Evaluation of an optimal ventilation IAQ control strategy using control performance assessment and energy demand

Energy and Buildings 98 (2015) 134–143

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Evaluation of an optimal ventilation IAQ control strategy using control performance assessment and energy demand Seungchul Lee a , Min Jeong Kim a , Se Hee Pyo a , Jeong Tai Kim b , Chang Kyoo Yoo a,∗ a b

Department of Environmental Science & Engineering, College of Engineering, Kyung Hee University, Yongin 446-701, South Korea Department of Architectural Engineering, College of Engineering, Kyung Hee University, Yongin 446-701, South Korea

a r t i c l e

i n f o

Article history: Available online 29 August 2014 Keywords: Ventilation system Indoor air quality PID Control performance assessment

a b s t r a c t The purpose of this study is to suggest an optimal indoor air quality (IAQ) strategy for ventilation systems based on the control performance assessment (CPA) technique, whereby the performance of a control system can be quantified by a performance index. Four control structures of the IAQ ventilation system are proposed using a combination of a proportional-integral-derivative (PID) controller which is employed to control the concentration of particulate matter (PM), and two feed-forward controllers which are used to reject the influence of the subway train schedule and the concentration of outdoor PM. The control structure with the highest control performance is selected as an optimal control structure and re-tuned by a line search algorithm. The control structure consisting of a PID controller and a feed-forward controller for rejecting the effect of the train schedule exhibited the best control performance as well as low energy demand and low healthy risk of IAQ. When compared to a conventional ventilation system, the optimized structure exhibited a 70% improvement in control performance and reduced almost healthy risk of IAQ while reducing energy demand by 36%. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Due to the increased use of insulation to reduce energy demand for heating and cooling, the ability to monitor and control the indoor air quality (IAQ) has become an important factor in the health and productivity of residents in interior spaces (e.g., buildings, underground stations) [1]. Ventilation systems are primarily used to maintain a comfortable and healthy level of the IAQ. As such, extensive research has been conducted on the characteristics and performance of ventilation systems [1–6]. Mankibi [1] developed an experimental scale model to suggest control strategies with low energy for a ventilation system according to intermittent building usage. Wang et al. [2] optimized the fresh air flow rate of a multi-zone variable air volume (VAV) air-conditioning system for the critical zone of a building when considering the carbon-dioxide concentration. Pan et al. [5] attempted to identify the relationship between the IAQ and energy efficiency of both VAV air-conditioning and fan coil unit (FCU) systems through the use of measured data and simulations. The effect of outdoor air disturbance on two office buildings in Shanghai, China made it difficult to ensure suitable IAQ. Interestingly, recent research has focused on identifying the

∗ Corresponding author. Tel.: +82 31 201 3824; fax: +82 31 202 8854. E-mail address: [email protected] (C.K. Yoo). http://dx.doi.org/10.1016/j.enbuild.2014.08.040 0378-7788/© 2014 Elsevier B.V. All rights reserved.

problem in terms of the IAQ and developing control strategies for ventilation systems so as to ensure both IAQ and an improvement in energy efficiency. Up to now, most studies have been devoted to IAQ monitoring and control for buildings located on the ground. However, underground buildings and semi-open built space have become major spaces of use for metropolitan residents [7–10]. While many control strategies have been suggested in previous reports [1–6,11,12], to the best of our knowledge only a few studies have been conducted on the quantification and comparison of control strategies for ventilation systems. Joo et al. [3] suggested an optimum ratio of makeup air and recirculation air to enhance IAQ (especially, CO2 ) and energy efficiency of the ventilation system for buildings in Seoul, Korea. Liu et al. [12] optimized a schedule of ventilation fan speed to minimize the concentration of particulate matter (PM) and energy demand of the ventilation system in an underground subway station, which was compared with the fixed ventilation fan speed. In this work, several practical control strategies are proposed for a ventilation control system in an underground subway station in Seoul, Korea. Furthermore, a new and promising quantified control performance index is suggested using the control performance assessment (CPA) technique. The energy demand of each control strategy is subsequently compared to achieve energy efficient ventilation control. Control performance assessment (CPA) or monitoring (CPM) is important for maintaining the efficiency of automated systems in

S. Lee et al. / Energy and Buildings 98 (2015) 134–143

Nomenclature AR ARMA CPA CPM F Fat FB FCOR FCU FF FOPTD G(s) IAQ IMC ITAE-1

MVC N N4SID PID PM PM10 Q R RPM SISO TMS VAV ZN at d fi k kc ku pu q-d T˜ t1 t2 uP (t) uI (t) uD (t) u(s) ylimit yt ys (t) y(s) y(t) d

auto regressive autoregressive moving average control performance assessment control performance monitoring vector form of constant coefficient of time series model for disturbance white noise part of yt feedback filtering and correlation analysis fan coil unit feed-forward first-order plus time delay transfer function in frequency domain indoor air quality internal model control tuning rule for PID controller integral of the time-weighted absolute value of the error tuning rule for a first-order plus time delay model tuning rule for PID controller minimum variance control disturbance transfer function of SISO process with the closed loop controller numerical algorithms for the subspace state space system identification proportional-integral-derivative particulate matter particulate matter less than 10 ␮m controller transfer function of SISO process with the closed loop controller remaining transfer function of time series model for disturbance revolutions per minute of a ventilation fan speed single input and single output tele-monitoring system variable air volume Ziegler–Nichols tuning rule for PID controller white noise sequence with zero mean of SISO process with the closed loop controller time-delay of SISO process with the closed loop controller constant coefficients of time series model for disturbance process gain proportional gain of PID controller ultimate period of process ultimate gain of process backshift operator in terms of the time delay d delay-free plant transfer function of SISO process with the closed loop controller time of starting point for an interval time of end point for an interval proportional part of controller output integral part of controller output derivate part of controller output process input in frequency domain upper limitation of IAQ process output of SISO process with the closed loop controller set-point of the process output process output in frequency domain process output in time domain derivative time of PID controller

i a2t 2 m v y2

135

integral time of PID controller variance of the white noise residuals of the timeseries model invariant portion of the output variance variance of system output

Greek symbols  filter parameter of IMC tuning rule  time delay of process performance index of SISO process with the closed  loop controller  time coefficient of process risk cumulative healthy risk assessment index Superscripts and subscripts t sampling time

various industrial processes, such as refinery, chemical, and pulp and paper processes [13]. The CPA method, which was first suggested by Astrom, Harris, and Stanfelj [14], involves the use of minimum variance control (MVC) to compare a benchmark with the current control loop performance. CPA techniques allow the control system performance to be assessed in terms of the variance of system outputs as well as the variance of the input and output simultaneously [15–19]. The economic factors of control systems have also been considered [14]. In this work, several strategies of proportional-integralderivative (PID), feed forward, and combined control with the CPA technique are evaluated to control the level of PM in the ventilation system of an underground subway station. The energy demand of each scheme is also examined. The procedure to find an optimal control strategy involves (1) the development of four control structures of a ventilation system for adequate IAQ in the underground subway station, (2) a comparison of the controller performance, energy demand, and cumulative healthy risk index of each control strategy, and (3) the tuning of optimal control parameters of the optimal IAQ control structure using the line search algorithm. The remainder of this paper is organized as follows. In Section 2, descriptions of the data, the subway ventilation system, CPA, the concept of PID control, and parameter tuning methods to control the concentrations of PM in an underground subway station by a ventilation system are given. Several control strategies are also suggested for the ventilation system using combinations of the feedback and feed forward control strategies and an appropriate tuning method. In Section 3, CPA and the energy demand of each control strategy are used to assess the performance of the suggested control strategies. The final control strategy is ultimately evaluated and compared to the other approaches using the optimal tuning rule. 2. Materials and methods 2.1. Data description The measured IAQ data set was collected at a D-subway station in Seoul, South Korea during the period from November 21 to November 25, 2011. Specifically, the following data were obtained: the concentration of particulate matter less than 10 ␮m (PM10 ) at the platform, the fan speed of the ventilation system, the schedule of the subway that passed through the station, and the outside PM10 concentration. The concentration of PM10 in the platform was obtained by a tele-monitoring system (TMS) (Fig. 1) at 3-min intervals, as shown in Fig. 2(a). The variation of the outside PM10

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S. Lee et al. / Energy and Buildings 98 (2015) 134–143

Fig. 1. Tele-monitoring system (TMS) for IAQ measurements.

concentration, which was taken at one-hour sampling times from the Seoul Metropolitan Research Institute of Public Health, South Korea, is displayed in Fig. 2(b). The subway schedule (Fig. 2(c)) was obtained from the Seoul Metro System (SMS) and the ventilation fan speed (Fig. 2(d)) was measured in revolutions per minute (RPM). In Fig. 2(a), the two peaks in the platform PM10 within each day correspond to rush hour periods. The outside PM10 concentration is also lower than the platform PM10 concentration, as shown in Fig. 2(b). The number of subway trains that passed through the D-subway station is given in Fig. 2(c). The ventilation fan speed (Fig. 2(d)) was operated at 45 Hz before 6 p.m., and then increased to 60 Hz from

6 p.m. to 10 p.m. for rush hour. After the rush hour, the RPM is set to 40 Hz for two hours. Direct measurements of the energy demand of the ventilation system were not obtained. Instead, the energy demand for calculated energy need was calculated according to the relationship between the ventilation fan speed (RPM) and its energy demand, as shown in Fig. 3. Energy demand = 0.0007 × RPM3 − 0.046 × RPM2 + 2.01 × RPM + 8.8

(1)

Fig. 2. Variation of the measured IAQ control variables from a D-subway station: (a) PM10 concentration in the platform, (b) outside PM10 concentration, (c) the subway schedule, and (d) ventilation fan speed (RPM).

S. Lee et al. / Energy and Buildings 98 (2015) 134–143

137

Fig. 6. Concept of the SISO process with the closed loop controller.

Fig. 3. Relationship between the ventilation fan speed (RPM) and energy demand.

adjustments (e.g., re-designing or re-tuning unsatisfactory controllers) to improve the control potential. Several CPA benchmarks have been developed to evaluate the performance of the current control system, including minimum variance control (MVC) and the linear quadratic Gaussian (LQG) [13,20,21]. The MVC benchmark based on the time series analysis technique is a simple but robust CPA method [13]. Thus, the MVC benchmark was used in this work to assess the performance of the controllers in the ventilation system. Fig. 6 shows a conceptual schematic of the single input and single output (SISO) process with the closed loop controller. The process output yt can be expressed as [16]: yt =

N 1 + q−d T˜ Q

(2)

at

where Q is the controller transfer function, d is the time-delay, q−d is the backshift operator in terms of the time delay d, T˜ is the delayfree plant transfer function, N is the disturbance transfer function, at is a white noise sequence with zero mean, and yt is the process output. The disturbance transfer function N is represented as a time series model using a Diophantine identity: N = f0 + f1 q−1 + · · · + fd−1 q−(d−1) + Rq−d = F + Rq−d

Fig. 4. Schematic diagram of the ventilation system at a D-station.

The ventilation control system for the concentration of PM10 in the platform consists of one manipulated variable, one controlled variable, and two disturbance variables, as displayed in Fig. 4. The manipulated variable is the fan speed of the ventilation system (denoted as “RPM”), while the controlled variable is the PM10 concentration at the platform (denoted as “Platform PM10 ”). The disturbance variables are the subway schedule (denoted as “Schedule”) and the outside PM10 concentration (denoted as “Outdoor PM10 ”), both of which can directly affect the platform PM10 concentration. 2.2. Control performance assessment A schematic of the CPA framework used to diagnose the current control system is shown in Fig. 5. If the control system does not exhibit satisfactory performance, then engineers will make specific

(3)

where fi (for i = 1,. . .,d − 1) are constant coefficients that can be replaced by F, and R is the remaining transfer function. Eq. (2) can be rewritten by substituting Eq. (3) for N as follows [16]:



yt =

= F+

F + q−d R at 1 + q−d T˜ Q



R − F T˜ Q −d at q 1 + q−d T˜ Q

(4)

= Fat + Lat−d As a result, the system output yt is divided into two parts, Fat and Lat−d . Here, Fat is the white noise part of yt and is predictable by the time series model. The two parts of yt are independent and the variance of the system output can be described as follows [17]: Var(yt ) = Var(Fat ) + Var(Lat−d )

(5)

Var(yt ) ≥ Var(Fat )

(6)

According to Eq. (6), the invariant portion of the output variation is equal to the variance of Fat ; it can be described as a summation of the product of the constant coefficients fi (for i = 1,. . .,d − 1) and the white noise at as follows: 2 m v

=

 d−1   fi2

a2t

(7)

i=0

Fig. 5. A conceptual schematic of the process assessment framework.

2 is the invariant portion of the output variance and  2 is where m v at the variance of the white noise residuals of the time-series model

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for the process [22]. Finally, the performance index is obtained using both the invariant portion of the output variance and the variance of the data: =

2 m v

(8)

y2

where ŋ is the performance index and indicates the potential for improving the control performance. The performance index has a value between zero and one [14,22]. In general, if the performance index is close to one, the controller is operated more accurately. 2.3. Proportional-integral-derivative (PID) control The proportional-integral-derivative (PID) method is used to control the IAQ by manipulating the fan speed of the ventilation system. PID controllers are widely employed in feedback control loops in most industrial plants due to their simple structure, robustness, easy implementation, and good performance [23–27]. As indicated by the name, PID controllers consist of a proportional part, an integral part, and a derivative part as follows [28]: Proportional part: uP (t) = kc (ys (t) − y(t))

(9)

Integral part: kc uI (t) = i

t (ys () − y ()) d

(10)

0

Derivative part: uD (t) = kc d

d(ys (t) − y(t)) dt

(11)

where ys (t) is the set-point of the process output, y(t) is the process output, and uP (t), uI (t), and uD (t) are the controller outputs of the proportional, integral, and derivative parts, respectively. The constants kc ,  i , and  d in Eq. (11) are the proportional gain, integral time, and derivative time, respectively. The total output of the PID controller is a summation of the three individual outputs in Eqs. (9)–(11), i.e., [28]: u(t) = uP (t) + uI (t) + uD (t) kc = kc (ys (t) − y(t)) + i

t (ys (t) − yd + kc d

d(ys (t) − y(t)) dt

Fig. 7. Process identification with the relay feedback method.

of the process. By a Fourier series approximation, the ultimate gain (pu ) and ultimate period (ku ) are estimated as [29]: pu ≈ pr ku ≈

4d a

∞ ITAE =

0

2.4. Parameter tuning To achieve high performance with a PID controller, the controller parameters must be tuned. Numerous tuning methods have been developed, including the Ziegler–Nichols (ZN) method, internal model control (IMC), and the integral of the time-weighted absolute value of the error tuning rule for a first-order plus time delay model (ITAE-1). In this study, all three methods were implemented to tune the controller in the IAQ ventilation system. The ZN tuning rule requires the ultimate gain and ultimate period of a given process for tuning controller parameters. Such process information can be obtained from the relay feedback identification, as shown in Fig. 7. After four or five cyclic inputs, the output reaches a cyclic steady state that includes the ultimate information

(14)

The tuning parameters of the PID controller are estimated in accordance with the ZN tuning rule given in Table 1. The ZN tuning method exhibits robustness in the presence of disturbances and acceptable control performance for usual processes. However, it shows poor performance in processes with underdamped or dominant time delays [24,28]. The IMC tuning rule is based on the internal model principal in accordance with the prescribed model. The first-order plus time delay (FOPTD) model is necessary if the parameters of the controller are to be tuned by the IMC method. Table 2 shows the IMC tuning rule. The variable  (≥2.5) is used for the PID controllers. The IMC tuning rule shows good performance for a step set point change, but poor performance for disturbance rejection as well as underdamped and high-order processes because of the limitations of the FOPTD model structure. In addition, the performance of the IMC tuning rule is more related to the identification model than other tuning rules [24,28]. The ITAE-1 tuning rule can be used to calculate the optimal parameters of the PID controller in terms of a minimization of the criteria [28]:

(12)

The three constants kc ,  i , and  d are usually called the parameters of the PID controller and should be determined under the dynamic condition of the process. Finally, the parameters for the improvement of the control performance must be tuned.

(13)





t ys (t) − y(t) dt

(15)

0

Table 3 shows the tuning formulas of the ITAE rule. There are two types of tuning methods, one is for disturbance rejection and the other is for a step set point change. The ITAE-1 tuning rule for disturbance rejection shows good performance, whereas the ITAE Table 1 Controller parameters’ tuning rule based on the ZN method. Tuning parameters

Controller

PID

kc

i

d

ku /1.7

pu /2.0

pu /8.0

Table 2 Controller parameters’ tuning rule based on the IMC method. Controller

PID

Tuning parameters kc

i

d

(2 + )/2( + )

 + /2

/(2 + )

S. Lee et al. / Energy and Buildings 98 (2015) 134–143 Table 3 Controller parameters’ tuning rule based on the ITAE-1 method. Controller

ITAE-1 disturbance ITAE-1 setpoint

139

Table 4 Suggested control structures for the ventilation system.

Tuning parameter kc

/ i

 d /

1.357(/)−0.947 0.965(/)−0.850

0.842(/)−0.738 0.796–0.1465(/)

0.381(/)0.995 0.308(/)0.929

Control structure

Description

S1

PID controller (ventilation fan speed − platform PM10 ) PID controller + feed-forward controller 1 PID controller + feed-forward controller 2 PID controller + feed-forward controller 1 + feed-forward controller 2

S2 S3 S4

tuning rule for set-point tracking exhibits poor performance due to sluggish control movement. 2.5. Proposed method The objectives of this study are (1) to assess the control performance of different control strategies for an IAQ ventilation system, and (2) to determine an optimal control strategy and its tuning parameters in order to maintain a healthy PM10 concentration in an underground building space. Fig. 8 shows a flowchart of the proposed ventilation control strategy. In the first step, the ventilation control system is implemented using Matlab/SimulinkTM . In the second step, four IAQ control structures of the ventilation system are designed based on the FOPTD identification model. The FOPTD model establishes the kinetics of a process using a mathematical expression. One such expression is a transfer function that is the ratio of the Laplace transform of the process output and process input: G(s) =

y(s) k exp(−s) = s + 1 u(s)

(16)

where y(s) and u(s) are the process output and input, respectively, k is the gain,  is the time coefficient, and  is the time delay. Table 4 lists the four proposed control structures of the feedback for PID control, feed-forward (FF1, FF2) control, and combined FB-FF control. In S2 and S3, two feed-forward controllers (FF) are used to reject the disturbance effects of the subway schedule and the outdoor PM10 concentration on the IAQ control, respectively. In S4, the combined feedback of the PID controller and two feed-forward controllers is utilized to simultaneously reject the disturbance effects of the subway schedule and outdoor PM10 concentration. Here, the parameters of the PID controller are determined by the ZN, IMC,

Fig. 8. The procedure of the proposed ventilation control strategy using CPA.

and ITAE-1 tuning rules. Finally, the CPA technique is employed to monitor the performance of each IAQ control law (4 control structures × 3 tuning rules = 12 control strategies). The control structure with the highest CPA is selected as the best one for IAQ control, and its tuning parameters are re-tuned by the line search algorithm for more precise fine-tuning. To evaluate other performance aspect of the control structures, the energy demand rate and the cumulative healthy risk assessment index (risk ) are compared. The energy demand rate is calculated using Eq. (1) which is mentioned in the previous section. The cumulative healthy risk assessment index is used to represent the public health impact of the controlled IAQ by the suggested control structures (in Fig 9) and it calculated by time weighted difference between upper limitation of IAQ regulation and IAQ data points that are located above the limitation of IAQ regulation.

t2 risk =

  ylimit − y (t) dt, y > ylimit

(17)

t1

where t1 and t2 are the time of starting point and end point for an interval when the IAQ data points are located above limitation, ylimit is the upper limitation of IAQ (120 ␮g/m3 for PM10 in Korea), and y(t) is the IAQ data point at time t. 3. Results and discussion 3.1. Ventilation system modeling and controller design Fig. 10 shows the control structure of the ventilation system for IAQ control in a subway station; it consists of one PID controller and two feed-forward controllers. These controllers are implemented based on the transfer functions of IAQ dynamics, which describe the variation of the platform PM10 concentration with respect to

Fig. 9. The concept of cumulative healthy risk assessment index for IAQ.

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Fig. 10. Control structure of the IAQ ventilation system with PID, FF1, and FF2 implemented by Matlab/SimulinkTM .

variations of the outdoor PM10 concentration and the subway vehicle schedule. To implement the four control structures in Table 4, gain blocks are used to turn the feed-forward controllers on or off by changing the gains to 1 or 0, respectively. ZN, IMC, and ITAE-1 tuning rules are used for each ventilation controller and their performances are evaluated using the CPA technique. Table 5 lists the tuned parameters of the PID controller according to the three tuning rules. In general, a small value of  i guarantees high control performance, but results in an unstable system because a small  i induces a large magnitude of the input. The system input (RPM) is defined by Eqs. (9)–(11). Therefore, the parameters kc ,  i , and  d are related to the ventilation fan speed. The parameters tuned by ZN induce a large variation of RPM to control the IAQ, since the values of kc , kc / i , and kc ×  d that are multiplied by the difference between the setpoint of the output and the measured output are larger than the values of other tuning methods. The coefficient obtained with Eqs. (9)–(11) using the parameter tuned by the IMC rule shows the lowest value and causes a small variation of RPM. Note that the IMC tuning rule exhibits high control performance according to the minimum value of  i [28]. 3.2. Evaluation of control performance of four IAQ ventilation control strategies with tuning rules

the autoregressive moving average (ARMA) model. In this study, numerical algorithms for the subspace state space system identification (N4SID) method are employed to estimate the potential 2 ) variances of the ventilation control systems. The variances (m v of each control structure with the tuning rules are obtained by conducting twelve simulations. The performance indices are subsequently calculated using Eq. (8). Fig. 11 shows a comparison of the performance indices for the four control structures with the three tuning methods. It can be seen that all of the proposed ventilation control strategies with tuning rules exhibit much better performance than the existing manual control scheme. Based on the CPA technique, the PID controller combined with a feed-forward controller (S2) shows the best control performance without reference to the tuning rules. This is due to the fact that the FF1 controller successfully rejects the effect of the train schedule and, since the train schedule is fixed for each day, the effect of the train schedule from the platform PM10 can be easily calculated. However, it is interesting that the performance index of the control structure with S2 is better than that with S4. This may be due to the low performance of the feed forward controller for rejecting the effect of outdoor PM10 . It is known that the performance of an FF controller depends on the accuracy of the process model. Because the dynamics of the outdoor PM10 and their effect on the platform PM10 concentration in a subway station are more

After designing four IAQ control strategies, the CPA technique was employed to compare the control performances of the twelve control outputs (4×3 = 12). To calculate the performance index of the control strategies, the potential variance (y2 ) of the ventilation system was determined using the filtering and correlation analysis (FCOR) algorithm [14]. The FCOR algorithm is able to estimate the potential variance of the process from the controlled output data using the residual of the auto regressive (AR) time series or Table 5 Parameters of the PID controller tuned by ZN, IMC, and ITAE-1 tuning rules8 . Tuning rules

ZN IMC ITAE-1 *

 = 1.7 in the IMC tuning rule.

Tuned parameters kc

i

d

−0.350 −0.1368 −0.161

0.074 0.0443 0.049

0.019 0.0092 0.024

Fig. 11. Comparison of the performance indices for the four control structures with three tuning methods.

S. Lee et al. / Energy and Buildings 98 (2015) 134–143

141

Table 6 Performance indices and energy demand of the ventilation system with the suggested control structures. Control structures

Indices

Control performance () Cumulative healthy risk assessment index (ITAE ) Energy demand (kW/d) *

Manual

S1

S2

S3

S4

PID + FF1 (optimum)

0.16 3.39 × 103 1839

0.21 1.79 × 105 1154

0.80 1.68 × 102 1171

0.21 2.00 × 105 1170

0.72 7.78 × 102 1189

0.86 2.21 1172

ITAE was calculated using IAQ data above the upper limitation (=120 ␮g/m3 ).

complex than the train schedule (which is operated periodically), the accuracy of the FF process model in determining the effect of the platform PM10 on the outdoor PM10 is worse than that for the train schedule. The low accuracy of the FF process model due to irregular variations of the outdoor PM10 ultimately leads to low performance for the FF controller (S3). Table 6 shows that S2 is the best ventilation control structure to control the IAQ in the underground subway station. To obtain optimum control parameters, the line search method is employed. With the control structure fixed as S2, the proportional gain (kc ) of the PID controller was increased from −0.3 to −0.1 with a step change of 0.002. Fig. 12 shows the variations in the performance index of the fine tuning parameters for the S2 control system with respect to variations in kc . The optimal performance index is found to be 0.8607, and the optimal proportional gain (kc ) of the PID controller is −0.192. The absolute value of the optimal proportional gain is higher than the proportional gain tuned by the ITAE-1 rule. This is due to the fact that a larger ratio of input to output is necessary to maintain the IAQ concentration in the underground subway station when compared to the ratio of input to output obtained by the ITAE-1 tuning rule. The CPA based on the MVC leads to an increase in the RPM so as to reduce rapid rises in the platform PM10 , which exhibits peaks and periodic variations due to the rush hour periods and train schedule. Fig. 13 shows the controlled output of the platform PM10 concentration and the energy demand for the proposed control and manual control schemes. In Fig. 13(a), both the variance

of the platform PM10 and the number of points that are over the maximum Korea IAQ limit (120 ␮g/m3 ) are significantly reduced when optimal parameters are used. Specifically, the average concentration of platform PM10 is approximately 95.6 ␮g/m3 since the set point of the proposed control is 120 ␮g/m3 (the legal limit). This value is higher than the average concentration of platform PM10 (64.1 ␮g/m3 ) obtained with the manual scheme. The energy demand of S2 with the optimal parameters is also much lower than that observed in the case of manual control, as shown in Fig. 13(b). In addition, the variation in the energy demand of S2 with optimal parameters is similar to the variation of the platform PM10 obtained by the manual control. For the manual control scheme, a periodic pattern in the platform PM10 was observed because manual control of the ventilation system does not allow for control of the IAQ concentration in an underground subway station. Such a limitation arises because the manual control cannot handle the disturbance effect of the subway schedule and outdoor PM10 . In contrast, the proposed control structure (S2) with optimal parameters effectively rejects the influence of disturbances using a combination of FB and FF controllers, while also maintaining low energy demand. From these results, it can be inferred that S2 with optimal parameters is more robust than the manual control strategy. However, the average concentration of platform PM10 observed with S2 is larger than that obtained with the manual control. Table 6 compares the performance indices, energy demand, and cumulative healthy risk assessment index of the four ventilation

Fig. 12. Determination of an optimal tuning parameter based on a variation of the performance index for the S2 control structure.

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Fig. 13. A comparison of the S2 control structure with an optimal control parameter and manual control: (a) controlled PM10 concentration at the platform and (b) energy demand of the ventilation system for IAQ.

control strategies for IAQ control. All proposed control structures outperform the current manual control strategy. However, the cumulative healthy risk assessment index of S1 and S3 are both larger than that obtained with the manual control. This is because the implementation of PID controllers, while enhancing control performance, makes it more difficult to keep the IAQ under the criteria. S2 exhibits high control performance and its cumulative healthy risk assessment index is significantly reduced than that of manual control. It means that the healthy risk due to the exceeded the limitation of PMs almost removed by S2 control structure while the average concentration of platform PM10 of S2 is larger than that of manual control. Ultimately, the S2 control structure with an optimal tuning parameter obtained by the line search algorithm shows the best control performance with the lowest cumulative healthy risk assessment index. If the manual control is replaced with the S2 control structure having an optimal parameter, the control performance is increased by 70% and the IAQ is maintained under the legal indoor limit in Korea. On average, the energy demand is reduced by 36% with all of the control structures. While the energy demand of the S2 control structure with the optimal parameter is slightly larger than that of the other control structures (S1, S2 without an optimized parameter and S3), the S2 structure with the optimal control parameter shows a dramatic improvement with respect to control performance and cumulative healthy risk assessment index. Therefore, the S2 control structure with an optimal control parameter is the best overall control structure when considering control performance, cumulative healthy risk assessment index, and energy demand. 4. Conclusions An optimal control structure of a ventilation system in an underground subway station was selected among four proposed control structures in terms of control performance and energy demand. To

assess the performance of the control structure, a CPA technique based on the variance of the system output was employed, and the cumulative healthy risk assessment index and energy demand were examined. Among the suggested control structures, the PID controller with a feed forward controller for the train schedule (S2) showed high control performance. To improve the control performance of S2, the proportional gain (kc ) of the PID controller was optimized by a line search algorithm. The optimized value of kc yielded a higher ratio of the system input to output when compared to other values. Consequently, the optimized S2 control structure exhibited the best performance among all proposed structures in terms of the CPA, cumulative healthy risk assessment index, and energy demand. When compared to the open loop structure, a 70% improvement in the control performance, a 36% reduction in energy demand, and an elimination of almost healthy risk due to particulate matter were observed. In future work, a new CPA technique that considers control performance, cumulative healthy risk assessment index, and energy demand simultaneously will be developed.

Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (no. 2008-0061908).

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