Gain scheduling based ventilation control with varying periodic indoor air quality (IAQ) dynamics for healthy IAQ and energy savings

Energy and Buildings 153 (2017) 275–286

Contents lists available at ScienceDirect

Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild

Replication Studies paper

Gain scheduling based ventilation control with varying periodic indoor air quality (IAQ) dynamics for healthy IAQ and energy savings Seungchul Lee a , Soonho Hwangbo a , Jeong Tai Kim b , Chang Kyoo Yoo a,∗ a b

Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Yongin 446-701, Republic of Korea Department of Architectural Engineering, Center for Environmental Studies, Kyung Hee University, Yongin 446-701, Republic of Korea

a r t i c l e

i n f o

Article history: Received 24 September 2016 Received in revised form 19 June 2017 Accepted 8 August 2017 Available online 12 August 2017 Keywords: Gain scheduling Energy saving Indoor air quality (IAQ) Ventilation control system Ventilation energy Periodic energy demand

a b s t r a c t The subway is a popular mode of transportation during the morning and evening rush hours. At these times, an underground subway station generates a high concentration of air pollutants and affects the dynamics of indoor air quality (IAQ) differently compared to other normal operation times. This study proposes a new ventilation control system targeting the varying IAQ dynamics by using a gain scheduling method. The gain-scheduled ventilation control system is implemented at a D-subway station and consists of one feedback and two feedforward controllers that manipulate ventilation inverter frequency. These controllers were able to reject the effect of the train schedule and the effect of the outdoor air quality (OAQ). The feedback controller of the gain scheduling method was tuned by ZN, IMC, and ITAE-1 tuning rules for the three different operating time zones (morning rush hour, evening rush hour and ordinary time zone), and the ventilation control performances are compared. The results show that the proposed gain-scheduled ventilation control system saved 4% of the ventilation energy compared to the manual ventilation system while maintaining a comfortable IAQ under 120 ␮g/m3 . © 2017 Elsevier B.V. All rights reserved.

1. Introduction As social infrastructures are being modernized, rooms tend to be sealed for energy saving and energy efficiency as well as preventing hazardous pollutants to move into main indoor spaces. However, indoor sources such as heating, ventilation, air conditioning, insulation, building materials, furniture, chemicals, cigarette smoke, and the movement of occupants can easily generate and accumulate air pollutants. Indoor air quality (IAQ) depends on various and complicated factors such as indoor and outdoor temperatures, air pollution, humidity, and ventilation conditions. IAQ is important in that it affects public health and welfare because many people in contemporary society spend most of their time in indoor buildings and are threatened from the exposure to persistent harmful materials [1]. The concentration of indoor air pollutants increases inevitably in some places regarding overcrowding and inappropriate ventilation [2–5]. For this reason, the Korean Ministry of Environment recognizes that subway stations are a potentially dangerous indoor space and advises that indoor air pollutants be kept under an acceptable level. Therefore, Seoul metro has monitored indoor air pollutants by using a tele monitoring system (TMS) and

∗ Corresponding author. E-mail address: [email protected] (C.K. Yoo). http://dx.doi.org/10.1016/j.enbuild.2017.08.021 0378-7788/© 2017 Elsevier B.V. All rights reserved.

has implemented ventilation systems to manage and control pollutant accumulation in subway stations. To control IAQ, it is important to understand the dynamics of IAQ, which are easily influenced by annual events such as the heavy rainy season and yellow dust [6,7]. In addition, the variation of train schedule and the number of passenger are key factors affecting the IAQ [6]. Energy consumption of the ventilation system is another important factor in achieving healthy IAQ because approximately 50% of the total electricity used in the building space is consumed [8–10]. Furthermore, a lot of research has been conducted in order to improve the energy efficiency of the ventilation system and IAQ quality in the building space [10–13]. Gruber et al. [11] evaluated four alternative strategies (the total occupancy strategy, operation of local system strategy, room air temperature strategy, and optimal strategy) on heating, ventilation, and air conditioning (HVAC) system and satisfying desired indoor climates in office buildings. They validated two types of HVAC system and two types of buildings including multiple local zones with comparison of outdoor air temperature (OAT) strategy. The proposed strategies saved up to 30% of energy demand of the HVAC systems, furthermore the optimal strategy reduced up to 39% of energy demand while maintaining thermal comfort and IAQ. Cho et al. [12] implemented an air-cleaning unit and demand control on a ventilation system for a multi-residential building. The proposed ventilation system controlled IAQ using outdoor air, cleaning indoor air (by air-cleaning

276

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

unit), and both of them according to the CO2 and formaldehyde concentrations for minimizing the energy demand of the system. As a result, the system saved 20% of energy demand compared to the conventional system with constant ventilation rate. Son and Lee [10] analyzed two economizer systems according to mixed air temperature, outdoor air fraction, and cooling demand under varying outdoor temperature of four climatic conditions for reducing cooling energy demand. The proposed two economizer systems, differential dry-bulb control and differential enthalpy control, were implemented to simulation model of an office building, and differential enthalpy control method showed around 10% of energy saving under Korean climate that represents cool and humid condition. Dongmei et al. [13] numerically optimized supply air flow rate, supply air flow temperature, and supply vane angle of a novel bed based on task/ambient conditioning (TAC) system according to predicted mean vote (PMV) and energy utilization coefficient (EUC). The numerical optimization method resulted in 75 L/s of supply air flowrate, 30 ◦ C of supply air flow temperature, and 30◦ of supply vane angle of the TAC system as optimal solutions, satisfying acceptable level of thermal comfort (range of PMV = −0.5 to 0.5) and low energy consumption (EUC  1). However, ventilation systems for the subway station still operate with a fixed ventilation inverter frequency according to a time schedule. This existing fixed time schedule method causes excessive energy consumption and insufficient ventilation because it does not account for the IAQ dynamics, the train schedule or the number of passengers in the IAQ control system. Remember that unnecessary ventilation power is sometimes consumed and too low of a ventilation is sometimes given; therefore, it is necessary to develop a new ventilation method that can control periodic IAQ dynamics and achieve a balance between maintaining the IAQ and minimizing the energy consumption. Heat and CO2 of buildings in the current ventilation system have been controlled by adjusting the amount of ventilation into buildings or underground spaces. Some researchers tried to find a strategy that can modulate the required ventilation rate according to the occupants’ usage [14–18]. For example, Wemhoff [14] confirmed that an advanced control strategy reduced energy consumption by a maximum of 35% by adjusting the tuning parameters of a proportional-integral-derivative (PID) controller in a ventilation system considering the difference between indoor and outdoor temperatures. Mofidi and Akbari [15] developed multi-objective energy management system for improving occupants’ productivity and energy saving based on thermal comfort, IAQ, consumed energy information, and occupancy information. The proposed system was analyzed by simulation of a commercial building in Montreal, Canada, and it provided the optimum solution of indoor conditions for specific situations with high economic improvements (up to 1000/year/person). Mofidi and Akbari [16] implemented multi-objective optimization method for finding optimal solution of indoor environment control system in an office, with five zones, located in Montreal, Canada while improving personalized productivity of workers with minimum energy cost under varied thermal preference of each occupant. The sensitivity analysis of the proposed method was conducted to individual productivity and thermal tolerance. The proposed method more focused on the more productive occupant in thermal preference and high thermal tolerance of occupant had benefit in energy cost. Mei and Xia [17] implemented model predictive control (MPC) into direct expansion air conditioning (DX A/C) system for improving thermal comfort (indoor air temperature and humidity) and indoor air quality (CO2 concentration) with low energy demand of the system. Based on non-linear model of IAQ developed for considering coupling effect of the IAQ variables, open-loop optimization resulted in an optimum IAQ condition of the DX A/C system under steady state condition, and closed-loop optimization with MPC

reduced 1.39 kWh/day of energy consumption of the system compared with optimized open-loop system. Furthermore, Wang et al. [18] proposed predictive control method for maintaining indoor environment and reducing energy demand of HVAC system using information of occupant number change detected and calibrated by video camera and CO2 concentration. The control method was examined by simulation and experiments and it saved 39.4% of total energy consumption in HVAC system under acceptable thermal comfort. However, the above mentioned methods are relatively complex and not easy to be applied to a real system such as a subway system. In addition, particle matters (PMs), which are the main pollutant in subway station are generated by wear and tear of the railway line, wires, and brakes of the subway [19], and should be controlled in the subway system. Indeed, ventilation system for subway stations needs to be controlled based on the level of PMs. The objectives of this study are 1) to propose a new gainscheduled ventilation control system that incorporates process information of the train schedule, high traffic times (i.e., rush hour), and dynamics of outdoor air quality, and 2) to maximize energy saving while maintaining a healthy IAQ. First, an IAQ model representing the relationship between the PMs and the inverter frequency of the ventilation system is developed using system identification. Then, a PID controller, which reduces the difference between the actual pollutant’s level and the set-point of the controller, is used to control the PMs concentration. Because it is required to tune parameters of the PID controller appropriately, several tuning methods, including Ziegler-Nichols (ZN), an internal model control (IMC) and the integral of the time absolute value of error (ITAE), are compared in the ventilation control system. Finally, gain scheduling based on the rush hour effect on the IAQ is used to tune PID parameters, and feedforward control is added to reject the disturbance effects of the train schedule and the outdoor air quality (OAQ). The proposed ventilation control is tested at a D-subway station and the effectiveness of the proposed control system is demonstrated.

2. Materials and methods 2.1. System configuration This study is conducted at a D-subway station that consists of three basements, including two waiting rooms and one platform (Fig. 1(a)). To identify the dynamics of the PM10 (particle matter less than 10 ␮m diameter) concentration on the platform, the PM10 concentration on the platform, the outdoor PM10 concentration, the subway schedule and the ventilation inverter frequency are measured as shown in Fig. 2. The PM10 concentration on the platform (Fig. 2(a)) is measured by a tele-monitoring system (TMS, Fig. 1(c)), and the PM10 concentration has a diurnal variation with two peaks, from 7 a.m. to 10 a.m. and from 6 p.m. to 9 p.m. The PM10 concentration at outside (outdoor PM10 in Fig. 2(b)) is collected at a ventilator in Fig. 1(d) with a one-hour time interval. The concentration of outdoor PM10 is less than the PM10 concentration on the platform, and it describes an irregular variation compared to other measurements. Fig. 2(c) shows the number of passed subways (subway schedule) through the subway station, and these data were collected from the meteorological office of the Seoul Metro System (SMS). The subway schedule is operated with a fixed train schedule on weekdays, which correlates with the variation of PM10 concentration on the platform. A large number of subways pass through the D-subway station from 7 a.m. to 10 a.m. and from 6 p.m. to 9 p.m. The ventilation system at the D-subway station is operated by using two ventilation facilities in the second waiting room and on the platform (Fig. 1(a)) and the amount of ventilation volume is manipulated by

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

277

Fig. 1. An installed ventilation system at a D-subway station: (a) map of the D-subway station, (b) platform screen door on the platform, (c) tele-monitoring system (TMS) of the IAQ measurements, and (d) ventilator outside.

Fig. 2. Variations of four key measurements at the D-subway station: (a) PM10 concentration on the platform, (b) PM10 concentration outside, (c) subway schedule, and (d) ventilation.

278

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

Fig. 3. Relationship between variables of the ventilation control system in the D-subway station.

the inverter frequency (Hz) of a ventilation fan of the ventilation facility. Fig. 2(d) shows the ventilation strategy used herein at the D-subway station based on the inverter frequency; the value of inverter frequency was set at 45 Hz from 12 a.m. to 6 p.m., 60 Hz from 6 p.m. to 10 p.m., and 40 Hz from 10 p.m. to 12 a.m. Two important characteristics of the ventilation system in the D-subway station are 1) the rush hour time period, and 2) the correlation between the PM10 concentration on the platform and the subway schedule. The PM10 concentration on the platform and the number of subways that pass through the station concurrently show different dynamics from 7 a.m. to 10 a.m. and from 6 p.m. to 9 p.m. comparing with the other periods. Therefore, the operating time should be divided into two time periods (rush hour and non-rush hour time periods) to consider different dynamics of the PM10 concentration between the time periods in the ventilation system. In addition, the PM10 concentration on the platform and the subway schedule seem to have similar variations of the day. Specifically, the number of subway cars passing through the station was the main driver of the increase of PM10 concentration on the platform despite of the presence of the platform screen door (PSD) because the PMs are introduced directly from the tunnel to the platform by the piston effect [20]. To maintain a healthy level of PM10 on the platform, the ventilation system should be operated at a high inverter frequency during the two rush hour periods however, the current ventilation strategy does not do that. Hence, in the following sections, we describe the gain scheduling-based ventilation control system that is implemented at a D-subway station to achieve a healthy level of PM10 and save energy used in ventilation. 2.2. Development of a ventilation control system for periodic IAQ variation 2.2.1. Identification of an IAQ ventilation system in the subway station Process identification of the dynamics of PM10 concentration is an essential step in obtaining the characteristics of the target system. A mathematical expression is used to design the PID and feed-forward (FF) controllers. A first-order plus time-delay (FOPTD) process model is selected because it captures the dynamics of a PM10 -ventilation system, is easily implementable, and suitable to describe the kinetics of the target system. The FOPTD model is described by the transfer function defined by Eq. (1) [21,22]. G(s) =

y(s) k exp(−s) = s + 1 u(s)

(1)

Here, y(s) and u(s) are the output and input variables of the system, and k, , and  are the gain, the time coefficient, and the time delay of the system, respectively. Fig. 3 shows a relationship between the four variables of the ventilation control system at the D-subway station: two disturbances of the subway schedule and the outside PM10 , one input of inverter frequency, and one output of PM10 concentration on the platform. The PM10 concentration on the platform is controlled by manipulating the inverter frequency,

Fig. 4. Variation of the process input and output variables of the first order plus the time delay model with a step response [22].

where an increase in inverter frequency results in an increase in the amount of filtered outdoor air fed into the platform through the ventilation system. As a result, the PM10 concentration on the platform decreases with the increased filtered fresh air, which dilutes the PM10 concentration on the platform. The outdoor PM10 and the subway schedule are two disturbance variables that also affect the PM10 concentration on the platform. Indeed, unexpected inflow of PMs from outdoor air by movement of passengers and PMs generated by the braking system of the subway lead to a rise in platform PM10 [23–25]. The parameter for the FOPTD model was easily identified according to a step response of the system. Fig. 4 shows the response of an output variable of the system when a value of the input variable is instantly changed from zero to one at time zero (i.e., step input). The input value is maintained at u∞ , the value of the output variable y(t) starts to come up to y∞ after a time delay, then y(t) converges to y∞ . The process gain k was obtained by using y∞ /u∞ and the time delay  was defined as a time from 0 to a point of contact between the x-axis and the tangent line at the inflection point of the y(t) curve. The time constant  represents the time difference between two points: where the x-axis intersects with the tangent line at the inflection point of the y(t) curve, and where y∞ intersects with the tangent line at the inflection point of y(t). To keep the PM10 concentration on the platform at a heathy level, the ventilation system should be controlled to reject effect of disturbances. For the ventilation of the D-subway station, this study implemented a combined control system consisting of one feedback controller with PID and two FF controllers, which manipulate inverter frequency for controlling platform PM10 and rejecting disturbance effects from outdoor PM10 and the subway schedule.

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

279

Table 1 Specification of the ZN, IMC, and ITAE-1 tuning rules for the PID controller [22]. Controller

Tuning parameters

ZN IMC ITAE-1 (Set-point change) ITAE-1 (Disturbance rejection) *

kc

i

d

ku /1.7 (2 + )/2(* + )/k (0.965(␪/␶)−0.850 )/k (1.357 (/)−0.947 )/k

pu /2.0 /(0.796 − 0.1465(␪/)) ␶/(0.796-0.1465(␪/␶)) /(0.842(␪/)−0.738 )

pu /8.0 (0.308(␪/)0.929) (0.308(␪/␶)0.929 )␶ (0.381 (/)0.995 )

 ≥ 1.7.

Fig. 5. Block diagram of the proposed ventilation control system for the D-subway station.

2.2.2. Proportional-integral-derivative (PID) control The PID controller is widely used to control a system’s output value in various industries due to its simple structure, robustness, easy implementation, and good control performance. The PID controller consists of proportional, proportional-integral, and proportional derivative parts [22]:

⎛

u(t) = kc ⎝(ys (t) − y(t)) +

1 i

⎞

t (ys () − y())d + D

d(ys (t) − y(t)) ⎠ dt

(2)

0

where ys , y(t), and u(t) are set-point of one controlled variable, another controlled variable, and a manipulate variable of the system at time t, respectively. kc ,  i , and  d are parameters of the PID controller which correspond to proportional gain, an integral time constant, and a derivative time constant. These parameters should be determined according to the dynamics of the system to guarantee good control performances. In this study, the PID controller was implemented to control the PM10 concentration on the platform by manipulating the ventilation inverter frequency. To improve control performance of the PID controller, the values of the parameter in the PID controller should be tuned in accordance with the characteristics of the system. Three tuning rules of ZN, IMC, and ITAE-1 were applied to tune the controller’s parameters among the tuning rules for the PID controller, and their performances were compared (Table 1). 2.2.3. Feedforward control for disturbance rejection When the system is open to several disturbances, feedback control has two fundamental disadvantages: 1) no corrective action until after a deviation in the controlled output occurs, and 2) no predictive control action to compensate for the effects of known measurable disturbances. Therefore, an FF controller was used to reject an effect of a disturbance variable on a controlled variable.

The concept of feedforward control is to take corrective control action using a measurable disturbance and knowledge about system dynamics before upsetting a process [26]. In the mathematical expression, the disturbance (d) affects a process output (y) through the transfer function of the disturbance (Gd ). To reject an effect of the disturbance, the feedforward controller is designed as shown in Eq. (3): Gf (s) =

G (s) u (s) =− d Gp (s) d (s)

(3)

where Gf (s) is the transfer function of the feedforward controller, u (s) is the control action, d (s) is the disturbance signal, and Gp (s) and Gd (s) are the process models for the manipulated and disturbance variables, respectively. Through the feedforward controller, appropriate input (u) is generated and the disturbance effect on the process output (yd ) is zero. Fig. 5 shows a block diagram of the proposed ventilation control system, which consists of one feedback and two feedforward controllers. The objective feedback controller (FB controller) controls the PM10 concentration on the platform by manipulating the ventilation inverter frequency in order to achieve a concentration dictated by the set point. In addition, two feedforward controllers are used to control disturbance effects of the subway schedule and the outdoor PM10 by handling the ventilation inverter frequency [23–25]. Finally, the estimated ventilation inverter frequency from the ventilation control system was integrated with the ventilation system to maintain a healthy level of IAQ. 2.3. Gain scheduling The manual ventilation system at the D-subway station handled ventilation air volume by manipulating the ventilation inverter frequency, and the ventilation inverter frequency was scheduled at

280

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

2.4. Proposed gain-scheduling ventilation control with periodic IAQ dynamic model

Fig. 6. The hourly average values of PM10 concentration on the platform during weekdays.

45 Hz from 12 a.m. to 6 p.m., 60 Hz from 6 p.m. to 10 p.m., and 40 Hz from 10 p.m. to 12 a.m. for one day (in Fig. 2(d)). This means that the manual ventilation system prevented deterioration of IAQ on the platform by operating a high ventilation inverter frequency (60 Hz) from 6 p.m. to 10 p.m. (evening rush hour). During the other periods (non-rush hour), ventilation energy was saved by operating a lower inverter frequency (40–45 Hz). However, the PM10 concentration on the platform at the D-subway station actually showed a diurnal pattern with two peaks for the day (as shown in Fig. 6): from 7 a.m. to 10 a.m. and from 6 p.m. to 9 p.m. Based on the peaks of platform PM10 concentration, it is presumed that there is another rush hour from 7 a.m. to 10 a.m. (morning rush hour) with different dynamics than the non-rush hour periods. Therefore, gain scheduling is necessary to capture the varying dynamics of PM10 concentration on the platform according to the time. kc = kc1 ,

i = i1 ,

d = d1

for

7 ≤ t < 10 (morning rush hour)

kc = kc2 ,

i = i2 ,

d = d2

for

18 ≤ t < 21 (evening rush hour)

kc = kc3 ,

i = i3 ,

d = d3

for

0 ≤ t < 7,

In order to maintain a healthy IAQ under varying dynamics of PM10 concentration on the platform according to time, gain-scheduled ventilation control was applied to the D-subway station. Gain scheduling is used to tune the parameters of the controller by incorporating a variation in the system dynamics according to the operation regions. For example, the varying system dynamics is described by the following nonlinear model as Eq. (4): 

dy(t) + y(t) = ku(t − ) dt

A schematic diagram of the proposed method that incorporates an IAQ ventilation control system considering time-varying dynamics of IAQ is shown in Fig. 7. The proposed method is divided into three parts: 1) identification of the IAQ process in the subway station, 2) development of the gain-scheduled ventilation control system, and 3) evaluation of its control performance and comparison with the controls. The ventilation control system generates control signals to the ventilation inverter frequency to control the air pollutants inside the platform, where the inverter is an electrical device that regulates revolution speed of the ventilation fan motor. At the D-subway station, IAQ is controlled by ventilation inverter frequency which indicates ventilation air volume, where the number of passed subway cars and OAQ chiefly reduce IAQ. Therefore, the ventilation inverter frequency is used as a manipulated variable, the outside PM10 concentration and the number of passed subway cars are used as disturbance variables, and the platform PM10 concentration is used as a controlled variable. To control the PM10 concentration on the platform, an identification of dynamics of the PM10 concentration on the platform and good control system play an important role. In the second part of the proposed method, a gain-scheduled ventilation control system is necessary for capturing the timevarying PM10 dynamics (Fig. 8). Here, time periods that represent the different IAQ dynamics over a 24-h period should be defined. The 24 h are divided into three time periods: 1) morning rush hour (from 7 a.m. to 10 a.m.), 2) evening rush hour (from 6 p.m. to 10 p.m.), and 3) non-rush hour (the other times) based on the variation of the platform PM10 concentration. Then, a feedback control-based gain-scheduled ventilation control system is designed with three sets of tuned parameter values—ZN, IMC, and ITAE-1 tuning rules—for each time period in order to generate appropriate ventilation control action under the varying IAQ dynamics as in Eq. (5).

(4)

where k = k0 + k1 y(t),  =  0 +  1 u(t), and  are the nonlinear process coefficients of process gain, time constant, and time delay. Eq. (4) is a nonlinear system that has a nonlinear static gain of k = k0 + k1 y(t) consisting of output function and nonlinear gain. When output of the process goes over from one operating point to another, the behavior of the process may change, resulting in different dynamic behavior [22]. Because the system dynamics of k, , and  change according to y(t), which varies with operation region or time (rush/non-rush hour), the system needs to be controlled by tuning k,  d , and  i (parameters of the controller) to compensate for the nonlinearity of the IAQ ventilation dynamics.

10 ≤ t < 18,

(5)

21 ≤ t < 24 (non-rush hour)

In addition, the feed-forward ventilation control signal is added to reject the disturbance effects due to outside PM10 concentration and the number of passing subway cars. Finally, IAQ control performance of the gain-scheduled ventilation control system is evaluated using two indices: the average PM10 concentration on the platform and the energy consumption of the ventilation system. The PM10 concentration is a key factor in the evaluation of ventilation systems because the PM10 in the air is related to various diseases such as respiratory disease, cardiovascular disease, and allergies [27]; accordingly, the average value of PM10 concentration on the platform is selected as the first performance index. The energy demand of the ventilation system is set as the second performance index because the ventilation system powers facilities in the subway station that consume significant energy, such as blowers and fans [28–30]. The energy consumption of the ventilation system (kWh) according to the ventilation inverter frequency (Hz) is estimated by the 3rd order polynomial model shown in Eq. (6). Energy consumption (kWh) = 0.0007 × Hz3 − 0.046 × Hz2 +2.01 × Hz + 8.8

(6)

To verify the control performance of the proposed method, the gain-scheduled ventilation control system is compared with the

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

281

Fig. 7. Schematic of the proposed method of gain-scheduled ventilation control system for controlling the IAQ in the subway station.

manual control and the ventilation control system (without gain scheduling) in terms of the control performance indices. 3. Results and discussions 3.1. Identification of the IAQ dynamics for gain scheduling This study is carried out in an underground D-subway station as part of the Seoul Metro, Korea. Properties of the ventilation system (e.g., the number of ventilation unit, ventilation capacity) are represented in Table 2. The dynamics of varying PM10 concen-

Table 2 Properties of the IAQ ventilation system at the D-subway station. Property

Values

Average platform PM10 conc. (␮g/m3 ) The number of ventilation units Capacity (m3 /h) Static pressure (mmAg) Range of inverter frequency (Hz)

64.0 2 60,000 105 0–60

282

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

Fig. 8. Conceptual diagram of the gain scheduled ventilation control system based on the knowledge of rush hour and non-rush hour time.

Table 3 Tuning parameters of the PID controller with gain scheduling based on ZN, IMC, and ITAE-1 rules. Tuning parameters Proportional gain KP (= kc )

Integral gain KI (= kc /␶i )

Derivative gain KD (= kc ␶d )

ZN

Morning Ordinary Evening

3.30 × 105 −0.35 13.12

2.06 × 108 −4.73 107.91

135.96 −6.65 × 10−3 0.40

IMC

Morning Ordinary Evening

1.33 × 105 −0.14 −5.29

4.63 × 105 −3.09 −5.86

54.80 −1.26 × 10−3 −0.16

ITAE-1

Morning Ordinary Evening

3.57 × 105 −0.19 −0.16

7.14 × 107 −3.58 −3.29

0 −4.46 × 10−3 −3.86 × 10−3

trations are identified with three FOPTD models (Eqs. (7)–(9)) in order to determine the relationship between inverter frequency and PM10 concentration on the platform during the three time periods, and two models (Eqs. (10) and (11)) are used to account for the disturbance effects on PM10 concentration on the platform from the subway schedule and the outdoor PM10 . As shown in Eq. (7), the process model for the relationship between inverter frequency and PM10 concentration on the platform for non-rush hours is the FOPTD model where process gain, the time coefficient, and the time delay are assigned as −1.92, 0.013, and 0.0625, respectively. Gu =

−1.92 exp(−0.0625s) 0.013s + 1

(7)

Eq. (7) means that the PM10 concentration on the platform is decreased by 1.92 times for every increasing inverter frequency value and the PM10 concentration on the platform takes 0.0625 days (90 min) before starting to change due to the inverter frequency. Also, the PM10 concentration on the platform gradually changes and it takes 0.013 days (18.72 min) to reach 63% of the total variation in PM10 concentration on the platform. For gain scheduling of the PID controller to control the concentration of platform PM10 by manipulating the ventilation inverter frequency (Hz) in the ventilation system, the relationship between PM10 concentration on the platform and inverter frequency are identified by predefined operating time periods: morning and evening rush hours. Gp,morning

0.000967 exp(−0.000825s) (s) = 0.286s + 1

Gp,evening (s) =

− 1.011 exp(−0.0625s) 0.872s + 1

(8) (9)

Compared to the non-rush hour time period, the absolute value of process gain for morning rush hour (Eq. (8)) decreases and the value of the process gain changes from a positive to a negative value. It suggests that the amounts of generated and entering particulate

matter are excessively higher than the amount of decreased particulate matter by the ventilation system. The subway car frequency is highest during in the morning rush hour, and this is also when the value of PM10 concentration on the platform tends to be very high. Therefore, we can deduce that heavy use of the subway in the morning rush hour leads to the deterioration of IAQ. The time delay in the FOPTD model for the morning rush hour is reduced, but the time coefficients are increased compared with the ordinary time periods. Thus, the ventilation system instantly starts to dissipate pollutants but it takes more time to completely recover the PM10 concentration on the platform to lower levels during the morning rush hour. The absolute value of process gain for the model of PM10 concentration on the platform (Eq. (9)) during the evening rush hour is lower than that of the non-rush hour time period, where the amount of the removed pollutants in the evening rush hour is lower than during non-rush hour time periods at the same inverter frequencies. The value of time delay for the evening rush hour is the same as that of the non-rush hour time periods. Two FOPTD models for disturbance effects (subway schedule: d1 and outdoor PM10 : d2 ) are identified as follows. Gd1 (s) =

0.603 0.0618s + 1

(10)

Gd2 (s) =

3.48 exp(−0.0328s) 0.023s + 1

(11)

Note that the process gains in the two disturbance models (Eqs. (10) and (11)) are positive, which means that an increase in subway car frequency and an increase in outdoor PM10 leads to increase in PM10 concentration on the platform due to the positive value of the process gains in the two disturbance models. In addition, we know that the outdoor PM10 has more impact on the concentration of PM10 concentration on the platform than the subway schedule because the value of process gain in Eq. (11) is larger than that in Eq. (10). On the other hand, the subway schedule provides more of an instant disturbance effect on the PM10 concentration on the

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

283

platform than the outdoor PM10 because there is no time delay in the model of the subway schedule. 3.2. PID controller tuning with gain scheduling The parameter values of the PID controller for each time period were tuned in accordance with ZN, IMC, and ITAE-1 tuning rules. The tuned parameters show positive values in the morning rush hour while almost all parameters are negative for non-rush hours and the evening rush hour (Table 3). The sign of the parameter values indicates the direction of the control effort of a ventilation controller. For example, once the PM10 concentration is less than the set-point value, a controller including positive parameters generates a negative input variable to satisfy the set-point; on the contrary, a controller including negative parameters generates a positive input variable. Therefore, the control parameter sets tuned by the ZN rule for the morning and evening rush hours and tuned by IMC and ITAE-1 rules for the morning rush hour would show an opposite control effort compared with the other sets. The almost absolute values of KP in Table 3 for the rush hour time periods are higher than the value of KP for the non-rush hour time period except for the ITAE-1 tuning rule. The value of KP implies the amount of overall control efforts of the controller KP is equal to kc and the kc is included in all parts of the PID controller. The increased value of KP during rush hour means a higher inverter frequency than during non-rush hour periods when controlling the PM10 concentration on the platform. Therefore, it reflects that the inverter frequency in the ventilation system is significantly changed due to a small difference between PM10 concentration on the platform and the set-point of the PID controller using the tuned parameters. The absolute value of tuned KI for rush hours are bigger than that of the non-rush hour time period (Table 3). KI tends to generates a large amount of control action for quickly satisfying value of control variable to the value of set-point of the controller. If the absolute value of KI is quite high, it can lead to poor ventilation control performance of the PM10 concentration on the platform due to the fluctuating inverter frequency. Therefore, KI tuned by IMC rule might lead to a more stable result than the other tuning rules. The values of KD tuned by the ITAE-1 rules converge to zero (Table 3). However, the ZN and IMC rules lead to a larger value of KD for the morning and evening rush hours than that of non-rush hour time period. Because a large value of KD reduces the settling time and improves the stability of a process, PID controllers tuned by ZN and IMC show more stable control performance in the morning rush hour than the other two time periods. According to the tuning rules, the KD value tuned by the ITAE-1 rule shows less stable control than the other tuning rules. 3.3. A gain-scheduled ventilation control system In the D-subway station, a manual ventilation system operates to maintain the PM10 concentration on the platform at a healthy IAQ, while it uses a lot of energy. In manual control, for each day, the fixed inverter frequency of the manual ventilation system is 45 Hz from 12 a.m. to 5 p.m., 60 Hz from 5 p.m. to 9 p.m. (i.e., rush hour), and 40 Hz from 9 p.m. to 12 a.m. During the rush hours, to increase the ventilation performance, the inverter frequency of the manual ventilation system is set at its maximum value, 60 Hz. On the other hand, during the non-rush hours, the inverter frequency is set at 40–45 Hz. However, the manual ventilation system cannot effectively handle the PM10 concentration on the platform due to two characteristics of the PM10 concentration on the platform: 1) the diurnal pattern, and 2) the different dynamics according to operating time periods. Therefore, a ventilation control system is implemented to maintain PM10 concentration on the platform at a healthy level by combining a PID controller and two feedforward

Fig. 9. Control performances of the manual ventilation system and the ventilation control system tuned by (a) ZN, (b) IMC, and (c) ITAE-1 (set-point PM10 = 70 ␮g/m3 on the platform).

controllers for solving two disturbance effects. Then, gain scheduling is implemented in the ventilation control system to incorporate different IAQ dynamics according to the operating time periods (namely, gain-scheduled ventilation control system). Here, the setpoint of the ventilation control system and the gain-scheduled ventilation control system is fixed at 70 ␮g/m3 , and all control systems were operated with 3 min time step.

284

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

Fig. 10. Comparison of control performances of the ventilation control system with gain-scheduled ventilation control system tuned by (a) ZN, (b) IMC, and (c) ITAE-1 for five days (set-point PM10 = 70 ␮g/m3 on the platform).

Fig. 11. Comparison of control performances of the ventilation control system with gain- scheduled ventilation control system tuned by (a) ZN, (b) IMC, and (c) ITAE-1 for typical day (set-point PM10 = 70 ␮g/m3 on the platform).

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

285

Table 4 Control performance evaluation of the three ventilation control systems in terms of average value of PM10 concentration on the platform, ventilation energy consumption, and number of exceeded points over the IAQ health limit (120 ␮g/m3 ). Tuning rules Manual

Average value of PM10 concentration on the platform (␮g/m3 )*

Ventilation energy consumption (kWh/d)

Number of points exceeding the limit (120 ␮g/m3 )

64.0

1811

Multitude

Without gain scheduling

ZN IMC ITAE-1

69.9 69.8 69.8

1780 1767 1767

0 0 0

With gain scheduling

ZN IMC ITAE-1

70.1 70.0 69.9

1745 1766 1764

0 0 0

*

Using gain scheduling at a set-point of 70 ␮g/m3 tuned by the ZN, IMC, and ITAE-1 tuning rules.

Fig. 9 shows the control performances of the manual ventilation system and the ventilation control system (FB + 2FFs) with three tuning rules. Compared to the manual ventilation system, the ventilation control system show smaller variations in PM10 concentration on the platform (Fig. 9). The ventilation control system maintains a PM10 concentration under the control limit of 120 ␮g/m3 during the whole ventilation period. In addition, the diurnal pattern PM10 concentration on the platform is significantly reduced when the inverter frequency is varied according to operating time. During the morning rush hour (7 a.m.–10 a.m.), the ventilation control system uses higher inverter frequencies than that of the fixed inverter frequency of the manual ventilation system to prevent an increase of PM10 concentration with the increased number of subway cars and passengers. During the evening rush hours, the ventilation control system manages the inverter frequency at equal or lower values compared to the manual ventilation system. This means that the ventilation control system could reduce the energy demand of the ventilation system while maintaining a healthy PM10 concentration. From 12 a.m. to 6 a.m. (dawn), the values of the controlled inverter frequency are very low compared to those of the manual ventilation system. The ventilation control system saves energy rather than improves IAQ because the disturbance effects of the subway schedule and the outdoor PM10 during the dawn hours are much less than the other operating times. Figs. 10 and 11 compare the control performances of the ventilation control system and gain-scheduled ventilation control system for five days and a typical day, where the gray indicates morning and evening rush hours. The gain-scheduled ventilation control system tuned by ZN and IMC leads to a significant reduction in variations of PM10 concentration on the platform and inverter frequency (Fig. 10(a) and (b)). Comparing with the ventilation control system, the gain-scheduled ventilation control system with ZN turning rule represents lower values of the inverter frequency in both morning and evening rush hours, and the gain-scheduled ventilation control system with IMC tuning rule uses lower inverter frequency only in morning rush hour while yielding the similar PM10 concentration on the platform (Fig. 11(a) and (b)). On the other hand, the gain-scheduled ventilation control system with ITAE-1 shows similar values of inverter frequency to that of the ventilation control system. This means that the excessive inverter frequency caused by the PID controller in rush hours is reduced by applying the gain scheduling method with ZN and IMC turning rules. In the non-rush hour time period, the PM10 concentration on the platform and inverter frequency of the gain-scheduled ventilation control system with ZN tuning rules traced an opposite pattern to that of the ventilation control system. The opposite pattern is caused by the different dynamics of the PM10 concentration on the platform according to the gain-scheduled ventilation control system with ZN and IMC tuning rules. However, the gain-scheduled ventilation control system tuned by ITAE-1 does not lead to a differ-

ence in PM10 concentration on the platform and inverter frequency compared with the ventilation control system (Figs. 10 (c) and 11 (c)). Table 4 shows control performances of the manual ventilation system, the ventilation control system, and the gain-scheduled ventilation control system with three tuning rules. Compared to the manual ventilation system and the ventilation control system (without gain scheduling), the gain-scheduled ventilation control system saves more energy than the ventilation control system (Table 4). Note that the gain-scheduled ventilation control system with ZN tuning shows the best performance in regard to energy consumption with similar PM10 concentration on the platform. According to the identified ventilation model (Eq. (8)), it is hard to prevent an increase in PM10 concentration on the platform at rush hour using ventilation control system that used a fixed parameter values of PID controller due to a large time delay. In the D-subway station, the FF controller is the most effective at reducing disturbance effects of the subway schedule in regard to PM10 concentration on the platform during the rush hours. Because the number of passed subways depending on the number of passengers at the subway station can account for the varied PM10 concentration on the platform. By implementing the gain-scheduled ventilation control system with the ZN rule, the excessive inverter frequency recommended from the PID controller was reduced, allowing less energy consumption of the ventilation system. The gain-scheduled ventilation control system with the ZN rule could reduce 66 kWh/d of total energy demand of the ventilation system. In South Korea, there are 20 lines and 577 subway stations in metropolitan area including Seoul. If the gain-scheduled ventilation control system is applied into all subway stations, 38,082 kWh/d of energy can be saved (i.e., 13,899,930 kWh/year) and it is equal to =/year) 991,082 US$/year of economic benefit (i.e., 1,111,994,400 = W =/kWh) W based on the energy charge for multiple-use facilities (80 = and the present exchange rate from Korean won to US dollar (1 =). In the greenhouse gas (GHG) emission aspect, the US$ = 1122 = W gain-scheduled ventilation control system with the ZN rule could reduce 2,988,485 kgCO2 e/year based on the estimated amount of saved energy consumption (average GHG emission factor = 215 kgCO2 e/MWh [31]). Moreover, the gain-scheduled ventilation control system can be easily implemented into the subway stations by modifying or changing a software for ventilation system without additional installation of equipment. Therefore, it is concluded that the gain-scheduled ventilation control system is an economic solution, maintaining healthy level of PM10 concentration in subway station. As a result, the gain-scheduled ventilation control system with the ZN tuning rule saves about 4% and 2% of the total energy demand of the ventilation system compared to the manual ventilation system and the ventilation control system during the entire ventilation period, respectively, while maintaining a comfortable PM10 concentration on the platform.

286

S. Lee et al. / Energy and Buildings 153 (2017) 275–286

4. Conclusions In this study, a new ventilation control design is proposed by utilizing the information on the periodicity and variation of IAQ dynamics according to rush-hour. The ventilation control system is developed by combining PID and FF controllers to minimize pollutants and save energy in the subway station. According to the defined time periods (morning, evening, and non-rush hours), the IAQ process dynamics were identified. The gain scheduling control was implemented in the ventilation system dependent on the different IAQ dynamics according to rush-hour. The gain-scheduled ventilation control system provided appropriate ventilating actions at two types of rush hour zones and non-rush hour zones that have different IAQ dynamics, as a result the gain-scheduled ventilation control system could reduce 4% of its energy consumption while maintaining the PM10 concentration under 120 ␮g/m3 at platform. We expect that this type of gain-scheduled control strategy can be used at multiplex facilities such as airports, hospitals, libraries and great malls, and is able to improve public health and save energy consumption. Acknowledgements This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. 2015R1A2A2A11001120). References [1] M. Kim, B. SankaraRao, O. Kang, J. Kim, C. Yoo, Monitoring and prediction of indoor air quality (IAQ) in subway or metro systems using season dependent models, Energy Build. 46 (2012) 48–55. [2] N. De Nevers, Air Pollution Control Engineering, Waveland press, 2010. [3] P. Aarnio, T. Yli-Tuomi, A. Kousa, T. Mäkelä, A. Hirsikko, K. Hämeri, M. Räisänen, R. Hillamo, T. Koskentalo, M. Jantunen, The concentrations and composition of and exposure to fine particles (PM2.5 ) in the Helsinki subway system, Atmos. Environ. 39 (28) (2005) 5059–5066. [4] N. Kim, S. Lee, J. Jeon, J. Kim, M. Kim, Evaluation of factors to affect PM10 concentration in subway station, Proceedings of the 43rd Conference of Korean Society for Atmospheric Environment (2006) 571–573. [5] Y. Kim, M. Kim, J. Lim, J.T. Kim, C. Yoo, Predictive monitoring and diagnosis of periodic air pollution in a subway station, J. Hazard. Mater. 183 (1) (2010) 448–459. [6] Y.-S. Kim, J.T. Kim, I.-W. Kim, J.-C. Kim, C. Yoo, Multivariate monitoring and local interpretation of indoor air quality in Seoul’s metro system, Environ. Eng. Sci. 27 (9) (2010) 721–731. [7] J.Y. Lee, D.A. Lane, J.B. Heo, S.-M. Yi, Y.P. Kim, Quantification and seasonal pattern of atmospheric reaction products of gas phase PAHs in PM2.5 , Atmos. Environ. 55 (2012) 17–25. [8] M. Itani, K. Ghali, N. Ghaddar, Increasing energy efficiency of displacement ventilation integrated with an evaporative-cooled ceiling for operation in hot humid climate, Energy Build. 105 (2015) 26–36. [9] M. El Mankibi, N. Stathopoulos, N. Rezaï, A. Zoubir, Optimization of an air-PCM heat exchanger and elaboration of peak power reduction strategies, Energy Build. 106 (2015) 74–86.

[10] J.E. Son, K.H. Lee, Cooling energy performance analysis depending on the economizer cycle control methods in an office building, Energy Build. 120 (2016) 45–57. [11] M. Gruber, A. Trüschel, J.-O. Dalenbäck, Alternative strategies for supply air temperature control in office buildings, Energy Build. 82 (2014) 406–415. [12] W. Cho, D. Song, S. Hwang, S. Yun, Energy-efficient ventilation with air-cleaning mode and demand control in a multi-residential building, Energy Build. 90 (2015) 6–14. [13] D. Pan, S. Deng, M. Chan, Optimization on the performances of a novel bed-based task/ambient conditioning (TAC) system, Energy Build. 144 (2017) 181–190. [14] A. Wemhoff, Calibration of HVAC equipment PID coefficients for energy conservation, Energy Build. 45 (2012) 60–66. [15] F. Mofidi, H. Akbari, Integrated optimization of energy costs and occupants’ productivity in commercial buildings, Energy Build. 129 (2016) 247–260. [16] F. Mofidi, H. Akbari, Personalized energy costs and productivity optimization in offices, Energy Build. 143 (2017) 173–190. [17] J. Mei, X. Xia, Energy-efficient predictive control of indoor thermal comfort and air quality in a direct expansion air conditioning system, Appl. Energy 195 (2017) 439–452. [18] F. Wang, Q. Feng, Z. Chen, Q. Zhao, Z. Cheng, J. Zou, Y. Zhang, J. Mai, Y. Li, H. Reeve, Predictive control of indoor environment using occupant number detected by video data and CO2 concentration, Energy Build. 145 (2017) 155–162. [19] O. Kang, Development of an Integrated System for IAQ Monitoring Control of Air Pollutants, and Energy Optimization in an Underground Station, Kyung Hee University, 2013. [20] M. Kim, R.D. Braatz, J.T. Kim, C. Yoo, Indoor air quality control for improving passenger health in subway platforms using an outdoor air quality dependent ventilation system, Build. Environ. 92 (2015) 407–417. [21] J.-H. Cho, S. Sung, I.-B. Lee, Cascade control strategy for external carbon dosage in predenitrifying process, Water Sci. Technol. 45 (4–5) (2002) 53–60. [22] S.W. Sung, J. Lee, I.-B. Lee, Process Identification and PID Control, John Wiley & Sons, 2009. [23] O. Kang, H. Liu, M. Kim, J.T. Kim, K.L. Wasewar, C. Yoo, Periodic local multi-way analysis and monitoring of indoor air quality in a subway system considering the weekly effect, Indoor Built Environ. 22 (1) (2013) 77–93. [24] S. Lee, H. Liu, M. Kim, J.T. Kim, C. Yoo, Online monitoring and interpretation of periodic diurnal and seasonal variations of indoor air pollutants in a subway station using parallel factor analysis (PARAFAC), Energy Build. 68 (2014) 87–98. [25] M.J. Kim, R.D. Braatz, J.T. Kim, C.K. Yoo, Economical control of indoor air quality in underground metro station using an iterative dynamic programming-based ventilation system, Indoor Built Environ. 25 (6) (2016) 949–961. [26] D.E. Seborg, D.A. Mellichamp, T.F. Edgar, F.J. Doyle III, Process Dynamics and Control, John Wiley & Sons, 2010. [27] H.-J. Oh, I.-S. Nam, H. Yun, J. Kim, J. Yang, J.-R. Sohn, Characterization of indoor air quality and efficiency of air purifier in childcare centers Korea, Build. Environ. 82 (2014) 203–214. [28] A. Merzkirch, S. Maas, F. Scholzen, D. Waldmann, A semi-centralized, valveless and demand controlled ventilation system in comparison to other concepts in field tests, Build. Environ. 93 (2015) 21–26. [29] S.M. Dutton, W.J. Fisk, Energy and indoor air quality implications of alternative minimum ventilation rates in California offices, Build. Environ. 82 (2014) 121–127. [30] L.C. Ng, A.K. Persily, S.J. Emmerich, IAQ and energy impacts of ventilation strategies and building envelope airtightness in a big box retail building, Build. Environ. 92 (2015) 627–634. [31] B. Howard, M. Waite, V. Modi, G.H.G. Current near-term, emissions factors from electricity production for New York State and New York City, Appl. Energy 187 (2017) 255–271.