Demand-Based Control of Hvac System for Indoor Air Quality and Thermal Comfort

DEMAND-BASED CONTROL OF HVAC SYSTEM FOR INDOOR AIR QUALITY AND THERMAL COMFORT

Prakash Rathod, Roop Pahuja Prakash Rathod, Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India. Dr. Roop Pahuja, Associate Professor, Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India.

Abstract—People spend most of our time in indoors. In indoors the health of the occupant is mostly affected by the indoor air quality (IAQ) and thermal condition. IAQ can be affected by microbial contaminants and gaseous pollutants. Since generation of carbon dioxide (CO2) often increases its concentrations to uncomfortable level, an occupant inside room affects mostly CO2 concentration level out of other IAQ parameters. Changes in the thermal condition inside room is mostly due to seasonal changes, however thermal comfort inside room can be achieved by desired level of temperature and relative humidity.Usually for controlling indoor air quality (IAQ) and thermal comfort inside buildings a costly heating, ventilation and air conditioning (HVAC) system is used. To achieve these comfort conditions inside single room it is not feasible to install this costly HVAC system, although to partially achieve these comfort conditions different components operating independently were used but individual operation ofthese components can’t achieve desired comfort conditions.In this paper,PC based automatic virtual instrumentation system was designed to integrate working of individual components to achieve the comfort conditions inside a room. The three indoor parameters (i.e. CO2 concentration, temperature and relative humidity) were monitored in a room online using sensors and data acquisition device and controlled through the application software implemented on LabVIEW platform. A fuzzy controller wasdesigned and implemented to control CO2 concentration and temperature and relative humidity was controlled by ON-OFF controller. Control signals were issued to vary the exhaust fan ventilation rate, heating or cooling power of the heating or cooling system installed in the room to provide desired comfort conditions along with saving of electrical energy.

Index Terms—fuzzy logic control, HVAC system, indoor air quality, LabVIEW, thermal comfort

I. INTRODUCTION Most of the people spend their time in indoors. In indoors health of the occupants is mostly affected by IAQ and thermal conditions. The indoor parameters which affects the quality of air includes CO2 concentration, co concentration, volatile organic compounds, smoke, dust, mold, fungus and thermal condition is affected by the temperature and relative humidity [1].People consume oxygen and generate CO2, the generation ofthe CO2 concentration in indoors increases to uncomfortable level, therefore an occupant in indoor mostly affects the CO2concentration level out of other IAQ parameters. Hence CO2concentration level can be used to indicate the quality of air. And thermal condition can be achieved by controlling the temperature and relative humidity.The uncomfortable level of indoor parameters causes some health problems to occupants working in indoors. Such as headache, eye burning, irritation in nose, fatigue, allergy, it cal also affect the heart and lungs and cause serious adverse health effect. Therefore, the health of the occupants in indoors can be prevented by maintaining the indoor parameters within their desired limits. HVAC (heating, ventilation and air conditioning) system is an automated system which provides the occupants working inside the buildings with conditioned air so that they will have safe and comfortable work environment. The purpose of the HVAC system is to control the IAQ and thermal comfort. The ventilation system maintains the acceptable IAQ while heating and cooling systems maintains the thermal comfort. The indoor parameters information (i.e. measurement of CO2 concentration, Temperature and relative humidity) can be used as input for controlling the HVAC system. There are two approaches available for controlling HVAC system. In the first approach, the fix amount of ventilation, heating and cooling is provided by making the HVAC system either ON or OFF. In the second approach, demand based ventilation, heating and cooling is provided by varying the power deliver to the HVAC system. The first approach is the easiest one but if we apply this approach then there is wastage of unnecessary electrical energy. The second approach is the complex one but by applying this approach the unnecessary electrical energy can be saved. Various control algorithms have been implemented in the HVAC literature. Mitsios [2] dealt with natural ventilation and daylight penetration. Jiaming li [3] has carried out work on the development and validation of a control algorithm that adapts to the dynamics of a HVAC system using sensor-based demand-controlled ventilation.Pitchiah R [4] has investigated the parameters affecting the IAQ.Tarun Kumar Das [5] has carried out work on the designing of a room temperature and humidity controller using fuzzy logic. Bo Wahlberg [6] in this work, the dynamical systems for CO2 concentration, temperature and humidity were modeled,identified and validated in the study of HVAC system. Motivated by the work [3,4], in this paper, PC based automatic virtual instrumentation system was designed to integrate working of individual components to achieve the comfort conditions inside a room.

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II. BLOCK DIAGRAM OF PROPOSED IAQ AND THERMAL COMFORT CONTROLLING SYSTEM Figure 1 shows the block diagram of proposed controlling system. The indoor CO2 concentration, temperature and relative humidity is monitored and controlled.The block diagram is divided into different parts. The description of each block and their corresponding hardware is given bellow.

Fig. 1. Block diagram of proposed IAQ and thermal comfort controlling system.

A. IAQ and Thermal Comfort sensors IAQ and thermal comfort sensors (i.e. CO2 gas, temperature and relative humidity) are used to measure the three indoor parameters (i.e. CO2 concentration, temperature and relative humidity). Vernier CO2 gas sensor[10] is used measure CO2 concentration while LM35 temperature IC and Capacitive humidity sensor HTM1500LFare used to measured thermal comfort in a room. The indoor sensors are powered by the data acquisition device. Figure 2 have been used to measure three indoor parameters.

Fig. 2. IAQ and thermal comfort sensors. B. Data Acquisition Device The measured signals are acquired by the data acquisition device. ATmega328 based arduino uno, a microcontroller board is used as data acquisition device. The arduino uno is powered by the PC through USB cable. C. PC as Controller The acquired signals are monitored on front panel of virtual instrumentation (i.e. on PC) through USB cable. The measured and acquiredsignals are processed by the PC and generate the necessary control signals. D. Actuators Sub-System Actuators sub-system receives the signals issued by the controller through data acquisition device. Then, actuators sub-system generates the necessary actuating signalsrequired to operate the actuators (i.e. exhaust fan, heater, and air cooler). Actuators sub- system is consisting of power control devices (i.e. optocoupler, triac, and diac ) and relay and their driver circuit. E. Actuators Actuating signals generated by the actuators sub-system are issued to the actuators (i.e. exhaust fan, heater, and air cooler)to vary the exhaust fan ventilation rate, heating or cooling power of the heating or cooling system installed in the room to provide desired comfort conditions along with saving of electrical energy.Single phase 230 VAC operated exhaust fan is used to control CO2 concentration and relative humidity level.The exhaust fan speed varies from 0-1400 RPM.Single phase 230 VAC operated air cooler is used to control the room temperature and relative humidity. The cooling fan speed varies from 0-1200 RPM.Single phase 230 VAC operated fan heater of 2000 Watts is used to control the room temperature.Figure 4 shows the actuators used for controlling IAQ and thermal comfort.

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Fig. 4. Actuators used to control IAQ and thermal comfort..

III. FLOWCHART FOR IAQ AND THERMAL COMFORT CONTROL Theapplication software for controlling IAQ and thermal comfort was implemented on LabVIEW platform. Figure 5shows the flowchart diagram for IAQ and thermal comfort control.

Fig. 5. Flowchart for IAQ and Thermal comfort control. The measure signals of the three indoor sensors are first acquired simultaneously. These acquired signal is calibrated in respective unit of the three indoor parameters (i.e CO2 in ppm, temeperature in °C, and relative humidity in %). Then, the calibrated signal are displayed on the graph of the front panel of virtual instrumentation. These calibrated signals are processed by the controller. If there is error between measured and desired level, the controller will take some corrective action and generate necessary actuating signal. These actuating signals operate the various actuators (i.e. heater, cooler and exhaust fan). Then, these actuators maintain the comfort conditions inside a room. If there is no error means the three parameters are within their desired levels then data controller will make the actuators in their off state.Fuzzy controllers are designed to control CO2 concentration and temperature while ON-OFF controller is designed to control relative humidity.

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A. Auto/Manual parameter settings There is provision available for the user to select the desired level of the parameters through Auto/Manual mode. In Auto mode, the desired level of three parameters is automatically selected. In Manual mode, user can set the desired levels of three parameters. The figure 6 shows the flowchart for auto/manual parameter settings.

Fig. 6. Flowchart of Auto/Manual settings

B. Scaling and mapping of sensors output The sensor output which acquired in LabVIEW is in the voltage form. So it is required to convert this voltage output of sensor into their corresponding unit. Figure 7 shows the flowchart for scaling of sensor output.

Fig. 7. Flowchart of scaling of sensor output

C. Issue controller output signals to actuators Controller receives the acquired signals and processes the signals. Depending on the errors controllers generates the control signals for actuating the actuators. Figure 8 shows the flowchart for issuing controller output to actuators.

Fig. 8. Flowchart of issuing controller output to actuators

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IV. EXPERIMENTAL SETUP Figure 9 shows the complete experimental setup diagram for IAQ and thermal comfort control.

Fig. 9. Experimental setup diagram

Figure 10 shows the image of system during experiment.

Fig. 10. Image of the system during experiment

V. EXPERIMENTAL RESULTS Experiment was performed in a single room (10×8×8 ft³). The results obtained has been discussed below.

A. Virtual Instrument Front Panel The front panel is divided into two tabs namely; process data and process diagram. Figure 11shows process data tab which provides desired levels of the three indoor parameters, measured levels of the three indoor parameters, status of the room status of actuators, selection of parameter settings (i.e. manual and auto), graphs for displaying the measurement of three parameter, com port selection for interfacing arduino with LabVIEW through VISA resource, control for logging data and control for stopping the operation.

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Fig. 11. Tab showing online process data of IAQ and thermal comfort control in a room.

Figure 12 shows the process diagram tab which provides complete flow of process. It also provides the running status of the three actuators (i.e. exhaust fan, heater and cooler), online measured value of the three sensors.

Fig. 12. Tab showing online process diagram of IAQ and thermal comfort control in a room.

B. Variations of Indoor Parameters with Control Action. Three indoor parameters (i.e. CO2 concentration, temperature and relative humidity) were measured and control in a room. The experiment was performed on different days with different desired levels of the three indoor parameters. Variations of these indoor parameters are discussed below. Online relative humidity inside a room was measured through humidity sensor and recorded and displayed on graph. Figure 13 shows the variation of relative humidity with control action. As shown in figure 13,16, and 19 the measured value of relative humidity (i.e. black line) is below the setpoint of it (i.e. red line), the controller will make the cooler water pump ON and therefore relative humidity inside a room increases. Cooler water pump remains ON till measured value reaches to the setpoint of it. In the figure 13,16, and 19 setpoint of relative humidity is 55%, 50% and 65% respectively.

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Fig. 13. Variations of relative humidity in a room (day 1)

Online temperature inside a room was measured through temperature sensor and recorded and displayed on graph. Figure 14 shows the variation of temperature with control action. As shown in figure 14 the measured value of temperature (i.e. black line) is above the setpoint of it (i.e. red line), the controller will make the cooling fan ON with variable fan speed. Speed of cooling fan is reduced as error between measured value of temperature and setpoint of it reduced to zero. Depending on the error value increases or decreases, cooling fan speed increases or decreases. Therefore, temperature inside a room decreases. Cooling fan remains ON till measured value reaches to the setpoint of it. In the figure 14,17, and 20 setpoint of temperature is 24°C,23°C, and 28°C respectively.

Fig. 14. Variations of temperature in a room (day 1)

Online CO2 concentration inside a room was measured through CO2gas sensor and recorded and displayed on graph. Figure 15 shows the variation of CO2 concentration with control action. As shown in figure 15 the measured value of CO2 concentration (i.e. black line) is above the setpoint of it (i.e. red line), the controller will make the exhaust fan ON with variable fan speed. Speed of exhaust fan is reduced as error between measured value of temperature and setpoint of it reduced to zero. Depending on the error value increases or decreases, exhaust fan speed increases or decreases.Therefore,CO2 concentration inside a room decreases. Exhaust fan remains ON till measured value reaches to the setpoint of it. In the figure 15,18, and 21 setpoint of CO2 concentration is 1005 ppm, 904 ppm, and 1003 ppm respectively.

Fig. 15. Variations of CO2 concentration in a room (day 1)

Fig. 16. Variations of relative humidity in a room (day 2)

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Fig. 17. Variations of temperature in a room (day 2)

Fig. 18. Variations of CO2 concentration in a room (day 2)

Fig. 19. Variations of relative humidity in a room (day 3)

Fig. 20. Variations of temperature in a room (day 3)

Fig. 21. Variations of CO2 concentration in a room (day 3)

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VI. CONCLUSION PC based automatic virtual instrumentation system has been designed to integrate working of individual components to achieve the comfort conditions inside a room. The three indoor parameters (i.e. CO2 concentration, temperature and relative humidity) were monitored in a room online using sensors and data acquisition device and controlled through the application software implemented on LabVIEW platform.A fuzzy controller was designed and implemented to control CO2 concentration and temperature while ON-OFF controller was designed to control relative humidity and issued signals to vary the exhaust fan ventilation rate, heating or cooling power of the heating or cooling system installed in the room to provide desired comfort conditions along with saving of electrical energy.A non-mathematical model based fuzzy controller provides better control action than on-off controller. Experimental results ensure better performance of proposed algorithm.

VII. FUTURE SCOPE This work has only explored to a single room. There are manypossible directions for future work, which includes;The Proposed approach can be applied to whole house.Other IAQ parameters can also be monitored and controlled.Better controller can be designed for optimal solution.

REFERENCES [1] An Introduction to Indoor Air Quality (IAQ), http://www.epa.gov/iaq/ia-intro.html. [2] I. Mitsios, D. Kolokotsa, G. Stavrakakis, K. Kalaitzakis, A. Pouliezos “Developing a control algorithm for CEN indoor environmental criteria – addressing air quality, thermal comfort and lighting” 17th Mediterranean Conference on Control & Automation Makedonia Palace, Thessaloniki, Greece June 24 - 26, 2009, pp 976-981. [3] Jiaming Li, Josh Wall, and Glenn Platt “Indoor Air Quality Control of HVAC System” Proceedings of the 2010 International Conference on Modelling, Identification and Control, Okayama, Japan, July17-19,2010, pp 756-761. [4] Sayantani Bhattacharya, Sridevi S, Pitchiah R “Indoor Air Quality Monitoring using Wireless Sensor Network” 2012 Sixth International Conference on Sensing Technology (ICST), 2012, pp 422-427. [5] Tarun Kumar Das, Yudhajit Das “Design of A Room Temperature And Humidity Controller Using Fuzzy Logic” American Journal of Engineering Research (AJER) e-ISSN : 2320-0847 p-ISSN : 2320-0936 Volume-02, Issue-11, 2013, pp-86-97. [6] Francesco Scotton, Lirong Huang, Seyed A. Ahmadi, Bo Wahlberg “Physics-based Modeling and Identification for HVAC Systems” 2013 European Control Conference (ECC) July 17-19, 2013, Zürich, Switzerland, pp 1404-1409. [7] Warren BF,HarperNC.Demandcontrolledventilationby roomcarbondioxideconcentration: a comparisonsimulated energysavings in an auditorium space. Energy Building,1991;17:87-96. [8] ASHRAE: ASHRAE Standard 62-1999. Ventilation foracceptableindoor air quality, 1999. [9] Vernier CO2 gas sensor user manual, Revision 05/03/2005. [10] LabVIEW PID and Fuzzy logic toolkit user manual, June 2009.

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