Energy conservation improvement and ON–OFF switch times reduction for an existing VFD-fan-based cooling tower

(1)

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/277338416

Energy conservation improvement and ON–OFF switch times reduction for an existing VFD-fan-based cooling tower

Article in Applied Energy · September 2015

DOI: 10.1016/j.apenergy.2015.05.025

CITATIONS

25

READS

574 5 authors, including:

Some of the authors of this publication are also working on these related projects:

Methanol to AromaticsView project Chun-Cheng Chang

National Tsing Hua University 11PUBLICATIONS 139CITATIONS

SEE PROFILE

Shyan-Shu Shieh

Chang Jung Christian University 36PUBLICATIONS 743CITATIONS

SEE PROFILE

Shi-Shang Jang

National Tsing Hua University 102PUBLICATIONS 1,563CITATIONS

SEE PROFILE

Chan-Wei Wu CSC Solar Corporation 13PUBLICATIONS 510CITATIONS

SEE PROFILE

All content following this page was uploaded by Shyan-Shu Shieh on 31 March 2020.

The user has requested enhancement of the downloaded file.

(2)

Energy conservation improvement and ON–OFF switch times reduction for an existing VFD-fan-based cooling tower

Chun-Cheng Chang

^a

, Shyan-Shu Shieh

^b,^⇑

, Shi-Shang Jang

^a,^⇑

, Chan-Wei Wu

^c

, Ying Tsou

^c

aDepartment of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan

bDepartment of Occupational Safety and Health, Chang Jung Christian University, Tainan 71101, Taiwan

cChina Steel Corporation, Kaohsiung 81233, Taiwan

h i g h l i g h t s

Achieve energy saving and reduce the ON/OFF switching frequency at the same time.

Propose a PI feedback controller with a temperature zone setting.

Implement hybrid operations of rule-based and equation-based feedback control.

Introduce larger approach for the setting of the outlet cooling water temperature.

a r t i c l e i n f o

Article history:

Received 19 December 2014 Received in revised form 3 May 2015 Accepted 9 May 2015

Keywords:

Cooling tower Energy conservation Variable-frequency drive Zone control

Approach

a b s t r a c t

The increasing economic advantage of replacing traditional two-speed fans with variable-frequency drive (VFD) fans has been gaining popularity in the industry. However, concerns regarding frequent ON/OFF switching and the lack of a well-devised controller have discouraged widespread adoption. In this study, a temperature zone method is proposed to replace the set-point method of fan control. Additionally, the highest output water temperature allowed in the process is set as the upper limit of a zone in order to further conserve energy. Both strategies are comprehensively analyzed for a virtual cooling tower that uses operational data from an existing VFD-fan-based cooling tower system in Taiwan. The results show energy savings of 38% for a 0.75°C zone without increasing the ON/OFF switching frequency. The proposed strategies were further verified via an on-line field experiment. The proposed methods can be uni- versally and easily applied to any existing cooling tower, and have significant implications for energy conservation if adopted globally.

1. Introduction

Cooling towers are a common feature of industrial plants, especially in energy-intensive sectors such as the iron, steel, or petro- chemical industries. A cooling tower dissipates waste heat to the atmosphere through a combination of heat- and mass-transfer pro- cesses. When considering an individual plant, the potential energy savings for a cooling tower may not be as great as those of powered devices, such as compressors, or energy-intensive components such as boilers or distillation columns. In addition to industrial applications, cooling towers are also widely used in heating, venti- lating and air-conditioning systems. Overall, any improvement in

cooling tower operations would provide signiﬁcant opportunity for energy conservation.

Variable-frequency drive (VFD) devices have been available for more than four decades, but were not applied to cooling tower fans until their prices fell sharply over the past decade. Recently, two-speed fans have been gradually replaced with VFD-fans.

Practical concerns remain, of how to avoid frequent START/STOP fan operation, which can cause sudden increases in stress due to the large inertia moment. This drawback has limited cooling systems based on VFD-fans from fully exploiting their energy-saving potential, which is more compelling and signiﬁcant in large-scale, multi-cell systems used in energy-intensive industries.

This study proposes two operational strategies to take advantage of energy-saving potential represented by VFD-fan-based cooling tower systems to the greatest possibility.

Many studies on cooling towers have focused on design[1–6]

and performance parameters [7–9]. Comparatively few studies

⇑ Corresponding authors.

E-mail addresses:[email protected] (S.-S. Shieh), [email protected] (S.-S. Jang).

Contents lists available atScienceDirect

Applied Energy

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a p e n e r g y

(3)

have addressed the optimal operation of cooling towers. Castro et al.[10]simulated the operating cost of a water-cooling system considering water make-up, energy consumption, and climatic effects. Cortinovis et al.[11]minimized operating costs by optimiz- ing the fan speed, rate of water removal, and the positions of valves at heat exchanger branches. They also suggested that economical operation would require the water outlet temperature to remain as high as possible. The ﬁndings of these studies were based on single-cell tower operations utilizing two-speed fan mode. Wang et al.[12]used statistical modeling to predict outlet water temperature and developed a discrete model-based strategy to determine optimal fan-operating mode in a multi-cell cooling tower with two-speed fans. In that work, the authors simulated a virtual plant to verify their proposed method.

Other studies addressed the economic gains of applying VFD to cooling tower fans. Cohen[13]indicated that variable-speed fans can significantly reduce the energy requirements of cooling tower systems by precisely assigning air flow rates to the required heat dissipation. Also in his theoretical viewpoint, in a multi-fan VFD system, i.e., multi-cell, the fan speed must be synchronized, because a marginal increase in fan speed increases the power consumption, whereas synchronized fan speed put more cells into service and maximize the total heat transfer surface while minimizing the power consumption. Muntean et al.[14,15]implemented temperature control for VFD-fan operation in a cooling tower and found that energy savings and control of out-flow water temperature are the two major advantages. They also claimed that using VFD technology to track wet-bulb temperature throughout the year resulted in annual energy savings of 83% compared to running the fans continuously at full speed. These studies addressed the advantages of VFD installations and the configurations of the simulation examples. However, as pointing out in this work, synchro- nizing fan speeds across the cells during low-speed stages is not practical because it may cause frequent ON–OFF switching among fans. It is the very reason that we propose temperature zone control, and hybrid operation of rule-based and equation-based feedback control in this study.

Yu and Chan [16] used a load-based method to control the speed of cooling tower fans and condenser water pumps in order to improve the energy efﬁciency of a cooling tower system, and highlighted the need to widen the use of such control systems.

Aha and Mitchell[17]proposed optimal supervisory control strategies for the set points (exact values) of controlled variables such as supply air, chilled water, and condenser water temperatures. The fan speed was controlled via a proportional-integral-derivative (PID) feedback controller such that the water temperature reached the set point designated by the optimal supervisory control strategy. Those studies considered the integration of cooling towers and chiller systems. However, previous studies have not addressed the frequency of ON/OFF switching in VFD fans.

In traditional cooling towers, two-speed fans, i.e., OFF, low, or high speed, are the most common type. The limited choices of fan speed make feedback controllers difﬁcult to control outlet water temperature at the desired target. As a result, feedback control is rarely used in the traditional cooling tower. With improving affordability, VFD fans have gained popularity and gradually replaced two-speed fans in cooling towers. This provides a great opportunity to fully exploit the energy-saving potential of VFD technology by applying feedback control to ensure outlet water temperatures are maintained at set points. Nevertheless, frequent ON/OFF switching of fans and the lack of a well-devised controller prevented industry from widely adopting or wholly utilizing this technology. As an alternative, rule-coded programmable logic controllers (PLC) were used instead of the feedback controller. The control of the outlet water temperature at the highest allowable temperature is then compromised, as is the energy-saving potential associated with the introduction of VFD-fans. The shortfall is more apparent in a large-scale cooling tower system comprising multiple cells. Therefore, this study proposes two operational strategies to fully exploit the energy-saving potential of VFD-fan-based multi-cell cooling towers.

2. Operational strategies

Section2.1describes the effects of a larger ‘‘approach value’’ on energy savings during fan operation. Section2.2details the implementation and theoretical development of zone-setting proportional-integral (PI) controllers. The rule based control strategy to avoid frequent ON–OFF switch is described in Section2.3.

2.1. Larger approach strategy

In a cooling tower system, heat is removed from the water by sensible heat, via temperature differences; and by latent heat, via the evaporation of small amounts of water. The evaporation process accounts for 80% of total heat removal. The rate of water evaporation in a cooling tower is determined by relative humidity, ambient air temperature and airﬂow rate. The thermal performance of the cooling tower depends principally on the wet-bulb temperature of inﬂowing air. The lowest outlet water temperature is limited by ambient air wet-bulb temperatures.

Two kinds of temperature differences are indexed for the design and operation of tower systems, namely, range and approach. The temperature difference between water entering and leaving the cooling tower is called range, (or cooling tower range, or cooling range). Range is determined using a heat load and water ﬂow rate, rather than by the thermal capability of the cooling tower. The difference between the outlet water temperature and the wet-bulb temperature of inﬂowing air is termed approach. At the design Nomenclature

CV controlled variable Dt sampling period (min) ek error term at thekth run f fan operating frequency (Hz) H relative humidity (%) I total number of fans KC controller gain (kW/°C) MV manufactured variable

pk power requirement of fan set (kW)

pmin power requirement of fan set at the minimum operating frequency (kW)

pcutoff the cut-off value of power requirement (kW)

s

I integral time (min/°C)

nfan,k number of fans operated atkth run Tsp set point temperature (°C)

Tair air temperature (°C)

Tw,out,k outlet water temperature atkth run (°C) Tw,in inlet water temperature (°C)

TUL upper limit temperature in the temperature zone control (°C)

TLL lower limit temperature in the temperature zone control (°C)

yk measured value of the CV at thekth run Ysp set point of the CV

492 C.-C. Chang et al. / Applied Energy 154 (2015) 491–499

(4)

stage, the approach value is vital for determining the physical size of the cooling tower.

Water temperature decreases along its path through the tower, due to evaporative cooling. The specific enthalpy of the saturated air film and its variation with water temperature is given by the saturation curve on the psychometric chart. The difference between the specific enthalpies of saturated and bulk air is the enthalpy driving force responsible for evaporative cooling. The driving force can be visualized in the enthalpy–temperature plot as shown inFig. 1. The distance between the process line and the saturation curve is the enthalpy driving force for evaporative cooling at certain points, e.g., line AA⁰, line BB⁰, line CC⁰, line DD⁰.[18,19]

It is apparent that the enthalpy driving force varies along the path from cooling water entering to its leaving. The average enthalpy driving force can be deﬁned as the area above the process line and below the saturation curve, as area ABB⁰A⁰, and CDD⁰C⁰shown inFig. 1, divided by cooling tower range.

At steady state, the heat load and water ﬂow rate are constant, and thus, the range is also constant. Subsequently, the enthalpy driving force increases as the outlet water temperature increases.

Taking the selected cooling tower as an example, the operational conditions are assumed as follows: input water temperature of 35.0°C, air temperature of 25.4°C, relative humidity of 75%, and ratio of liquid to air ﬂow rate of 2.02. If the output water temperature is 31.5°C, then Range is 3.5°C, the operation line is AB and the average enthalpy driving force is area ABB⁰A⁰ divided by 3.5, as shown inFig. 1. If the output water temperature is maintained at 33.0°C, then the operation line shifts to the line CD; the average enthalpy driving force becomes the area CDD⁰C⁰divided by 3.5. It is obvious that area CDD⁰C⁰ is larger than ABB⁰A⁰, so is the average driving force of the shifting operation line in the latter case of high output water temperature. Operations with large driving force consume less energy.

For the operation of an existing cooling tower, a large driving force means a large approach and, as indicated inFig. 1, a high outlet water temperature. Equipped with feedback control, a cooling tower with variable-speed fans can be automatically operated at the highest allowable outlet temperature.

2.2. Temperature zone control strategy

For a feedback control system, two process variables must be identiﬁed: the controlled (CVs) and manipulated (MVs) variables.

In a VFD-controlled cooling tower, operators adjust the fan operating frequency (Hz), which in turn determines the fan speed and

controls the outlet water temperature. Given the relationship between fan operating frequency (Hz) and power requirement, the power necessary to achieve the desired outlet water temperature can be calculated. Hence, outlet water temperature and fan power are considered as CV and MV respectively. In the following feedback controller, the objective is to reduce the error to zero:

ek¼y_sp;ky_k ð1Þ

wherekis the sequence index,ekis the error term on thekth run,yk

is the measured value of the CV at thekth run, andyspis the set point of the CV.

The discrete PI control algorithm is considered most appropriate because of its ﬂexibility, computational simplicity, cost effec- tiveness, and transparency[20]. The goal of feedback control is to maintain the measured value of the CV at its set point.

In this study, the measured value of the CV can be lower than the set point during periods of cold weather. When the sustained error occurs, the integral term keeps accumulating and continues to build up to saturate the controller output element (e.g., fully open or close of valves) in the traditional PI controller. Further buildup of the integral term while the controller is saturated is referred to as reset windup, and usually results in overshooting.

The velocity form of PI control is chosen in order to avoid the above-mentioned reset windup problem for the proposed temperature control. This approach inherently resolves anti-reset windup, because the summation of errors is not explicitly calculated. The velocity form of the formula for discrete PI control is described in Eq.(2).

Pkþ1¼p_kþKc ðekek1Þ þDt

s

1

ek

ð2Þ

Here,pis the value of the MV, namely the power requirement of fan set;Kcis the controller gain;

s

1is an adjustable parameter and is referred to as integral time;Dtis the sampling period; andKcand

s

I are determined by the process reaction curve method. In this study, these values are also further optimized by using data-driven estimation and a Matlab function, fmincon.

Zone control strategies have been proposed to stabilize some feedback control systems[21,22]. Theoretical developments of this method have been widely discussed in previous works. In order to reduce the number of ON/OFF events, the temperature zone control strategy is adopted in this work. The temperature is controlled within a pre-set zone rather than by a precise target value. When the measured outlet temperature is outside the upper or lower limits of the temperature zone, the target is set as the zone mid-value; the target is set to the present outlet temperature when its value is within the zone, i.e.,

ysp;k¼Tsp;Tw;out;k>TUL

y_sp;k¼Tsp;Tw;out;k<TLL

y_sp;k¼Tw;out;k;TUL>Tw;out;k>TLL

8>

<

>: ð3Þ

combining(1) and (3)gives,

ek¼yspyk;yk>TUL

ek¼yspyk;yk<TLL

ek¼0; TUL>yk>TLL

8>

<

>: ð4Þ

whereTw,out,kis the temperature for thekth run;Tspis the set point temperature; andTULandTLLare the upper and lower limit values, respectively. The three values have the following relationship:

TLL<Tsp<TUL. The power requirement can be calculated using Eqs.

(2) and (4). If the outlet temperature remains within the target zone no adjustments are necessary, so the power requirement remains constant.

28 30 32 34 36 38 40

40 60 80 100 120 140

C' A'

D' B'

Twin Twin'

Twout'

Saturation Curve Operating Line

D C

Enthalpy (kJ/kg) B

Temperature of water (^OC) A

Twout

Enthalpy driving force

Fig. 1.Inﬂuence of range shifting on enthalpy driving force.

(5)

2.3. Hybrid operation

For a multi-fan cooling system, there are two types of fan operations, namely, whole set of fans and partial set of fans in operation. In the cold weather, only partial set of fans are needed for service. A slight change of outlet cooling water temperature might cause frequent ON–OFF switch of fans, especially when fans are operated at the lowest speed. With the additional consideration of energy saving, we would devise looking-up tables to determine the number and the frequency of fans needed for operation. This is the stage of rule-based operation when power requirement are low and partial set of fans are needed in service.

When in the hot weather, all fans are needed to put in service, the frequency of fans are determined by computing the power requirement. This is the stage of equation-based operation. The cut-off value of power requirement,p_cutoff is needed to decide in which stage the operation is. To avoid frequent ON–OFF switching of fans, we suggest that the cut-off value is computed by the summation of power of one fan operating at the lowest speed and pow- ers of the rest fans operated at the next-to-lowest speed.

In Summary, when the required power is larger thanp_cutoff, all fans are operated simultaneously at the same frequency. If the required power is smaller thanp_cutoff, the number of fans required and their frequency are based on a rule-based table. There is no general rule-based table. A rule-based table is decided by number of fans and their operating frequency. The basic principle of a rule-based table is to minimize ON/OFF switch possibility. The rules of turning one more fan ON (or OFF) need to be carefully devised by the consideration of not turning the very one OFF (or ON) again if the consequent temperature slightly drops (or rises).

Therefore, the orders of switching fans ON when cooling water temperature increases and that when temperature decreases are not the same. The rule-based tables for looking-up in these two cases are slightly different.

3. Plant description

In this study, the cooling tower of a steel plant was investigated.

This cooling tower is one of relatively few retroﬁtted with VFD-fans. It provides cold water for a tandem cold mill and contin- uous annealing line. The designed water circulation rate is 11,000 m³/h. The operating water ﬂow rate is around 4000 m³/h.

The tower has six cells, each of which originally had a two-speed fan, of which four have been replaced with VFD-fans and the remaining two are usually operated at low speed throughout the year. The size of each cooling tower is 11158.6 m. The basin beneath the cooling tower is 10.8155.9 m. The real system residence time is about 15 min. The fan diameter is 7.31 m and maximal fan speed is 150 RPM. The operating frequency of each VFD-fan is from 30 and 60 Hz. The corresponding energy consumption is from 10 to 55 kW h. The energy consumption has a cubic relationship with the fan operating frequency. Marginal increase in fan operating frequencies therefore requires a large increase in energy consumption. It requires less power to run two fans at 30 Hz, i.e., 10.51 kW2, than to run one fan at 60 Hz, i.e., 54.59 kW. Furthermore, two cells, i.e., two fans operating at

30 Hz, provide a larger heat transfer surface than one cell with a fan operating at 60 Hz. For multiple-fan applications, it is recom- mended that all fans are operated simultaneously at the same frequency[13]without considering the frequent ON–OFF switching of fans in the situation of low cooling water temperature.

Prior to the improvement strategies, the cooling tower operation followed a set of heuristic rules based on the experience of the operators. Table 1 shows the main principles of the rules, which are written in a PLC. There is no deﬁnite target for control- ling the outlet water temperature. The rules initialize the ﬁrst fan at 30 Hz; when the outlet water temperature exceeds 31.0°C, an additional fan is operated for every 0.5°C increment until the outlet water temperature reaches 32.5°C. Subsequently, the outlet water temperature is checked every 20 min. At each check, the frequency of all fans is simultaneously increased by 5 Hz if the temperature is found to be increasing. The rules for switching the fans off are similar, but are applied in the reverse order; the only difference is the target temperature: the last fan is turned off when the outlet temperature drops below 30.0°C.

Except during hot seasons, the ON/OFF frequency is high when the original plant operation mode is applied. For example, operating one fan at 30 Hz is insufﬁcient to cool water, so a second 30-Hz fan is required. Subsequently, the outlet water may become cooler than the target temperature, and so one 30 Hz fan is turned off to conserve energy. As a result, the water temperature may increase again, and thus the ON/OFF cycle is frequently repeated.

4. Virtual plant simulation

This study uses real operational data from an eight-day period of March 21 to 29, 2014 to model the energy-saving effect of the proposed strategies. During that period, average daily energy consumption was 1334.6 kW h, and statistics for cooling water and air properties are shown inTable 2. The air temperature and humidity data reﬂect the typical spring season in Taiwan.

The model estimates outlet water temperature for the selected cooling tower, which is a function of inlet water temperature, air temperature, relative humidity, water ﬂow rate, and air ﬂow rate.

In this system, water flow rate is usually constant and therefore does not form an input variable to the model. There are three water streams returning to the cooling tower, each with its own temperature measurement. Moreover, air flow rate is not measured, but the total power for fan operation is logged. The air flow rate is determined by fan speed, which is proportional to fan frequency and is a cubic root function of fan power. In summary, the outlet water temperature is modeled using three inlet water temperatures, air temperature, relative humidity, and total fan power.

Table 1

ON/OFF rules of fan operations in the studied plant.

Rules for starting fan

Outlet water temperature T> 31 T> 31.5 T> 32 T> 32.5 T> 33.5

Operation 1st at 30 Hz 2nd at 30 Hz 3rd at 30 Hz 4th at 30 Hz Increase frequency (5 Hz)

Rules for stopping fan

Outlet water temperature T< 30 T< 30.5 T< 31 T< 31.5 T< 33

Operation 1st 2nd 3rd 4th Decrease frequency (5 Hz)

Table 2

Statistics of ambient air and cooling-water data used to model a virtual plant.

Tw,in,1

(°C)

Tw,in,2

(°C)

Tw,in,3

(°C) Tair

(°C) H (%)

Tw,out

(°C)

Max. 38.1 35.1 36.6 31.9 94.6 32.9

Min. 35.5 32 29 18.8 52.3 30.2

Average 36.8 33.5 33.3 25.4 74.6 31.6

Standard deviation 0.5 0.6 1.8 3.2 7.8 0.5

(6)

The following equation(6)is obtained from linear regression of the actual data set, and functions as the virtual cooling tower system:

Tw;out¼0:539Tw;in;1þ0:221Tw;in;2þ6:3210² Tw;in;3þ3:6110²Tairþ

6:4910³H2:1610³Pþ0:814

ð5Þ

whereTw,in,1,Tw,in,2, andTw,in,3are the temperatures of each inlet water stream;Tairis air temperature;His relative humidity; and Pis total power consumption of the cooling tower fans.

Fig. 2shows the modeling and prediction results. The data from the ﬁrst seven days are implemented as the modeling set, and the data of the last day are used as the prediction set. The model gives anR²value of 0.85 and the mean square error (MSE) of the prediction set is 0.05. The results indicate that the model can accurately represent a cooling tower system to examine the implementation of the proposed operational strategies. In this study, a linear model is favorable because of its explicit form. And it is accurate for the simulation of an eight-day operation. To build a general purpose of a cooling tower system for a whole year operation is not within the scope of this research. The authors [7,12,23] proposed multiple-linear-models dealing with the complex nonlinearity of a cooling tower system.

To validate the proposed strategies, the virtual plant must be equipped with a PI feedback controller, which replaces the rule-based PLC used in the existing system. The construction of the proposed control loop is described in Section 2. To reduce ON/OFF switching of the fans, this PI feedback controller must adopt hybrid operations that combine rule- and equation-based operations.

When ambient air temperature is low, partial startup of the four-fan set can sufﬁciently cool the water; the rules shown in Table 3are adopted in this controller.Table 3shows that shifting from three-fan operation to four-fan operation is from the case of one 40 Hz fan and two 35 Hz fans to the case of one 30 Hz fan and three 35 Hz fans. This arrangement provides less opportunity to immediately turn OFF one of the fans in the system when cooling water temperature subsequently decreases. Say, right after shifting to turn all fans ON, (i.e., one 30 Hz fan and three 35-Hz fans ON), if the temperature drops slightly, then we may turn one of the 35 Hz fans to 30 Hz, then another one of 35 Hz fans to 30 Hz, until

all four fans are on 30 Hz. Therefore, the cut-off value is based on the case of one 30 Hz fan and three 35-Hz fans, i.e., 55.36 kW.

When power requirement exceeds the cut-off value, all four fans are required, and Eq.(6)is executed:

f¼17:093þ1:8610⁶p³1:2310³p²þ0:376p ð6Þ wherefis the operated frequency, andpis the power requirement.

In Eq.(2), the MV (i.e., power requirement) is calculated in the controller. The number of fans required is determined by compar- ing the power requirement top_cutoff. If number of operating fans is less than 4, then one of the rules in Table 3matching with the power requirement is executed, otherwise Eq.(6)is computed to determine appropriate fan frequency.

The proportional and integral parameters,Kcand

s

1, of the controller in the virtual system are calculated by the process reaction curve method and further optimized by using data-driven estimation and a Matlab function, fmincon.Kcand

s

Iare obtained as 42.62 and 0.52 respectively. The heat load of the process is assumed to be constant during the studied period.

Fig. 3shows the ﬂow chart of detailed operation procedures of the virtual plant simulation.

5. Results and discussion

This section at ﬁrst illustrates how much increasing enthalpy driving force inﬂuence the reduction of fan energy requirements by taking the operation of the virtual plant as the case study. The strategies of large allowable approach and temperature zone control are employed to operate the virtual cooling tower system, whose construction is based on the data acquired from the existing

03/21 03/23 03/25 03/27 03/29

30 32 TCW,out

Modelled Tout Predicted Tout Measured Tout

Prediction set MSE : 0.05 Modeling set R² : 0.85

Fig. 2.Modeled results of the virtual plant.

Table 3

Operation rules for power requirement less than 55.36 kW.

Operation mode All close 30 Hz⁄1 35 Hz⁄1 40 Hz⁄1 30 Hz⁄1, 35 Hz⁄1 35 Hz⁄2

Power requirement (kW) 0 10.51 14.95 20.5 25.46 29.9

Operation mode 35 Hz⁄1, 40 Hz⁄1 40 Hz⁄2 35 Hz⁄3 40 Hz⁄1, 35 Hz⁄2 30 Hz⁄1, 35 Hz⁄3

Power requirement (kW) 35.45 41 44.85 50.4 55.36

Fig. 3.Flowchart of operation procedures of the virtual plant simulation.

(7)

system. The data of energy consumption and ON/OFF frequency of the fans from the existing system are served as the benchmarking values. The comparison of the results from the virtual plant against the benchmarking data validates the effects of the proposed strategies in this section.

5.1. Relationship between driving force and energy consumption

Fig. 4shows how the target outlet water temperature affects the average driving force and daily energy consumption in the virtual system in the different seasons in Taiwan. The original data of eight-day period from the existing system represent the typical climate conditions in spring season. The average temperature and humidity of air in this data set are Tair= 25.4°C and H= 74.6%

respectively. We designate average air properties ofTair= 20.4°C, H= 69.6% as the typical climate conditions in cold and dry winter and Tair= 30.4°C,H= 79.6% as in hot and humid summer. Then, the difference value, i.e., 5°C and 5% between summer (or winter) and spring is added (or subtracted) to (or from) each datum in the original data set to mimic the climate conditions in the summer and winter in Taiwan. Then the datasets are obtained for the case study in summer and winter respectively.

With an outlet cooling water temperature and the average air properties, we can calculate the average enthalpy driving force for each dot in Fig. 4. Taking the constant range of 3.5°C and liquid-to-air ﬂow ratio of 2.02, average driving forces are calculated by following the procedures described in Fig. 1 and Section2.1. Also with the dataset of eight-day in each season, we calculate the energy consumption for the whole period and take the daily unit with the ﬁrst piece of datum as the initial value in operating the virtual plant.

Fig. 4demonstrates that the cooling tower has the largest driving force and the lowest energy consumption during winter. The target outlet water temperature strongly affects energy consumption by the fans. Increasing the target outlet water temperature from 30.0 to 32.5°C reduces daily energy consumption of the fan from 2417 to 9 kW h in winter, and from 4851 to 1234 kW h in summer. From this deﬁnition of approach, it is clear that higher outlet water temperature is associated with larger approach value.

For an existing cooling tower, whether or not the heat load is constant, it is always practical and cost-effective to set the outlet water to the highest temperature allowable by the process, in order to take advantage of the large driving force, i.e., a large approach, and thereby achieve the required energy savings.

5.2. Implementation of the larger approach strategy

The simulation presented in this study demonstrates how a zone-control PI controller maintains the largest allowable approach in the process by minimizing ON/OFF switching. The simulation experiments were conducted on the same period of eight days. The simulated results were compared with those of the existing system.

For this cooling tower, adjustments to the fan operation followed the rules coded in the PLC, as shown inTable 1. The operational histories of the outlet water temperature and power consumption of the four-fan set are documented in Fig. 5.

Throughout the entire period, the fans were almost always automatically controlled by the PLC, except between 17:00 on 03/23/2014 and 11:00 on 03/24/2014 when, for unknown reasons, they were switched to manual control and set at 50 Hz. For the majority of that time, the outlet water temperature differed from the acceptable highest temperature (32.5°C) and at least one of fans was operational because the temperature never dropped below 30.0°C, which is the lower limit at which the coded PLC rules would turn OFF all fans. During that period, ON/OFF frequency was 60 and daily energy consumption was 1334.6 kW h.

The green lines inFig. 5indicate the results of operating the PI controller in the virtual system. The estimated outlet temperature is similar to or less than the target temperature of 32.5°C. For those periods when the temperature is less than 32.5°C, water can be cooled using the two ﬁxed-speed fans, and it is not necessary to turn on any of the VFD-fans.

In the simulation, estimated daily energy consumption was 625.9 kW h, which is only 47% of the real energy consumption, rep- resenting a signiﬁcant saving for the VFD-equipped cooling tower.

The results reveal that the approach must be adjusted in order to conserve energy. With a PI controller, the cooling tower can be automatically operated at the highest allowable outlet temperature. However, the side effect of attempting to achieve a precise target temperature, as shown in Fig. 5, is that the simulated ON/OFF frequency was 199, which is three times greater than that of the real operation. This outcome is considered unacceptable;

therefore, temperature zone control is proposed, combined with a PI controller.

31.5 32 32.5 33

0 20 40 60 80 100

Average driving force, (kJ/kg dry air)

target of outlet water temperature, (^oC)

29.5 30 30.5 31 0

1000 2000 3000 4000 5000

Energy consumption per day, (kWh/day)

Driving force - Winter Driving force - Spring Driving force - Summer Energy consumption - Winter Energy consumption - Spring Energy consumption - Summer

Fig. 4.Relationship between driving force, energy consumption, and outlet water temperature.

03/21 03/25 03/29

29 30 31 32 33 34

TCW,out (OC)

estimated Tout Original Tout T target

03/210 03/23 03/25 03/27 03/29

50 100 150 200 250 300

Date

estimated fan power consumption real fan energy consumption

03/23 03/27

29 30 31 32 33 34

estimated Tout Original Tout T target

fan power consumption (kW)

Fig. 5.Estimated results of precise target control operation.

(8)

5.3. Implementation of temperature zone control

A temperature zone control strategy is proposed to reduce ON/OFF switching. The set point temperature in a PI controller is an interval rather than a specific value. When outlet water temperature is outside the zone limits, the target temperature is set to the zone mid-point. While the outlet water temperature remains within the zone, the set point is the present outlet water temperature. When the controller is configured according to zone setting, fan operations remain unchanged unless the CV, i.e., outlet temperature, is outside the zone boundaries. The detailed configuration of a PI zone controller is described in Section2.

A total of six simulation experiments were conducted by replacing the set value of 32.5°C with a temperature zone. The temperature interval was initially set to 0.25°C, and this interval width was increased by 0.25°C in each subsequent simulation experiment (widest 1.5°C zone). All simulations used 32.5°C as the upper limit of the zone. The results for ON/OFF frequency during the 8-day period and the daily energy consumption for each of the six zone-controlled experiments are listed inTable 4, and compared with those of the original operation and traditional (precise target) PI control. It is apparent that energy consumption increases and ON/OFF frequency decreases as the zone width increases. For the widest zone (1.5°C), daily energy consumption is 1118.6 kW h, which is 84% of the original energy consumption;

the ON/OFF frequency is 34, which is almost half the original value of 60. For the 0.75°C zone, daily energy consumption is 826.2 kW h, which is 62% of the original energy consumption, and ON/OFF frequency is 63. The simulation results also show that the average, maximum, and minimum outlet water temperatures are similar for the different zones. In other words, by employing a PI controller operating a temperature zone strategy, a VFD fan can achieve 38% energy saving in a cooling tower without increasing the frequency ON/OFF switching.

From the results of the six simulation experiments inTable 4, the 0.5°C zone was selected as the representative case and compared against the original operational proﬁle, as shown inFig. 6.

During the simulation, the zone-based PI controller limits outlet water temperature within the 0.5°C zone, between 32.0 and 32.5°C, except in cold weather, when the outlet water temperature is less than 32.0°C without the use of the VFD-fans.

In Fig. 7, the typical 24-h operational proﬁles show how the temperature zone PI controller reduces energy consumption and the ON/OFF frequency of the fans.Fig. 7reports three cases: original operation, precise target, and zone control of 0.5°C width. The daily energy consumptions, from low to high, are 855, 1041, 1553 kW h, respectively. In the original strategy, as mentioned in Section5.2, the rule-coded PLC does not take advantage of setting the largest approach to conserve energy. The precise strategy sets an outlet water temperature similar to the target of 32.5°C, and the lowest energy consumption is at the expense of frequent

ON/OFF switching (in this case, 34 times within a day). For the 0.5°C zone control, the outlet water temperature varies between 32.0 and 32.5°C with 16 ON/OFF switches. It is worth noting that there are some instances when the outlet water temperature

03/21 03/23 03/25 03/27 03/29

29 30 31 32 33 34

TCW,out (OC)

estimated Tout of 0.5°C zone control Original Tout

T target

03/21 03/23 03/25 03/27 03/29

0 50 100 150 200 250 300

Date

Fig. 6.Estimated results using 0.5°C zone control.

03/28 03:36 03/28 09:36 03/28 15:36 03/28 21:36 03/29 03:36 30

31 32 33 34 35

TCW,out (OC)

estimated Tout of 0.5°C zone control estimated Tout of precise control original Tout

T target

03/28 03:360 03/28 09:36 03/28 15:36 03/28 21:36 03/29 03:36 50

100 150

Date

fan power consumption of zone control fan power consumption of precise control original fan energy consumption

Fig. 7.Typical daily outlet water temperature and fan power consumption for three operational scenarios.

Table 4

Number of ON/OFF switching operations and energy consumption for precise and zone-control strategies.

Number of ON/OFF operations

Average daily power consumption (kW h/day)

Tave Tmax Tmin

Original operation 60 1334.6 31.5 32.7 30.2

Precise target strategy (32.5°C) 199 625.9 32.1 32.8 31.0

Zone control strategy Zone width (°C) Tup(°C) Tlow(°C)

1.5 32.5 31 34 1118.6 31.8 32.6 30.8

1.25 32.5 31.25 42 1001.7 31.9 32.6 30.9

1 32.5 31.5 43 891.2 31.9 32.6 31.0

0.75 32.5 31.75 63 826.2 32.0 32.6 31.0

0.5 32.5 32 92 735.7 32.0 32.6 31.0

0.25 32.5 32.25 159 668.9 32.1 32.7 31.0

(9)

increases sharply in the precise target scenario inFig. 7. This is known as an overshoot, and is characteristic of traditional feedback control. The observations inTable 4andFig. 7suggest the following: implementation of a PI feedback controller and setting the largest allowable approach as the target temperature achieves maximum energy efﬁciency of an existing VFD-fan system, but at the expense of frequent ON/OFF switching of the fans; the addition of zone control to the PI feedback control signiﬁcantly reduces ON/OFF frequency and further conserves energy.

5.4. Field experiments

Two short field experiments were conducted to demonstrate the validity of the proposed strategies. Since all the facilities has existed, only one notebook computer with the implementation of Matlab and Excel software is used to substitute the existing programmable logic controller. The cooling tower fans were operated by the control panel in the control room. The field experiment pro- cedure was similar to the flowchart of virtual plant simulation.

According to cutoff value and power requirement, the operating frequency was calculated byTable 3or Eq.(6)The ﬁrst experiment adopted the traditional (precise target) feedback control with a set point of 32°C, and was run from 10:35 to 15:50 on a day in late spring. During the experimental period, average air temperature was 27.2°C, relative humidity was 57.9%, and wet-bulb temperature was 21.1°C.

Fig. 8clearly shows that the traditional feedback control keeps adjusting the fans’ power, and after one or two cycles, the outlet temperatures gradually converge to the set point.Fig. 8shows corresponding temperature and power change proﬁles for the simulated rule-coded PLC operation that was run at the same time.

The same virtual plant simulation was repeated for the next case (shown inFig. 8). At 12:35, the outlet temperature dropped below 31.5°C, causing one of the fans to be turned OFF. The average power level was 47 kW, while that for the PLC operation would be 55 kW. However, the traditional feedback control involves 11 ON/OFF operation, compared with 1 for the PLC operation.

The second ﬁeld experiment was run from 9:45 to 13:15 on the next day, using PI feedback control with a 0.5°C zone as described in Section2. During the experimental period, average air temperature was 31.0°C, relative humidity was 59.3%, and the wet-bulb

temperature was 24.5°C. The target temperature zone was set between 32.0 and 32.5°C.

Fig. 9 shows that the temperature dropped below the lower bound of the zone interval; at 10:30, the fan frequencies decreased from 40 Hz to 30 and 35 Hz respectively, with resulting energy consumption of 55.3 kW. The temperature remained below the lower zone boundary; at 10:34, one fan turned OFF, and the frequency of the other increased from 35 Hz to 40 Hz, with resulting energy consumption of 50.4 kW. At 11:00, the temperature remained below the lower bound of the zone interval; the frequency of the one operational fan decreased from 40 Hz to 35 Hz, with resulting energy consumption of 44.8 kW. At 11:30, the temperature exceeded the lower bound and the same fan increased frequency from 35 Hz to 40 Hz. In summary, the fan operation was only adjusted when the temperature dropped below the lower limit; otherwise, it remained unchanged. The experimental period involved 14 fan adjustments, with only 1 ON/OFF operation. The outlet water temperatures were controlled around the target region.

Under the same ambient air conditions as the ﬁeld experiment, the operations following the rule-coded PLC are documented in Fig. 8. At 10:00, outlet water temperature was 32.5°C; according to the PLC rules, all fans turned ON, with corresponding energy consumption of 63 kW. Subsequently, the temperature ﬂuctuated slightly but never dropped below 31.5°C, and therefore all the fans remained ON. Average energy consumption throughout this period was 56.3 kW compared to 63 kW in the case of PLC operation. The zone control case required 1 ON/OFF operation, whereas the PLC scenario required none.

Both experiments were conducted at noon with high ambient air temperature. The resulting energy savings are less than those shown in the virtual simulation cases. Nevertheless, both experiments verify that the proposed strategies, when applied in the ﬁeld, produce similar results to those of the simulation.

6. Conclusion

The technical and economic advantages of replacing traditional two-speed fans with VFD-fans in a cooling tower have been gaining popularity in the industry. Concerns over frequent ON/OFF switching and the lack of a well-devised controller lead to conservative

10:48 12:00 13:12 14:24 15:36

30 31 32 33

time TCW,out (OC)

Precise control PLC mode Target

10:48 12:00 13:12 14:24 15:36

0 20 40 60 80 100

time

precise control PLC mode

Fig. 8.Field experiment using precise feedback control with set point of 32°C.

10:04 10:33 11:02 11:31 12:00 12:28 12:57 30

31 32 33

time TCW,out (OC)

Zone control PLC mode Target

10:04 10:33 11:02 11:31 12:00 12:28 12:57 0

20 40 60 80 100

time

zone control PLC mode

Fig. 9.Experimental zone temperature control strategy.

(10)

operations, which allow for further improvements in energy efﬁ- ciency. In this work, a PI feedback controller with a temperature zone setting was proposed for an existing VFD-controlled cooling tower system to manage the outlet water temperature while reducing fan energy consumption and solving the problem of frequent ON/OFF switching.

Simulation results showed that the original energy consumption can be reduced by 38% with a 0.75°C temperature zone without increasing ON/OFF frequency. Additionally, 16% of the original energy consumption can be saved with a 1.5°C temperature zone and a 50% reduction in ON/OFF switching. An on-line ﬁeld experiment was conducted to test the proposed strategies. The results veriﬁed the energy savings and the reduction in ON/OFF switching.

The implementation of the proposed PI feedback controller with temperature zone settings has been underway at the selected plant. It is worth noting that the proposed strategies can be implemented without further investment or any additional hardware if the existing system is operated with a distributed control system (DCS). Without a DCS, the cost of purchasing the additional hardware needed is still much lower than maintaining the original operating strategy, given the long-term economic beneﬁts of energy saving, not to mention the reduction in CO2emissions. It was concluded that the proposed energy-saving strategies are very promising for further improvements in any VFD-fan-based cooling tower system.

Acknowledgement

The authors are grateful for ﬁnancial support from the Ministry of Science and Technology, Taiwan under Grant MOST 103-2622-E007-025.

References

[1]Milosavljevic N, Heikkilä P. A comprehensive approach to cooling tower design. Appl Therm Eng 2001;21:899–915.

[2]Kim J-K, Smith R. Cooling water system design. Chem Eng Sci 2001;56:3641–58.

[3]Panjeshahi MH, Ataei A, Gharaie M, Parand R. Optimum design of cooling water systems for energy and water conservation. Chem Eng Res Des 2009;87:200–9.

[4]Zou Z, Guan Z, Gurgenci H. Optimization design of solar enhanced natural draft dry cooling tower. Energy Convers Manage 2013;76:945–55.

[5]Stabat P, Marchio D. Simpliﬁed model for indirect-contact evaporative cooling- tower behaviour. Appl Energy 2004;78:433–51.

[6]Yu FW, Chan KT. Improved condenser design and condenser-fan operation for air-cooled chillers. Appl Energy 2006;83:628–48.

[7]Pan T-H, Shieh S-S, Jang S-S, Tseng W-H, Wu C-W, Ou J-J. Statistical multi- model approach for performance assessment of cooling tower. Energy Convers Manage 2011;52:1377–85.

[8] CTI 105-ATC (00) CTI Code Tower Standard Speciﬁcations for Acceptance Test Code for Water Cooling Towers: Cooling Technology Institute, 2000.

[9]Söylemez MS. On the optimum performance of forced draft counter ﬂow cooling towers. Energy Convers Manage 2004;45:2335–41.

[10] Castro MM, Song TW, Pinto JM. Minimization of operational costs in cooling water systems. Chem Eng Res Des 2000;78:192–201.

[11]Cortinovis GF, Paiva JL, Song TW, Pinto JM. A systemic approach for optimal cooling tower operation. Energy Convers Manage 2009;50:2200–9.

[12]Wang J-G, Shieh S-S, Jang S-S, Wu C-W. Discrete model-based operation of cooling tower based on statistical analysis. Energy Convers Manage 2013;73:226–33.

[13]Cohen B. Variable frequency drives: operation and application with evaporative cooling equipment. CTI J 2007;28:28–31.

[14] Muntean N, Volosencu C, Hedes A. A temperature control system for water cooling towers based on variable speed drives. In: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information, Bucharest, Romania, 2008. p. 184–89.

[15] Muntean N, Hedes A, Scridon S. Practical aspects regarding implementation of variable speed drives in cooling tower fans. In: Proceedings of the 6-th WSEAS/

IASME Int. Conf. POWER’06, Tenerife, Canary Islands, Spain, 2006. p. 70–5.

[16]Yu FW, Chan KT. Optimization of water-cooled chiller system with load-based speed control. Appl Energy 2008;85:931–50.

[17]Ahn BC, Mitchell JW. Optimal control development for chilled water plants using a quadratic representation. Energy Build 2001;33:371–8.

[18]Cheremisinoff N, Cheremisinoff P. Cooling towers selection, design and practice. Ann Arbor Science Publishers; 1981.

[19]Picardo JR, Variyar JE. The Merkel equation revisited: a novel method to compute the packed height of a cooling tower. Energy Convers Manage 2012;57:167–72.

[20] Seborg D, Edgar T, Mellichamp D, Doyle F. Process Dynamics and Control:

Wiley, 2010.

[21]Pan T-H, Sheng B-Q, Wong DS-H, Jang S-S. Consumption optimization by Run to Run control with zone control. J. Taiwan Instit. Chem. Eng. 2010;41:500–5.

[22]González AH, Odloak D. A stable MPC with zone control. J Process Control 2009;19:110–22.

[23]Pan T-H, Shieh S-S, Jang S-S, Wu C-W, Ou J-J. Electricity gain via integrated operation of turbine generator and cooling tower using local model network.

Energy Conversion, IEEE Trans 2011;26:245–55.

View publication stats View publication stats