with andand corrosion corrosion of of the the heat heat exchanger exchanger will will decline. decline. This This trend trend will will be be more more pronounce pronouncedd in in areas areas with with lower leakage rates. The optimal operation strategy for different leakage rates is shown in Figure 7. lowerlower leakage leakage rates. rates. The The optimal optimal operation operation strategy strategy for for different different leakage leakage rates rates is is shown shown in in F Figureigure 7.7. Under the condition of low impurity removal rate, it makes little sense to the system for maintaining UnderUnder the the condition condition of of low low impurity impurity removal removal rate, rate, it it makes makes little little sense sense to to the the system system for for maintaining maintaining a lower blowdown rate; on the contrary, to control the cycles of concentration in this interval, the aa lower lower blowdown blowdown rate; rate; on on the the contra contrary,ry, to to control control the the cycles cycles of of concentration concentration in in this this interval, interval, the the system even needs to blowdown a part of the water actively. In the case of high impurity removal systemsystem even even needs needs to to blowdown blowdown a a part part of of the the water water actively. actively. In In the the case case of of high high impurity impurity removal removal rate, it is important to control the leakage rate to control the cycles of concentration and consume less rate,rate, it it is is important important to to control control the the leakage leakage rate rate to to control control the the cycles cycles of of concentration concentration and and c consumeonsume less less freshfresh water. water. fresh water. 4.4.4.4. Influence Influence of of Water Water Temperature Temperature 4.4. Influence of Water Temperature AsAs mentioned mentioned previously previously,, when when the the cooling cooling capacity capacity for for the the cooling cooling tower tower is is insufficient, insufficient, As mentioned previously, when the cooling capacity for the cooling tower is insufficient, EqEquationuation (25 (25)) can can be be applied applied to toanalyze analyze the theinfluence influence of water of water temperature temperature on the on makeup the makeup rate of ratethe Equation (25) can be applied to analyze the influence of water temperature on the makeup rate of the system.of the system. Hence, Hence, the relation the relation between between the cooling the cooling index index (푘), ( kthe), the cycles cycles of of temperature temperature (푁 (N푡)t )and and the the system. Hence, the relation between the cooling index (푘), the cycles of temperature (푁 ) and the systemsystem makeup makeup rate rate ( (푓f 푊W)) is is shown shown in in Figure Figuress 88 andand9 9.. 푡 system makeup rate (푓푊) is shown in Figures 8 and 9.

0.6 0.6 k=0.99 k=0.99 0.5 k=0.98 0.5 k=0.98 k=0.97 k=0.97 0.4 k=0.96 0.4 k=0.96 k=0.95 k=0.95

0.3

W f

0.3

W f

0.2 0.2

0.1 0.1

0.0 0.01.0 1.2 1.4 1.6 1.8 2.0 1.0 1.2 1.4 N 1.6 1.8 2.0 Nt t W Figure 8 8.. TheThe relationshiprelationship betweenbetween ffW and Nt.. Figure 8. The relationship between fW and Nt.

Water 2018, 10, 1592 12 of 16 Water 2018, 10, x FOR PEER REVIEW 12 of 16

0.10 N = 2.0 t

0.08

0.06

W f

0.04

0.02

0.00 0.95 0.96 0.97 0.98 0.99 1.00 1.01 k W Figure 9.9. TheThe relationshiprelationship betweenbetweenf fW and kk..

As shown in Figure 8 8,, whenwhen thethe coolingcooling capacitycapacity ofof thethe systemsystem isis insufficientinsufficient ((푘k << 1), the makeup rate will increase with the decrease of the cycles of temperature (Nt), and the change rate will increase rate will increase with the decrease of the cycles of temperature (푁푡), and the change rate will increase significantly when the Nt is lower than 1.4. It shows that reducing the makeup water temperature significantly when the 푁푡 is lower than 1.4. It shows that reducing the makeup water temperature will help to reduce fresh water consumption, but,but, when the temperature decreases to a certain value, it makes little sense to continue to decline. As shown in Figure9 9,, thethe makeupmakeup raterate increasesincreases with with thethe decrease ofof k푘.. To To ensure ensure that that the the inlet inlet temperature temperature of of the the cooling cooling network network remains remains constant, constant, 1% of1% the of reduction of cooling index will enhance 2% of the makeup rate (under the condition of Nt = 2.0) if the reduction of cooling index will enhance 2% of the makeup rate (under the condition of 푁푡 = 2.0) thereif there are are no no other other assistant assistant methods. method Thiss. This shows shows that that the coolingthe cooling capacity capacity of the ofcooling the cooling tower tower is very is importantvery important for reducing for reducing the consumption the consumption of fresh of water fresh whenwater makeup when makeup is chosen is tochosen maintain to maintain a desired a temperaturedesired temp inerature the recirculation in the recirculation line. line. 4.5. Coupling Effect of Quality and Temperature on the Makeup Rate 4.5. Coupling Effect of Quality and Temperature on the Makeup Rate For a certain process, the effects of quality and temperature on the system do coexist, such as For a certain process, the effects of quality and temperature on the system do coexist, such as their impact on the makeup rate. In practice, when the cooling capacity of the cooling tower is their impact on the makeup rate. In practice, when the cooling capacity of the cooling tower is insufficient or the makeup water temperature is over-rising, the open circulating cooling water system insufficient or the makeup water temperature is over-rising, the open circulating cooling water is faced with a relatively high rate of makeup water and drainage, then causing an excessive low system is faced with a relatively high rate of makeup water and drainage, then causing an excessive cycles of concentration, and thus, the water resources are wasted. That is, whether the system has low cycles of concentration, and thus, the water resources are wasted. That is, whether the system the appropriate cycles of concentration is not only a question of water quality, but also involves the has the appropriate cycles of concentration is not only a question of water quality, but also involves impact of the temperature and the cooling capacity of the cooling tower. According to Equation (17), the impact of the temperature and the cooling capacity of the cooling tower. According to Equation the corresponding makeup water rate of N = 3 is calculated. Then, this makeup water rate is taken as (17), the corresponding makeup water rate of 푁 = 3 is calculated. Then, this makeup water rate is input, and the cycles of temperature values under different cooling tower conditions (k) are calculated taken as input, and the cycles of temperature values under different cooling tower conditions (푘) are according to Equation (25), as shown in Table3. calculated according to Equation (25), as shown in Table 3.

Water 2018, 10, 1592 13 of 16

Table 3. The Nt of the system in the case of N = 3.

fV 0.01 0.012 0.014 0.016 0.018 0.02 0.022 0.024 0.026 Nt (k= 0.990) 3.00 2.25 1.91 1.71 1.59 1.50 1.43 1.38 1.34 Nt (k = 0.988) 5.00 3.00 2.33 2.00 1.80 1.67 1.57 1.50 1.44 Nt (k = 0.986) 15.00 4.50 3.00 2.40 2.08 1.88 1.74 1.64 1.56 Nt (k = 0.984) - 9.00 4.20 3.00 2.45 2.14 1.94 1.80 1.70 Nt (k = 0.982) - - 7.00 4.00 3.00 2.50 2.20 2.00 1.86 Nt (k = 0.980) - - 21.00 6.00 3.86 3.00 2.54 2.25 2.05 Nt (k = 0.978) - - - 12.00 5.40 3.75 3.00 2.57 2.29 Nt (k = 0.976) - - - - 9.00 5.00 3.67 3.00 2.60 Nt (k = 0.974) - - - - 27.00 7.50 4.71 3.60 3.00 Nt (k = 0.972) - - - - - 15.00 6.60 4.50 3.55 Nt (k = 0.970) ------11.00 6.00 4.33

The ﬁrst column in Table3 is the cooling index of the cooling tower ( k). The values gradually decrease from top to bottom, which indicates that the cooling capacity of the cooling tower gradually decreases. The ﬁrst row of Table3 is the evaporation rate of the system ( f V), gradually increasing from left to right, indicating that the proportion of evaporating heat transfer in the total heat exchange V gradually increases. Under certain conditions of k and f , when the actual Nt is greater than or equal to the values in Table3, the makeup rate calculated by Equation (17) is greater than or equal to the makeup rate calculated by Equation (25). The actual makeup rate takes the larger value of the two, so the cycles of concentration of the system will be equal to 3.0. Conversely, when the actual Nt is less than the values in Table3, the makeup rate will rise because of the heat emission, and the cycles of concentration of the system will be less than 3.0. As an example, if the actual value of Nt is 2.0, then, according to Table3, it is possible to achieve a cycles of concentration of the system of 3.0 in the colored region; however, the remaining part of the region cannot achieve its goal (N > 3). V In addition, increasing the k and f values can reduce the system requirement for Nt. This shows that, since the water quality and the temperature simultaneously impact the system makeup rate, these two parameters must be considered to control the makeup rate. Under the same evaporation rate of the system, keeping cooling capacity of the cooling tower (increase in the k value) is crucial for water-saving and drainage reduction. In Table3, under the f V = 0.016 condition, if the k value increases from 0.986 to 0.988, the water system requirement for Nt decreases from 2.4 to 2.0. This shows that, as the cooling capacity of the cooling tower increases, less waste heat needs to be removed by means of water replenishment, and then a very low makeup temperature is unnecessary. Similarly, when V the k value remains constant, if the f value increases, the Nt value of the system can be increased. In Table3, under the k = 0.988 condition, if the f V value increases from 0.014 to 0.016, then the water system requirement for Nt decreases from 2.33 to 2.0. This shows that, with the increase of evaporation rate, the enrichment of impurities has become the dominant factor in water replenishment. At this point, it is no longer important whether the makeup water temperature is low enough.

5. Conclusions This work uses the equations of mass conservation and energy conservation to analyze the open cooling water systems. The mathematical models of the relationship among water consumption, blowdown rate, water quality and water temperature parameters have been established, which can be used to analyze the change of water supply and blowdown of open cooling water systems under various water quality and water temperature parameters. A key indicator in water-saving and drainage reduction is the cycles of concentration. The results calculated by the models show that the makeup rate and the blowdown rate of the system decline with the increase of the cycles of concentration. This trend becomes more apparent when the cycles of concentration is less than 2.0. That is to say, for an open cooling water system, the cycles Water 2018, 10, 1592 14 of 16 of concentration should be at least 2.0. In a practical system, the appropriate cycles of concentration should be between 3 and 5. To keep the cycles of concentration in a reasonable range, the leakage rate of the system is assessed. Then, the operation strategies of “active discharge”, “zero emissions” and “leakage rate control” in the range of different cycles of concentration is proposed. After removing some of the impurities in the circulating water by filtration, the concentration of the system water can be slowed down and the resulting makeup rate can be reduced. Such a measure is even more important for the systems with lower leakage rates. The concepts of cooling index of cooling tower and cycles of temperature in water system are put forward. By applying these concepts and related models, the influence of water temperature on system makeup rate can be evaluated under the condition of insufficient cooling capacity of cooling tower. The effects of quality and temperature on system makeup rate coexist. For a certain system, to maintain the cycles of concentration not less than 3.0, it is necessary to ensure that the cycles of temperature of the system is not less than a corresponding value. The corresponding relation of this value varies with the change of the evaporation rate and the cooling index.

Author Contributions: Z.L.: literature search, data analysis, writing, revision, and final approval; J.C.: study design; W.S.: figures, and revision; L.W.: data collection. Funding: This work was supported by the National Natural Science Foundation of China (51704069 and 51734004), the China Scholarship Council—International Clean Energy Talent Program (CSC-iCET-201802180028) and the Fundamental Research Funds for the China Central Universities (N162504011). Conflicts of Interest: The authors declare no conflicts of interest.

Notation c heat capacity of water, kJ/(kg·◦C) CE ratio of evaporation heat transfer and total heat transfer, - Ci concentration of inlet water of cooling tower, kg/t CR concentration of outlet water of cooling tower, kg/t CW concentration of makeup water, kg/t CT cooling tower CW cooling water f l leakage rate, - f P blowdown rate, - f V evaporation rate, - f W makeup rate, - Fi inlet water flowrate of cooling tower, t/h Fl flowrate of Leakage water, t/h FP flowrate of blowdown water, t/h FR outlet water flowrate of cooling tower, t/h FW flowrate of makeup water, t/h W fcal calculated value of makeup rate, - W fexp measured value of makeup rate, - FV flowrate of evaporated water, t/h k cooling index of cooling tower, - N cycles of concentration, - Nt cycles of temperature of the cooling tower outlet, - 0 Nt cycles of temperature of the cooling network inlet, - Qi waste heat in the cooling network, kJ QR release heat from the cooling tower to the environment, kJ QV latent heat of water evaporation, kJ r latent heat of vaporization of water, kJ/kg ◦ tn cooling water temperature flowing into the cooling water network, C ◦ ti inlet water temperature of cooling tower, C Water 2018, 10, 1592 15 of 16

◦ tR outlet water temperature of cooling tower, C ◦ ∆tR cooling water temperature range of the cooling tower, C ◦ tW temperature of makeup water, C δ impurity removal rate of the ﬁlter, -

References

1. Liu, W.; Chien, S.H.; Dzombak, D.A.; Vidic, R.D. Scaling Control for Heat Exchangers in Recirculating Cooling Systems Using Treated Municipal Wastewater. Ind. Eng. Chem. Res. 2014, 53, 16366–16373. [CrossRef] 2. Sun, W.; Yue, X.; Wang, Y.; Cai, J. Energy and exergy recovery from exhaust hot water using ORC (organic Rankine cycle) and a retrofitted configuration. J. Cent. South Univ. 2018, 25, 1464–1474. [CrossRef] 3. Colla, V.; Matino, I.; Branca, T.A.; Fornai, B.; Romaniello, L.; Rosito, F. Efficient use of water resources in the steel industry. Water 2017, 9, 874. [CrossRef] 4. Kremes, J.J. Industrial water recycle/reuse. Curr. Opin. Chem. Eng. 2012, 1, 238–245. 5. Linnhoff, B.; Vredeveld, R. Pinch technology has come of age. Chem. Eng. Prog. 1984, 80, 33–40. 6. Wang, Y.; Smith, R. Wastewater minimization. Chem. Eng. Sci. 1994, 49, 981–1006. [CrossRef] 7. Dunn, R.F.; El-Halwagi, M. Process integration technology review: Background and applications in the chemical process industry. Chem. Technol. Biotechnol. 2003, 78, 1011–1021. [CrossRef] 8. Jacob, J.; Viviant, C.; Houle, J.F.; Paris, J. Analyses et optimization des réseaux d’eau dans les procédés de fabrication des pâtes et papiers. Tech. Pincement À L’oeuvre Pulp. Pap. Can. 2002, 103, 24–27. 9. Manan, Z.A.; Wan Alwi, S.R.; Ujang, Z. Water pinch analysis for an urban system: A case study on the Sultan Ismail Mosque at the Universiti Teknologi Malaysia (utm). Desalination 2006, 194, 52–68. [CrossRef] 10. Wang, T.; Fang, G.; Xie, X.; Liu, Y.; Ma, Z. A multi-dimensional equilibrium allocation model of water resources based on a groundwater multiple loop iteration technique. Water 2017, 9, 718. [CrossRef] 11. Feng, X.; Bai, J.; Wang, H.; Zheng, X. Grass-roots design of regeneration recycling water networks. Comput. Chem. Eng. 2008, 32, 1892–1907. [CrossRef] 12. Sun, W.; Wang, Y.; Zhang, F.; Zhao, Y. Dynamic allocation of surplus byproduct gas in steel plant by dynamic programming with reduced state space algorithm. Eng. Opt. 2018, 50, 1578–1592. [CrossRef] 13. Bagajewicz, M. A review of recent design procedures for water networks in refineries and process plants. Comput. Chem. Eng. 2000, 24, 2093–2113. [CrossRef] 14. Ramos, M.A.; Boix, M.; Montastruc, L.; Domenech, S. Multiobjective optimization using goal programming for industrial water network design. Ind. Eng. Chem. Res. 2014, 53, 17722–17735. [CrossRef] 15. Castro, P.; Matos, P.; Fernandes, M.C.; Nunes, C.P. Improvements for mass-exchange networks design. Chem. Eng. Sci. 1999, 54, 1649–1665. [CrossRef] 16. Kim, J.K.; Smith, R. Cooling water system design. Chem. Eng. Sci. 2001, 56, 3641–3658. [CrossRef] 17. Kim, J.K.; Savulescu, L.; Smith, R. Design of cooling systems for effluent temperature reduction. Chem. Eng. Sci. 2001, 56, 1811–1830. [CrossRef] 18. Jiang, W.; Yuan, Z.; Bi, J.; Sun, L. Conserving water by optimizing production schedules in the dyeing industry. J. Clean. Prod. 2010, 18, 1696–1702. [CrossRef] 19. Ponce-Ortega, J.M.; Serna-González, M.; Jiménez-Gutiérrez, A. Optimization model for re-circulating cooling water systems. Comput. Chem. Eng. 2010, 34, 177–195. [CrossRef] 20. Sun, J.; Feng, X.; Wang, Y. Cooling-water system optimisation with a novel two-step sequential. Appl. Therm. Eng. 2015, 89, 1006–1013. [CrossRef] 21. Bu, Q. Substance Flow Analysis and Its Application in Steel Industry; Northeastern University: Shenyang, China, 2005. 22. Qureshi, B.A.; Zubair, S.M. A comprehensive design and rating study of evaporative coolers and condensers. Part I. Performance evaluation. Int. J. Refrig. 2006, 29, 645–658. [CrossRef] 23. Rahmani, K. Reducing water consumption by increasing the cycles of concentration and Considerations of corrosion and scaling in a cooling system. Appl. Therm. Eng. 2017, 114, 849–856. [CrossRef] Water 2018, 10, 1592 16 of 16

24. Zubair, S.M. Prediction of evaporation losses in wet cooling towers. Heat Transf. Eng. 2006, 27, 86–92. 25. GOsi,˝ P. Method and chart for the determination of evaporation loss of wet cooling towers. Heat Transf. Eng. 1989, 10, 44–49. [CrossRef]

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).