Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller

Engineering in Agriculture, Environment and Food xxx (2015) 1e16

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Research paper

Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller Roop Pahuja a, *, Harish Kumar Verma b, Moin Uddin c a

Department of Instrumentation and Control Engineering, National Institute of Technology, Jalandhar, 144011, Punjab, India Department of Electrical and Electronics Engineering, School of Engineering and Technology, Sharda University, Greater Noida, 201306, UP, India c Faculty of Management and IT, Jamia Hamdard University, New Delhi, 110062, India b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 July 2014 Received in revised form 5 April 2015 Accepted 15 April 2015 Available online xxx

This paper discusses the implementation of greenhouse climate control simulator based upon dynamic model with intuitive user interface that provides various options to simulate greenhouse climate under open and closed loop control conditions. The simplified greenhouse climate model based upon mass and energy balance principle, encapsulates the realistic situations in a greenhouse to simulate and analyze the effect of different control strategies (shading, ventilation, cooling or heating) on inside climate for various greenhouse design configurations and under different weather conditions. The model is integrated with the proposed greenhouse-crop vapor pressure deficit (GH-crop VPD) based multi-input multi-output fuzzy climate controller that operates in feedforward mode and regulates greenhouse climate conducive for healthy plant growth and yield. Under closed loop operation, the controller automatically readjusts the operating status and rate of different climate control equipments (shade screen, roof vents, exhaust fan, cooling system and heating system) and simulates automatic control of GH-crop VPD within set limits irrespective of the variations in outside weather conditions. Designed as an educational tool, the simulator was used under open loop to demonstrate how for a selected greenhouse design and weather conditions, different climate control strategies affect greenhouse climate conditions. Multiple simulations run under different scenarios were simultaneously displayed for comparison study and analysis. Further, the closed loop simulations were performed that indicated high performance of the controller in regulating GH-crop VPD under different hot weather conditions and depicted what systems (rate and operating status of different climate control equipments) would be needed to meet desired conditions. © 2015 Published by Elsevier B.V. on behalf of Asian Agricultural and Biological Engineering Association.

Keywords: Energy and mass balance principle Fuzzy climate controller Greenhouse climate control dynamic model Greenhouse vapor pressure deficit (VPD) Greenhouse climate control simulator WSN based data acquisition system

1. Introduction Greenhouse (GH) is controlled environment agriculture, where plants are grown within a transparent/translucent (glass or plastic) closed structure under controlled environment, satisfying the requirements of the plant species, so that horticulture practices can be optimized. It works on the principle of greenhouse effect. When solar radiation falls on a greenhouse, it reflects some percentage of net radiation thus allowing the transmittance of the photosynthetic active solar radiation (PAR), in the range of 400e700 nm

* Corresponding author. Department of Instrumentation and Control Engineering, Dr B R Ambedkar National Institute of Technology, Near Bidipur, Jalandhar 144011, Punjab, India. Tel./fax: þ91 (0)181 2690301, þ91 (0)181 2690302x2908. E-mail address: [email protected] (R. Pahuja).

wavelengths. The sunlight admitted to the greenhouse is absorbed by the crops, floor and other objects, that in turn emit long wave thermal radiation in the infrared region for which the glazing material has lower transparency. As a result, the solar energy gets trapped in a greenhouse thus raising its temperature, the phenomenon known as greenhouse effect (Timmerman and Kamp, 2003; Hanan, 1998). Greenhouse cultivation is infact an advance and intensive form of agriculture. Greenhouse of different shapes, sizes, glazing materials, using different levels of climate control sophistication and employing different methods of plantation has been constructed as per grower needs and capital investment. Greenhouse-crop production is a complex process and depends upon how well the greenhouse environment is managed for conducive plant growth, health and yield. Greenhouse environment control refers to control of the variations in plant growing variables (such as temperature, humidity, light, soil moisture

http://dx.doi.org/10.1016/j.eaef.2015.04.009 1881-8366/© 2015 Published by Elsevier B.V. on behalf of Asian Agricultural and Biological Engineering Association.

Please cite this article in press as: Pahuja R, et al., Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller, Engineering in Agriculture, Environment and Food (2015), http://dx.doi.org/10.1016/j.eaef.2015.04.009

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temperature, conductivity and many more) with reference to the comfort zone conditions of plant during its growing cycle for day and night conditions. It mainly consists of three subsystems e climate control, fertigationeirrigation control, and pest management and control subsystems. Climate control subsystem caters to the control of greenhouse air temperature, relative humidity, light intensity, CO2, the important variables that directly affect the plant physiological process of photosynthesis, respiration, evapotranspiration and flow of nutrients to the plant. Fertigation-irrigation control subsystem manages adequate supply of water and nutrients to maintain healthy crop root ezone conditions. Pest management and control subsystem safeguards the crop against the growth of pest species and estimates the risk of infection to initiate preventative measures against them. Literature survey projects the use of dynamic system modeling and simulation approaches to devise greenhouse system models. One class of greenhouse climate models as summarized in Zabelite (1999), based on mass and energy balance equations, simulated the time variation of inside climate variables temperature, relative humidity with respect to weather parameters or other control equipments parameters. Another used advance system identification and neural network to model inside temperature based upon weather data and used experiment data to evaluate the model (Patil et al., 2008). Some climate models addressed specific phenomenon, for example natural ventilation (Al-Helal, 1998; Boulard et al., 1999; Dayan et al., 2004), forced ventilation (Arbel et al., 1998;. Willits, 2003), evaporative cooling (Boulard and Wang, 2000; Abdel-Ghany and Kozai, 2006) or heating systems (Kempkes et al., 2000; Bartzanas et al., 2005). More complex model incorporated crop dynamic growth models and used different plant responses as feedback signal for biological production control purposes (Challa, 1989; Kacira et al., 2005). Still another class of climate control models used simplified linear differential climate model to design and test climate controllers based on modern control strategies (Sigrimis et al., 2000; Pasgianos et al., 2003). Some climate controller models based upon newer research approaches had come up such as optimal controller to minimize resources such as energy consumption (Aaslyng et al., 2003; Korner, 2003), water consumption (Blasco et al., 2007), CO2 usage (Jones et al., 1989), economic-based optimal control (Tap, 2000) and adaptive control (Udkin ten Cate, 1983; Arvanitis et al., 2000). Also, a class of models that used computational intelligence to develop crop production management system (Chao et al., 2000; Hans-Juergen and Lange, 2003) or computer based network devices for flexible management of greenhouse (Serodio et al., 2001) had gained importance over the recent years. These nonmathematical, knowledge base models performed tasks normally done by human experts or consultants. Greenhouse climate models were also designed with an aim to develop interactive simulators to know and understand greenhouse climate dynamics and control strategies (Rodriguez et al., 2008; Tignor et al., 2007; Guzman et al., 2005). Motivated by the idea of creating a novel greenhouse climate control simulator that would allow the user to understand greenhouse climate control dynamics, control strategies and use of different climate control devices in regulating greenhouse ecrop vapor pressure deficit (GH-crop VPD), this work was carried out. GH-crop VPD is very important climate variable that controls the transpiration rate of the crop and effect its healthy growth. Control of GH-crop VPD, automatically adjusts adequate levels of relative humidity and temperature to provide appropriate climate for crop to transpire well (Prenger and Ling, 2000). The simulators discussed earlier were generally based upon one/two phenomenon, lack versatility in selecting greenhouse/crop/climate control equipment parameters to reflect realistic situations and easy user interaction

with the system. Moreover, the earlier simulators had not incorporated the concept of open and closed loop climate control methods integrated with VPD based fuzzy controller for automatic control of relative humidity and temperature in regulating GH-crop VPD to the desired limits. This paper discusses the implementation of interactive greenhouse climate control simulator that uses dynamic climate model based upon mass and energy relationship. It simulates inside climate conditions (temperature, relative humidity and VPD) of the greenhouse with respect to outside weather VPD, both under open loop and closed loop conditions, for user selectable greenhouse parameters. Open loop allows the user to select and change parameters of climate control equipments and graphically see and compare simulation results. This enables the user to easily understand the effect of using different devices under different outside weather conditions on greenhouse inside climate. In the closed loop conditions, the climate model is integrated with preloaded VPD based MIMO (multi-input multi-output) fuzzy climate controller that operates in feedforward mode of operation. Feedforward controller measures the disturbance directly and takes control action to minimize its impact on process output. To minimize the effect of outside weather disturbances that are continuously changing the greenhouse climate dynamics, feedforward control mode is preferred, unlike the conventional feedback (Timmerman and Kamp, 2003). Based upon the outside VPD, the controller programmed with unique set of control rules, simultaneously drives different climate control equipments (ventilation, heating and cooling system) at different states and rates to regulate GH-crop VPD within desired limits. This provides an insight to the requirement of specific climate control equipments under certain weather conditions for typical greenhouse to regulate GH-crop VPD. This VPD based controller has an edge over the already existing single variable temperature or relative humidity controller for greenhouse in regulating its climatic conditions (Prenger and Ling, 2000; Pahuja et al., 2013). The main goal was to implement climate control model that reflected the realistic situations of a greenhouse and integrated the same with VPD based controller into an interactive simulator. The climate model, variant of the simplified model as proposed by Rodriguez (Rodriguez et al., 2008), was designed to encapsulate the important factors that affected greenhouse inside climate conditions such as outside weather, greenhouse structure, size and glazing material, use of different climate control equipments and control methodology. The objective was not only to demonstrate the principles of greenhouse climate control operation for study and analysis purposes but also to provide an integrated platform to design, simulate and evaluate the performance of novel VPD based fuzzy controller for automatic control of GH-crop VPD, before field implementation of such a controller. The following sections discuss the system components, climate control model; design of VPD based fuzzy controller and implementation of simulator along with simulation results under open and closed loop conditions. 2. Material and methods In order to meet the goals highlighted above, generic greenhouse climate control simulator named ‘GHSim’ (greenhouse simulator) was implemented based upon dynamic greenhouse climate model integrated with VPD based climate controller. In a greenhouse, production of crop is a complex process and depends upon how well the greenhouse environment is managed for healthy crop growth and yield. Greenhouse inside climatic conditions vary dynamically and depend upon various factors such as

Please cite this article in press as: Pahuja R, et al., Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller, Engineering in Agriculture, Environment and Food (2015), http://dx.doi.org/10.1016/j.eaef.2015.04.009

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outside weather, types of actuating systems and operational rate, greenhouse structural design, size, glazing material, crop growth stage as well as type of controller and control strategies implemented etc. (Rodriguez et al., 2008). Fig. 1 shows the conceptual design schematics of greenhouse climate control model with components that define inputs to the dynamic greenhouse climate control model to simulate greenhouse climate variables as model output. The important components of the GHSim model are described below. 2.1. Greenhouse structure and equipments Type of greenhouse structure, its shape, size, glazing material and orientation directly affect the solar radiation entering the greenhouse, the main source of light and heat. This causes variation in overall climatic conditions (temperature, relative humidity, VPD) within the greenhouse and affects the physiological processes (photosynthesis, respiration and transpiration) in plant and hence its growth, yield and health. The structural parameters are important input parameters to the model and are used to estimate heat loss and gain within the greenhouse (Section 3.2). GHSim simulator (Fig. 5(a)) allows the selection of many of greenhouse structural parameters. Size of the greenhouse is specified in terms of dimensional parameters as length (m), width (m), height1 (m), height2 (m), number of spans. Shape of the greenhouse is selected in terms of different frame style (A frame, Arch roof, Quonset). It also allows the selection of commonly used glazing materials. Table 1 provides the summary of different glazing material used with associated parameters such as light transmissivity and coefficient of heat loss due to conductivity. Based upon the user defined structural parameters, model related parameters, greenhouse volume (m3), floor area (m2) and glazing area (m2) are calculated using the mathematical formula that suit the geometry of greenhouse (Timmerman and Kamp, 2003; Rodriguez et al., 2008). Originally, the condition of natural rise in greenhouse air temperature has made it possible to construct low cost greenhouses in

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cold regions to grow crop successfully. However, to exploit greenhouse cultivation in other seasons, additional climate control equipments are generally housed within the greenhouse. Depending upon the level of control sophistication used in a greenhouse, the climate equipment may be manually controlled by the grower or semi/fully automatically controlled by the dedicated/ PC based control system. Appropriate use of climate control equipments plays a vital role in affecting/regulating the greenhouse climatic conditions. The simulator (Fig. 5(a)) provides options to select different type of climate control equipments (roof vents, exhaust fan, heating system, cooling system, shade screen) suited in a greenhouse over wide weather fluctuations (summer to winter season). Also, the important parameters such as maximum rate (capacity) and operating status (load) of climate control equipments can be varied from the options available. The parameters thus selected, overall affect ventilating/cooling/heating rate of the equipments that change the greenhouse climatic conditions. Table 1 summarizes the details of the climate control equipments and their parameters used in the simulator (Timmerman and Kamp, 2003; Rodriguez et al., 2008). Ventilation is one of the common methods to provide air circulation within the greenhouse to reduce temperature. Reduced air movement and air exchange imposed by the closed glazing structure, which also trap long wave radiation within the greenhouse, result in the increase of greenhouse air temperature from the outside weather temperature. Ventilation allows comparatively cooler air from outside to replace the hot air from within to reduce inside temperature. Ventilation is provided by roof vents and exhaust fan. Roof ventilation system has motorized roof vents opening to vent greenhouse air volume. Simulator allows selection of roof vents status (OPEN/CLOSE) with any of the ventilation rate (10, 20, 30 air exchanges per hour (ACH)). Forced exhaust fan ventilation system in a greenhouse consists of number of exhaust fans mounted on the side wall of greenhouse to vent out hot greenhouse air and has higher ventilation rate as compared to roof vents. Depending upon the requirement, all or a few are in operation that varies its operating state and hence overall ventilating

Fig. 1. Conceptual design schematics of greenhouse climate control model showing components that provide inputs to the model to simulate greenhouse climate variables as model output.

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Table 1 Summary of glazing materials, climate control equipments and their parameters used in the simulator. Greenhouse glazing

Climate control equipment

Material

Light transmissivity

Coefficient of conductivity (W m2  C1)

Device

Rate/Capacity

Operating Status

Single layer glass

0.9

6.2

Roof Vents

CLOSE (0) OPEN (100%)

Single layer polyethylene

0.87

6.2

Exhaust Fan

10 (ACH) 20 30 60 (ACH) 120 240

Single layer polycarbonate

0.87

6.2

Cooling System

7 (Kg m2 h1) 15 30 70

Double layer glass

0.85

4.0

Heating System

100 (kW) 200 300 400

Double layer polyethylene

0.76

4.0

Shade Screen

60 (%) 70 80 90

Double layer polycarbonate

0.79

3.3

rate. The simulator provides the options to drive exhaust fan ventilation system with varying load\ operating status (OFF, LOW, MEDIUM, HIGH, FULL) with any of the ventilation rate (60, 120, 240 ACH). In the extreme summer conditions, ventilation alone is not possible to reduce greenhouse temperature and GH-crop VPD. Cooling system (cooling pad or fogger) with evaporative cooling method is required to humidify the greenhouse to increase relative humidity and reduce VPD. Cooling system with any of the cooing rates (7, 15, 30, 70 kg m2 h1) is selectable with different operating status (OFF, LOW, MEDIUM, HIGH, and FULL). Generally, along with evaporative cooling system, some amount of ventilation is required to avoid excessive rise of humidity in the greenhouse. In the extreme winter condition greenhouse inside temperature goes low and GH-crop VPD falls to a low value. This requires greenhouse to be dehumidified using suitable heating system combined with ventilation. There are many types and sizes of heating systems available for a greenhouse such as stream, hot water, hot air and infrared radiation based. The simulator allows the user to select heating system capacity (100, 200, 300, 400 kW) and the operating status (OFF, LOW, MEDIUM, HIGH, and FULL). Apart from using ventilation, heating or cooling systems, use of shade screen on the extreme hot summer days or cold winter nights is also recommended for greenhouse climate control. This not only improves the effectiveness of heating and cooling systems but avoids excessive rise of greenhouse temperature on hot days and heat loss on cold nights. Simulator provides the option to cover or uncover the shade screen with operating status of OPEN/ CLOSE. Also, it allows selecting different percentage of shading (60, 70, 80, and 90) for shade screen. Incorporating different control strategies by the appropriate use of different climate control equipments (rate and operating status) at different weather conditions is the key to the success of greenhouse climate control (Sigrimis et al., 2000). Apart from the greenhouse structural and climate control equipments, plants grown in the greenhouse also affect greenhouse climate. Humidity level in the greenhouse varies because of evapotranspiration process in plants. Simulator provides the option to select constant reference value of crop evapotranspiration (8.9,

OFF (0) LOW (25%) MEDIUM (50%) HIGH (75%) FULL (100%) OFF (0) LOW (25%) MEDIUM (50%) HIGH (75%) FULL (100%) OFF (0) LOW (25%) MEDIUM (50%) HIGH (75%) FULL (100%) CLOSE (0) OPEN (100%)

4.5, 0 kg m2 d1) corresponding to crop growth (large crop, small crop, no crop) (Timmerman and Kamp, 2003; Rodriguez et al., 2008). Though in literature different evapotranspiration models for different plant species are available depending upon many environmental parameters, but the idea in this paper is not to model evapotranspiration process in plants but to use a simple reference value to support this concept. 2.2. Weather data acquisition and recording using WSN Weather data is the most important input component of greenhouse climate control model as it provides the input variables to simulate greenhouse inside climate. This simulator uses greenhouse local weather data files stored in the hard disk of computing system to execute simulate scenarios. Each file of local weather database contains the recorded sampled values of outside temperature and relative humidity from greenhouse site with date/ time stamp. Outside weather data was acquired and recorded by GH-WSN (greenhouse wireless sensor network system) developed by the authors (Pahuja et al., 2013). Fig. 2 shows the schematics of WSN system deployed at the greenhouse site. A wireless sensor node, with on-board CMOS smart temperature (10 to 70  C with ±1.5  C accuracy) and relative humidity sensor (0 to 100% with ±4.5% accuracy), preprogrammed with embedded application program, was mounted outside the greenhouse to periodically acquire temperature and relative humidity variations over the day after 15 min of time interval. The node provided fully calibrated digital output in response to temperature and relative humidity. The digital data packets of the node were wirelessly transmitted to the gateway node using underlying XMesh multihop low power wireless networking protocol for sensor nodes (Crossbow Technology, 2007; Sohraby et al., 2007). The gateway node was interfaced to the host PC that forwarded the data packets to the port for application program to decipher the node packets. GH-WSN application software, running at host PC, acquired, processed and analyzed the raw data packets at each instant of time and extracted information about the outside weather data (Pahuja et al., 2013). From the measured values of outside weather temperature (Tout)

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Fig. 2. Schematics of greenhouse wireless sensor network system deployed at the greenhouse site for recording of local weather data for the simulator.

( C) and relative humidity (RHout) (%), as recorded by WSN system, outside absolute humidity AHout (gwater kg1dryair) and outside vapor pressure deficit VPDout (mB) data set were derived using static models given below. For the given outside temperature value (Tout) ( C) converted to temperature (T*out) ( R) in Rankine ( R) scale, outside saturated vapor pressure (vpsatout) in millibar (mB), is an exponential function of temperature ( R) and is estimated using Arrhenius equation (Prenger and Ling, 2000) given as:



vpsatout ¼ exp A=T*out þ B þ C:T*out þ D:ðT*out Þ2 þ E:ðT*out Þ3  þ FlnðT*out Þ *67:95 (1) where, coefficients of the equation are given as: 4

1

A ¼ 1.044  10 B ¼ 1.129  10 C ¼ 2.702  10 D ¼ 1.289  105 E ¼ 2.478  109 F ¼ 6.456.


mdryair ¼ Mdryair: ndryair: ¼ ð29Þ P: Vppar :V=R: T

(8)

where, MH20 is the molar weight of water (¼18 g/mol), Mdryair is the molar weight of dry air (¼29 g/mol), nH20 is the number of water particles, ndryair is the number of air particles, R is the universal gas constant (¼8.314472 J mol1 K1), T is the absolute outside temperature (K), P is the barometric air pressure (¼101,325 Pa) and Vppar is outside partial vapor pressure in Pascal (Pa). Vppar is calculated in terms of outside partial vapor pressure (vpparout) (mB) and is given by the following equation:

AHout ¼0:622 ðVpparÞ (2)

T^out ð FÞ ¼ ð9=5Þ:Tout ð CÞ þ 32

(3)

Based upon saturated vapor pressure and relative humidity, outside VPD (VPDout) (mB) is estimated as

(9)

.

  P  Vppar ð1000Þ

(10)

The processed weather data file containing sampled values of outside variables (temperature, absolute humidity, relative humidity and VPD) for different days were added to local greenhouse weather database and used by the simulator. Also, the average value of the outside solar radiation during the recording day was measured by handheld luxmeter and its value was stored in the local weather file.

(4)

Based upon outside relative humidity and outside saturated vapor pressure, outside partial vapor pressure (vpparout) (mB) is calculated as:

(5)

Outside absolute humidity AH*out (gwater/gdryair) (Sensirion, 2009) in terms of mass is defined as the ratio of mass of water vapor mH20 (g) per unit mass of dry air mdryair (g) and is given as:

. AH*out ¼ mH20 mdryair

(7)

Compiling and substituting above equations in Eq. (6), outside absolute humidity AHout (gwaterkg1dryair) is calculated as

  ðT*out Þð RÞ ¼ T^out ð FÞ þ 459:67

vpparout ¼ ðRHout Þðvpsatout Þ=100

mH20 ¼ MH20 : nH20 ¼ ð18:0Þ Vppar : V=R:T

Vppar ¼ ðvpparout Þ*100 2

and

VPDout ¼ vpsatout ð1eðRHout Þ=100Þ

where, mass of each (water vapor and dry air) is calculated using ideal gas law and molar weight by the following equations:

(6)

2.3. Greenhouse climate controller Climate controller is the key element of greenhouse automation system. In order to overcome the limitation of single variable (temperature or relative humidity) based ON/OFF climate controller to meet desirable temperature and relative humidity conditions in a greenhouse, VPD based MIMO fuzzy controller with MRVRL (multi-range variable rate and load) design methodology was implemented. As already highlighted in Section 1, VPD is an important greenhouse climate variable. It is defined as the difference between the saturated vapor pressure at the greenhouse temperature and actual vapor pressure present in a greenhouse (Prenger and Ling, 2000). Based upon the combined effect of

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temperature and relative humidity, VPD defines the single climatic variable conducive for plant growth and health conditions. As shown in Fig. 3, VPD increases nonlinearly with increase in temperature but decreases with increase in relative humidity. Every plant thrives well within its comfort VPD zone defined by its temperature and relative humidity levels. VPD controller has an edge over the temperature or relative humidity based controller as it balances both relative humidity and temperature levels in regulating greenhouse VPD that directly controls transpiration process in plants (Both, 2008; BC Ministry of Agriculture, 1994). The aim of this controller is to regulate GH-crop VPD within the acceptable levels irrespective of variations in outside VPD. The controller was programmed with expert fuzzy rule base that encapsulated the control strategies for driving multiple climate control equipments (roof vents, exhaust fan, cooling system, heating system and shade screen) at different operating status/load and rate (MRVRL design) in the best possible manner that could minimize greenhouse VPD error. Fig. 4 shows the simplified functional flow chart of the controller. Feedforward control loop mechanism was adopted that considered variation in outside VPD i.e. the disturbing signal to drive fuzzy controller inputs. VPD based multiple fuzzy controllers for cooling, heating and ventilation systems were designed and programmed for different VPD error ranges (low, medium or high). Depending upon the VPD error (VPDE), whether positive, negative or zero, calculated as difference of outside VPD and VPD set limits at each time instant, corresponding particular range controller (low, medium or high) was logically selected. Each range controller used three dual-input single-output fuzzy controllers to simultaneously operate ventilation (roof vents, exhaust fans), cooling and heating systems respectively. Each fuzzy controller was driven by the inputs, VPD error and change of VPD error, normalized within the particular (low/medium/high) range and fuzzified into seven linguistic terms associated with membership functions. Using the inputs and output membership functions, each fuzzy controller within the range was programmed with unique set (7  7) of IFTHEN control rules, thus providing total rules (7  7  3  3  2) in rule base. The intelligent rule set for each controller was devised considering combinational effect of operating all the actuating devices at different load and rate, to appropriately humidify or dehumidify greenhouse to reduce VPD error. Output of each fuzzy controller, normalized in the range (0e1) was further mapped to provide multistate output that decided the operating state (load) of the associated climate control equipment. Based upon VPD error range, variable rate algorithm was executed that varied the rate of heating and cooling systems. However, the rate of ventilation system remained constant. Also, in the case of extreme hot (outside temperature increases above 35  C) and cold conditions (falls below 5  C), shade screen was opened to avoid excessive rise of temperature or undue loss of heat. As soon as outside VPD fluctuated, climate controller appropriately activated all the climate control equipments and regulated greenhouse -crop VPD. The proposed controller is the

enhanced version of the event-based control paradigm gaining importance in GH climate control systems (Pawlowski et al., 2009).

3. Mathematical model The mathematical model describes the state of greenhouse climate i.e. time variation of different climatic variables such as greenhouse inside temperature, relative humidity and greenhouse crop VPD based upon outside climate data and greenhouse design parameters. The model equations were derived from heat and mass balance equations and various physical laws that explained the phenomenon of mass and heat flux transfer within greenhouse. Further, the model was programmed as open loop control model to simulate state of greenhouse climate under manual control. In this mode, user operates and changes parameters of the climate control equipments/ actuators. Also, the model was integrated with the VPD based MIMO fuzzy controller and was programmed as closed loop control model to simulate and control the state of greenhouse climate automatically for the selected weather data input and greenhouse design parameters.

3.1. Greenhouse temperature and absolute humidity model Greenhouse temperature model is based upon the basis relationship between temperature and heat flow governed by energy balance equation. It states that the rate of change of temperature within a closed structure is proportional to the difference between the inflow (Heatin) and outflow (Heatout) heat flux given as:

dT=dt ¼ CðHeatin  Heatout Þ

(11)

Assuming, the air enclosed within the greenhouse has homogeneous air properties of temperature and humidity within the entire space. Applying classical thermal modeling laws to greenhouse (Takakura and Fang, 2002), the rate of change of greenhouse inside temperature Tin ( C) is dependent upon the net solar radiation heat flux (Qin in W m2) entering the greenhouse, heat flux losses (Qloss in W m2) and heat flux gain (Qgain in W m2) due to various components such as greenhouse structure, design, glazing, shading, ventilation, cooling, heating and plant evapotranspiration effect and is estimated as:

  dTin =dt ¼ C Qin  SQloss þ SQgain

(12)

The net solar radiation (Qin in W m2) entering the greenhouse is dependent upon global solar radiation outside (Qout in W m2), glazing and shade screen parameters and is calculated as:


Qin ¼ 1  qfs Qing þ qfs ð1  Rs =100ÞQing

(13)

where, Rs is percentage of shade, qfs is the operating factor of shade screen given as:

qfs ¼ ð0=1Þ

(14)

and Qing (W m2) is heat radiation inside due to the glazing alone given as:

Qing ¼ aQout

Fig. 3. Variations in VPD with temperature and relative humidity.

(15)

where, a (dimensionless) is the solar radiation transmittance of glazing material. Heat flux loss due to conduction through glazing, (Qlg in W m2) is calculated as:

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Fig. 4. Functional flow chart of VPD based fuzzy climate controller for greenhouse.

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R. Pahuja et al. / Engineering in Agriculture, Environment and Food xxx (2015) 1e16


.  Qlg ¼ k Ag Af ðTin  Tout Þ

(16)

where, k is the overall heat transfer coefficient (W m2 C1), Ag/Af is the ratio of GH area of glazing (m2) to area of floor (m2) and (Tin  Tout) is the temperature difference between inside and outside temperature respectively. Heat loss due to radiation through glazing (nonlinear term) is neglected for simplicity. Heat flux loss due to ventilation (Qlv in W m2) is the summation of the heat losses due to infiltration (glazing and cracks) (Qlif in W m2), roof ventilation (Qlrv in W m2), forced exhaust fan ventilation (Qlfan in W m2) given as

Qlv ¼ Qlif þ Qlrv þ Qlfan

(17)

and is estimated as

Qlv ¼

j¼3  X


 ðNv Þj: qf v V=3600 :Cpair :rair :ðTin  Tout Þ

(18)

j

j¼1

where, j denotes index to represent heat flux losses due to infiltration (j ¼ 1), roof ventilation (j ¼ 2), exhaust fan (j ¼ 3) respectively. (Nv)j is the number of air exchanges per hour (ACH) for respective cases and for infiltration this value is taken as a constant of 2 ACH, V is the volume of GH structure (m3), Cpair is the specific heat of moist air (1020 Jkg1 K1), rair is the specific mass of air (1.2 kgdryairm3) and ((Nv)j.V (qfv)j)/3600) represents ventilation rate (Vrate)j (m3 m2 s1) corresponding to ventilation device, (qfv)j is the operating factor of ventilation devices (j ¼ 1, glazing for infiltration), (j ¼ 2, roof vents), (j ¼ 3, exhaust fan) given as:






qf v qf v qf v

 1

 2

 3

. 
Qgh ¼ Hc :qfh Af 1000

(25)

where, Hc is the heating capacity of heater (kW), qfh is the operating factor of heater and is given as:

qfh ¼ ð0=0:25=0:5=0:75=1:0Þ

(26)

Substituting and compiling all the above Eqs. in Eq. (12), greenhouse inside temperature is modeled as first order differential equation in terms of greenhouse parameters and is given as:



h
 1  qfs aQout dTin =dt ¼ 1 Cpair :rair: h
.  þ qfs ð1  Rs =100ÞaQout  k Ag Af ðTin  Tout Þ



 2 þ ðNv Þ2 qf v 2
    :V=3600 :Cpair :rair :ðTin  Tout ÞÞ þ ðNv Þ3 qf v 3 . 


ð24:3600Þ  L: ðET Þp þ EC qfc .  i
þ Hc :qfh Af : 1000 (27)

¼1

(19)

¼ ð0=1Þ

(20)

where, h (m) is the average height of greenhouse structure. Greenhouse absolute humidity model is based upon the basis relationship between absolute humidity and water vapor flow governed by mass balance equation. Expressing water vapor content within the greenhouse as absolute humidity, Win (gwater kg1dry air), it is modeled as first order differential equation which states that the rate of change of absolute humidity is proportional to the rate at which mass flux density accumulates (Wacc) in the greenhouse and is given as:

¼ ð0=0:25=0:5=0:75=1:0Þ

(21)

dWin =dt ¼ ½1=h:rair ½Wacc 

(28)

Heat flux loss due to evapotranspiration process in plants, (Qletp in W m2) is estimated as constant value depending upon the crop evapotranspiration rate (ET)p given by Eq. (22). With reference to maximum evapotranspiration rate of plants in summer season as quoted in literature (Rodriguez et al., 2008), (ET)p is approximated as constant value (8.9, 4.5,0 kg m2 d1) respectively for different plant growth index p (full crop, small crop, no crop).

where, Wacc is estimated by the mass balance equation as applied to greenhouse water vapor conditions inside and outside assuming that there are only two sources of water vapor gain in the greenhouse, evapotranspiration by cooling system, EC (kg m2 d1) and from plants (ET)p (kg m2 d1) and there is only one cause of water vapor loss, due to ventilation, given as:

. Qletp ¼ L:ðET Þp ð24:3600Þ

Wacc ¼ ½E þ ðAHout  Win ÞðVrate: rair Þ  ðVrate: rair Þ

(22) 1

where L is the latent heat of vaporization of water (2.5E6 Jkg ) Heat flux loss due to evaporative cooling system such as cooling pad/fogger, (Qlec in W m2), is given as

Qlec

. ð24:3600Þ ¼ L: EC: qfc


(23) 2

EC is transpiration rate of cooling system (kg m operating factor of cooling system and is given as:

qfc ¼ ð0=0:25=0:5=0:75=1:0Þ

d

1

), qfc is the

(24)

Heat flux gained by heating system (Qgh in W m2) is modeled as constant function depending upon the effective heating capacity of the heating system expressed per unit floor area (Af) and is given as:

(29)

where, E (kg m2 s1) is the total evapotranspiration rate inside greenhouse given as:

.
ð24:3600Þ E ¼ ðET Þp þ EC: qfc

(30)

Vrate (m3 m2 s1) is the total ventilation rate of greenhouse given as:

Vrate ¼

j¼3  X j¼1


  ðNv Þj :V qf v 3600 j

(31)

Compiling and substituting Eqs. (29)e(31) in Eq. (28), greenhouse inside absolute humidity is modeled as first order differential equation in terms of system parameters and is given as

Please cite this article in press as: Pahuja R, et al., Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller, Engineering in Agriculture, Environment and Food (2015), http://dx.doi.org/10.1016/j.eaef.2015.04.009

R. Pahuja et al. / Engineering in Agriculture, Environment and Food xxx (2015) 1e16

ih
. 
h ðET Þp þ EC: qfc ð24:3600Þ þ AHout dWin =dt ¼ 1=hrair 


  Win  1 2 þ ðNv Þ2 : qf v 2:  i

  qf v :V=3600 :rair þ Nv 3

3

(32)

9

Eqs. (33), (35) and (36) form the approximate dynamic model that describes the time behavior of greenhouse inside climatic conditions in response to outside conditions and system parameters under certain limitations. In order to get the model solution to simulate greenhouse inside variables, greenhouse initial climate conditions were assumed to be same as outside initial conditions. Also, greenhouse inside climatic conditions vary because of other factors not considered such as non linear radiation losses due to various other components of greenhouse, wind effect and actual evapotranspiration effect from plant growth.

3.2. Model solution 3.3. Open loop greenhouse climate control model Greenhouse inside temperature and absolute humidity as modeled by Eq. (27) and Eq. (32), were solved using Laplace and Inverse Laplace Transform considering variables (outside temperature and absolute humidity) as step signals of sampled value at each time instant and other parameters as constants. Initial value of greenhouse inside temperature and absolute humidity were assumed to be the same as the initial value of outside weather temperature and absolute humidity. Time response of greenhouse temperature, (Tin)(ti) in ( C), at the simulation time instant ti (min) is given as:

  Tin ðti Þ ¼ ððB þ F  EÞ=ðC þ DÞÞ þ ðTout Þi :½1  expð

The climate model described is Section 3.2 was programmed to simulate greenhouse climatic condition under open loop control mode. This model allows the user to simulate control of inside climatic conditions by manually selecting the climate control equipments, their operating state and rate given by Eqs. (39)e(48). All the other model parameters related to greenhouse structural design and plant parameters are user defined. In the case of ventilation, operating factors of roof vents (qfv)2 and exhaust fan (qfv)3 vary based upon manual selection of operating status (or percentage load) of devices given as:




 ððC þ DÞ=AÞ:ti  þ ðTout Þo ½expð  ððC þ DÞ=AÞ:ti  (33) where, A ¼ Cpair.rair.h, B ¼ ((1  qfs)aQout þ qfs (1  Rs/100)aQout), C ¼ k(Ag/Af), D ¼ (((2 þ (Nv)2 (qfv)2 þ (Nv)3(qfv)3.V/3600).Cpair. rair), E¼((L.((ET)p þ EC.qfc)/(24.3600)), F(ti) ¼ (Hc.qfh/Af.)1000, (Tout)i is the sampled value of outside temperature at the recording time instant (tr)i, (Tout)o is the initial value of outside recorded temperature, ti is the simulation time instant equal to the recorded time instant plus time lag of 1 min. Time response of greenhouse absolute humidity, (Win) (ti) in (gwater/kgdryair), at the simulation time instant ti (min) is given as:



 Win ðti Þ ¼ ðE=rair: Vrate Þ þ ðAHout Þi  1 1  expð  Vrate =hÞ:ti

þ ðAHout Þo expð  Vrate =hÞ:ti (34) where, (AHout)i is the sampled value of outside absolute humidity at the recording time instant (tr)i, (AHout)o is the initial value of outside recorded absolute humidity. Time response equations of greenhouse relative humidity, (RHin) (ti) in percentage and greenhouse crop vapor pressure differential, (VPDin)(ti) in (mB) at the simulation time instant ti, were calculated from the simulated values of greenhouse inside temperature and absolute humidity, and given as:

RHin ðti Þ ¼ ð100:vpin ðti Þ ðvpsat Þin ðti Þ

(35)

ðVPDin Þðti Þ ¼ ðvpsat Þin ðti Þð1eðRHin Þðti Þ=ð100ÞÞ

(36)

where, vpin(ti) is the partial vapor pressure inside greenhouse at time instant ti in Pa, calculated using ideal gas law and Win(ti) and is given as:

vpin ðti Þ ¼ ððWin ðti Þ:PÞ=ð0:622ð1000Þ þ Win ðti ÞÞ

(37)

(vpsat)in(ti) is the saturated vapor pressure (mB) inside greenhouse calculated using Arrhenius equation described in Eq. (1) at each instant of inside temperature Tin(ti).




qf v qf v

 2

 3

¼ 0=1  > roof vents : CLOSE=OPEN

(39)

¼ 0=0:25=0:5=0:75=1:0  > exhaust fan : OFF=LOW=MEDIUM=HIGH=FULL

(40)

Also, the rate of ventilation system varies based upon the manual selection of number of air exchanges per hour (ACH) for roof vents and exhaust fan given as:

ðNv Þ2 ¼ 10=20=30ðfor roof ventsÞ

(41)

ðNv Þ3 ¼ 60=120=240ð for exhaust fanÞ

(42)

In the case of shade screen, operating factor (qfs) and shading percentage (Rs in %) vary based upon the manual selection of operating status and percentage of shade given as:

qfs ¼ 0=1  > shade screen : CLOSE=OPEN

(43)

Rs ¼ 60=70=80=90%

(44)

In the case of cooling and heating system, the operating factor of these devices vary based upon manual selection of operating status of devices given as:

qfc ¼ 0=0:25=0:5=0:75=1:0  > cooling system : OFF=LOW=MEDIUM=HIGH=FULL

(45)

qfh ¼ 0=0:25=0; 5=0:75=1:0  > heating system : OFF=LOW=MEDIUM=HIGH=FULL

(46)

Also, the cooling and heating rate of the devices vary based upon the manual selection of transpiration rate (EC in kg m2 d1) of cooling system and heating capacity (Hc in kW) of heating system given as:

EC ¼ ðEC Þm ¼ 7=15=30=70

(47)

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Hc ¼ ðHc Þm ¼ 100=200=300=400

(48a)


 qfc ¼ qfc ðti Þc ; ¼ 0=0:5=1:0  > cooling system : OFF=HALF=FULL
 qfh ¼ qfh ðti Þc ; ¼ 0=0:25=0:5=0:75=1:0  > heating system

3.4. Closed loop greenhouse climate control model The model described in the Section 3.2 was also programmed in feedforward control mode and integrated with VPD based fuzzy greenhouse climate controller. It is used to simulate automatic control of greenhouse climate for the selected weather data input and other greenhouse design parameters. At each instant of simulation time (ti), the controller based upon inputs (outside VPD error and change of VPD error) and control rules, selects the appropriate rate (cooling and heating system rate) and operating status of different climate control equipments to regulate greenhouse crop VPD. The state of climate control equipments (rate and operating status) as decided by controller, describe the closed loop feedforward climate control model given by Eqs. (48)e(55). The operating factors of shade screen, roof vents, exhaust fan, cooling system and heating system vary, as decided by the controller. This changes the operating status of the devices accordingly at each instant of simulation time given as:


 qfs ¼ qfs ðti Þc ¼ 0=1  > shade screen : CLOSE=OPEN


qf v

 2

(48b)


 ¼ qf v ðti Þc ; ¼ 0=1  > roof vents : CLOSE=OPEN 2

(49)

 qf v ¼ qf v ðti Þc ¼ 0=0:25=0:5=0:75=1:0  > exhaust fan 3

(51)

3

: OFF=LOW=MEDIUM=HIGH=FULL (50)

: OFF=LOW=MEDIUM=HIGH=FULL (52) Also, the cooling rate (kg m2 d1) and heating rate (kW) of cooling and heating system vary as decided by the low, medium or high range controller.

EC ¼ ðEC Þðti Þc ¼ 10=26=32 Hc ¼ ðHc Þðti Þc ¼ 80=160=360 (ti)c denotes automatic selection of parameters by controller at each time instant.

4. Simulator Based upon greenhouse open and closed loop climate control model, an interactive, multi-featured simulation tool, ‘GHSim’ was implemented on the platform of GSD (graphical system design) software, LabVIEW 8.5 (Ritter, 2002; Johnson, 2001). It is standard software for designing virtual instruments for test, measurement and control applications (Wells, 1997). A general LabVIEW program is commonly called a Virtual Instrument (VI). It has a front panel that facilitates user interaction and the back-end block diagram that assembles graphical code by logically linking the functional blocks. The main panel of this VI tool was hierarchically designed by programming and linking subVIs, each of which executed a component of climate control model. Fig. 5(a) and (b) show user interface and operational flow chart of the simulator respectively. User interface allows the user to simulate greenhouse climate

Fig. 5. (a) GHSim GUI: Simulates greenhouse inside climate in response to the outside weather (20 Oct, 2011) data with average solar radiation of 50 W/m2 for A-frame, 12 span glass greenhouse, under open loop conditions with no crop and all actuating devices OFF. Under this condition, greenhouse inside climate is worse than the outside because of greenhouse effect and there is no loss of heat. (b) Functional flow chart of the simulator GHSim.

Please cite this article in press as: Pahuja R, et al., Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller, Engineering in Agriculture, Environment and Food (2015), http://dx.doi.org/10.1016/j.eaef.2015.04.009

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11

Fig. 5. (continued).

conditions based upon the weather data and other parameters selected. The important functions of the simulator are: (i) It provides option to simulate greenhouse climate conditions under open or closed loop conditions. (ii) It allows the user to select weather data from weather database and graphically displays weather trends and statistics with absolute time stamp. It also facilitates provision to add more weather records in desired format. (iii) It provides options to select various greenhouse structural parameters, plants parameters and climate control equipment parameters.

(iv) It allows user to feed set limits of temperature and relative humidity and calculates VPD low and high set limits for VPD based fuzzy controller operation under closed loop condition. (v) Under open loop conditions it allows the user to select greenhouse structure (shape, size, no. of spans, glazing material), weather data, state and rate of climate control equipment. At the click of START button it simulates variation of climate variables within greenhouse and save the result in the file, file name specified by the user. (vi) It has the provision to log results of consecutive simulation scenarios in the file. The stored simulation results are retrieved and viewed graphically on the same graph for comparison study and analysis.

Please cite this article in press as: Pahuja R, et al., Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller, Engineering in Agriculture, Environment and Food (2015), http://dx.doi.org/10.1016/j.eaef.2015.04.009

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(vii) Under closed loop conditions, it loads the predesigned fuzzy controller and simulates the control of greenhouse VPD within desired limits based upon weather data input and other parameters selected by the user. It indicates variations in outside and greenhouse inside VPD on time-graph along with set limits. It also shows the variation in operating status and rate of each of the actuating devices at time instant and save all results in the file. (viii) It calculates statistics of greenhouse simulated data and controller performance measures. (ix) It provides a mechanism to easily interact and work with the simulator, change/select parameters and study the effect of each on greenhouse inside climate.

5. Results Using this versatile simulator, greenhouse climatic conditions were simulated under various scenarios. A few of the results are discussed as under: 5.1. Greenhouse climate control simulation under open loop conditions with ventilation and cooling system During the summer season different control strategies are used to humidify greenhouse climate. Generally, combination of different climate control equipments such as ventilation (roof vents, exhaust vents), shade screen and cooling system (cooling pad, fogger or air conditioner) are used. Fig. 5(a) shows user interface of simulator that depicts the results of greenhouse climate simulation under open loop conditions, in response to the selected weather record, greenhouse structural parameters, operating status and rate of different climate control equipments. Table 2 provides the summary of climate control equipment parameters used in different simulation runs. For the weather data (temperature, absolute humidity and VPD) record of 5 h from time 10:31 A.M to 3:31P.M with average solar radiation of 50 W/m2, the greenhouse inside climate was simulated for A-frame, 12 span, single layered

glass greenhouse keeping all actuators OFF. The simulated results indicated that when all the actuating devices remained OFF and outside weather was hot, greenhouse inside climatic conditions became worse than the outside. There was no heat loss except due to infiltration and hence the energy trapped within the greenhouse (greenhouse effect) increased its temperature and VPD above the outside levels. Further, to study and compare the effect of using different control strategies (ventilation and cooling) on inside climatic conditions, different simulation runs were executed under different scenarios (SS1 to SS8) for the same structural parameters and weather data highlighted above. The consecutive simulation results were saved in the file (file name selected by the user) and depicted simultaneously on graphs along with the statistical analysis as shown in Fig. 6. For simulation scenarios SS2 (No crop, RV20Full) and SS3 (No crop, RV20Full, EF120), ventilation rate was increased by operating roof vents (RV) and exhaust fan (EF) respectively. This decreased greenhouse VPD but it remained higher than outside. For simulation scenarios SS4 (Large crop, RV20Full, EF120Full), crop transpiration rate was increased by selecting large crop. Under this condition, greenhouse climate conditions just became similar to outside as against simulation scenario SS1, when all the devices remained OFF. Ventilation provided some heat loss and reduced the excessive rise of greenhouse VPD above outside conditions, but was not able to decrease temperature and VPD further. Hence, effective cooling systems were required to humidify greenhouse during summer conditions. Further, simulations SS5 (Large crop, RV20Full, EF120Full, C7Medium) and SS6 (Large crop, RV20Full, EF120Full, C7Full) were done using full ventilation system and cooling system (C) with rate 7 kg/m2h. Operating status of cooling system increased from MEDIUM to FULL i.e. 50%e100% load for SS5 and SS6 respectively. With the increase in cooling system load, there was increase in greenhouse relative humidity and decrease in greenhouse VPD. To improve the effectiveness of the cooling system with the same rate, ventilation was reduced. For simulation scenarios SS7 (Large crop, RV20OFF, EF120Full, C7Full) roof ventilation remained OFF (CLOSE). This further reduced VPD. For simulation scenario SS8 (Large crop,

Table 2 Summary of greenhouse climate control model parameters used in open/closed loop simulations. Simulator model parameters: Open/closed loop control (user/controller selectable) GH structural/plant parameters

GH settings (summer)

Actuator parameters

Shape

A type

Climate variables

Low

High

Devices

Glazing

Single glass

Temperature ( C)

22

28

Roof ventilation

No. of Spans

12

Relative Humidity (%) VPD (mB) (Derived)

60 7.9

70 15.0

Exhaust fan

Length (m)

30

Cooling pad

Width (m)

6

Shade screen

Height1 (m) Height2 (m) Derived

8 12

Crop parameters Canopy Growth No crop/large crop Transpiration 8.9 (Kg m2 h1)

Floor area (m2) Glazing area (m2) Volume (m3)

2160 10,800 21,600

Open loop control (user selectable) Rate Operating Status 20 (ACH) CLOSE (0) OPEN(1) 120 (ACH) OFF (0) LOW (1) MEDIUM(2) HIGH (3) FULL(4) 2 1 7 (Kg m h ) OFF (0) LOW (1) MEDIUM(2) HIGH (3) FULL(4) 70% shade CLOSE (0) OPEN(1)

Closed loop control Rate (controller Operating status selectable) (controller o/p) 20 (ACH) (fixed) CLOSE (0) OPEN (1) 120 (ACH) (fixed) OFF (0) LOW (1) MEDIUM(2) HIGH (3) FULL(4) 15,26,32 OFF (0) 2 1 (Kg m h ) HALF (1) FULL (2) (variable)

70% shade (fixed)

CLOSE (0) OPEN(1)

Large crop 8.9

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13

Fig. 6. Simulated results of greenhouse climate (temperature, relative humidity and VPD) variables with respect to the outside summer data for A frame, 12 spans glass structure greenhouse, under open loop conditions using different control strategies (simulation scenarios SS1 to SS8). Table indicates average values of the input and simulated data.

RV20OFF, EF120High, C7Full), exhaust fan ventilation was reduced to HIGH state (75% load) that further decreased GH-crop VPD. During the early hours of the day, the combination of heating and ventilation was able to keep greenhouse inside VPD to acceptable levels but higher cooling rates were required to reduce it during the hottest part of the day. As the outside VPD increased during the day, cooling and ventilation rate needed to be readjusted accordingly. This would more effectively be done with the use of suitable controller as shown in the next section. 5.2. Greenhouse climate control simulation under closed loop conditions using VPD based fuzzy climate controller In the Section 5.1, it was demonstrated that appropriate combination of cooling with ventilation helped to humidify greenhouse to reduce high levels of GH-crop VPD. But, when outside VPD increased during the day then higher level of cooling rate was required to further reduce VPD levels. Manually operated greenhouse depends upon the user ability to select and operate actuators based upon the knowledge and experience as outside weather changes over the day and is a tedious process. With the use of proposed VPD based fuzzy climate controller that dynamically adjusted the rate and operating status (load) of different climate control devices as the weather changes, greenhouse climate was controlled automatically and more efficiently. Fig. 7 shows the simulation results of the greenhouse climate control process under different weather conditions on the typical days (17 Aug, 2012 and 20 Oct, 2011). Outside VPD variations for day

1 remained high and wide (24.3e113.9 mB) and for day 2 it was lower in the range (29.6e60 mB). Closed loop greenhouse climate control simulation demonstrated the effectiveness of proposed controller to automatically control greenhouse VPD within desired limits, irrespective of the variation in the outside VPD. The simulation was done using the same greenhouse structural parameters and set limits of temperature and relative humidity as summarized in Table 2. Some parameters of climate control devices such as ventilation rate of roof vents (20ACH), exhaust fan (120ACH) and shading percentage of shade screen (70%) remained constant whereas many parameters of climate control equipments varied according to the controller output. Based upon the user fed temperature and relative humidity set limits, VPD low (7.9 mB) and high (15 mB) set limits were calculated by the controller. Controller at each instant of weather data inputs and expert rules appropriately varied the operating status of all devices (shade screen, roof vents, exhaust fan, cooling pad) and cooling rate of cooling system and regulated greenhouse VPD within the set limits. Graphs (Fig. 7) indicate time variations of outside VPD; simulated greenhouse VPD and VPD set limits (7.91 mB and 15.09 mB) along with the variations and operating status/load of various devices at each time instance. When outside VPD was lower than 40 mB, on both the days, cooling system was operated at HALF (or1) state with rate of 10 kg/m2h. Exhaust fan was operated at HIGH (or 3) operating status (75% load) and thereafter, as outside VPD increased, it was switched to lower state (MEDIUM or 2) to control greenhouse VPD within set limits. When outside VPD was between (50e60 mB) on both the days, cooling system was operated at FULL (or 2) state thus

Please cite this article in press as: Pahuja R, et al., Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller, Engineering in Agriculture, Environment and Food (2015), http://dx.doi.org/10.1016/j.eaef.2015.04.009

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Fig. 7. Simulated result of automatic control of greenhouse inside VPD under closed loop using VPD based fuzzy controller in response to outside weather VPD on the two typical days. The controller tracks greenhouse VPD within desired limits with mean controller error of 0.88 mB and 0.25 mB respectively for two days, by simultaneously operating actuating devices at suitable loads and appropriately varying the cooling rate. Table indicates statistics of controlled greenhouse VPD, outside VPD and controller performance measures.

providing effective cooling rate of 10 kg/m2 h. Operating status of exhaust fan changed to HIGH (or 3) state to minimize VPD error. As outside VPD increased above 60 mB on the day 1, cooling system rate was increased to 26 kg/m2h but was switched to HALF (or1) operating status (50% load) thus providing effective cooling rate of 13 kg/m2 h. For very high VPD (above 90 mB) on the day 1, cooling system rate further increased to 32 kg/m2h but operating status remained the same (50% load), thus providing effective cooling rate of 16 kg/m2 h. Operating status of exhaust fan remained at MEDIUM (or2) (50% load) to control greenhouse VPD within optimum range. Roof ventilation remained OFF (operating status 0) and shade screen remained OPEN (operating status 1), because of continuous high outside temperature and VPD. Table (Fig. 6) summarizes the statistics of greenhouse climate control simulation and controller performance metrics. On each day, controller tracked greenhouse VPD within set limits with

average value of 12.13 mB and 12.41 mB respectively. However, outside VPD remained high on both the days with average value of 59.90 mB and 47.45 mB respectively. Controller performance measures such as VPD mean error (0.88 mB and 0.25 mB), VPD root mean square error (1.84 mB and 1.11 mB) and VPD root mean square deviation error (4.08 mB and 3.36 mB) remained within acceptable limits for the days. The simulated results indicate that the controller exhibited reasonably good performance in controlling greenhouse VPD under extreme weather conditions. 6. Conclusion and future scope Greenhouse simulator, GHSim, provides a good approximation to simulate greenhouse climatic under open and closed loop control model for different weather conditions and many greenhouse design parameters that encapsulates the realistic situations in the

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greenhouse. Interactive and easy to operate through the graphical user interface, the simulator is designed to be used as an educational tool to demonstrate greenhouse climate control principles and test the performance of automatic controller in regulating greenhouse climate within desired limit. In the open loop mode, various simulation runs can be executed to understand the effect of using different climate control equipments on inside climate for different weather conditions and greenhouse configurations. The simulation results can also be saved and graphically reproduced online for comparative study and analysis. In the closed loop control mode, VPD based MIMO fuzzy climate controller (MRVRL design) is integrated with the model. For the selected weather conditions and greenhouse configuration, the controller automatically simulates and regulate greenhouse crop VPD within the desired limits. It also provides information about the state of climate control equipments. This helps user to know and comprehend what type of climate control equipments would be suited under particular weather condition for the greenhouse in order to regulate its climate. Control of greenhouse crop VPD is in fact a better method of climate control as it simultaneously provides comfort zone of relative humidity and temperature within greenhouse for healthy growth and yield of crop. Simulator was used to demonstrate under open loop control, the effect of using ventilation and cooling system on greenhouse climate for the given greenhouse design and outside weather. Results highlighted that ventilation alone was not sufficient to regulate greenhouse climate under hot days. Cooling system of different rate was required to regulate greenhouse climate. Under closed loop control, the simulator demonstrated the effectiveness of VPD based fuzzy climate controller to simulate and regulate greenhouse crop VPD within desired set limits. It showed how a typical greenhouse design and controller would function for different summer conditions (outside weather) prevailing in the month of October and August and what climate control equipments would be needed to meet desired conditions. Under the hot weather condition, when outside VPD varied in the range 25 mBe100 mB or higher, VPD based fuzzy controller appropriately varied the rate of cooling system, and adjusted operating status of different climate control equipments such as shade screen, roof vents, exhaust fans and cooling system. This automatically regulated the greenhouse VPD within set limits irrespective of wide variations in the outside VPD. The controller exhibited high performance as it was able to keep the greenhouse climate optimal. The controller VPD mean error and root mean square deviation error remained low with very low (< 4mB) overshoot of GH-crop VPD for short duration of time (<1h). In short, the simulator is reasonably a good and novel tool to simulate greenhouse climate under different weather conditions and design parameters. It allows the user to study and comprehend the greenhouse climate control dynamics and methods of climate control. It also provides an integrated platform to design and test the performance of novel greenhouse crop VPD controller for automation of greenhouse climate. Being modular and flexible in design, the simulator functionality and features can further be enhanced both at the model and controller level, in a convenient manner. In future, the focus will be to modify the greenhouse climate model by incorporating the effect of other weather parameters such as wind speed, direction, rain etc., integrate and test performance of other advance (multivariable and multiobjective) greenhouse climate controllers and develop a web-enabled interface for the tool. Nomenclature AHout

Absolute humidity outside the greenhouse, gwaterkg1dryair

Win

15

Absolute humidity inside the greenhouse, gwater kg1dry air

Ag Af Cpair (ET)p EC E Hc h k L mH20 mdryair MH20 Mdryair P Qout Qin Qloss Qgain Qing Qlg Qlv Qlif Qlrv Qlfan Qletp Qlec Qgh RHout RHin R Rs Tout Tin T VPDout vpsatout vpparout Vppar V (Vrate)j Vrate vpin (vpsat)in VPDin

qfs

(qfv)j

qfc qfh a rair

Area of greenhouse glazing, m2 Area of greenhouse floor, m2 Specific heat of moist air, J kg1 K1, 1020 Evapotranspiration rate of plants, kg m2 d1 Transpiration rate of cooling system, kg m2 d1 Total evapotranspiration rate inside greenhouse, kg m2 s1 Heating capacity of heater, kW Average height of greenhouse structure, m Heat transfer coefficient, W m2 C1 Latent heat of vaporization of water, Jkg1, 2.5E6 Mass of water vapor, g Mass of dry air, g Molar weight of water, g/mol, 18 Molar weight of dry air, g/mol, 29 Barometric air pressure, Pa, 101325 Global solar radiation outside the greenhouse, W m2 Net solar radiation heat flux entering the greenhouse, W m2 Greenhouse heat flux loss, W m2 Greenhouse heat flux gain, W m2 Heat radiation flux inside due to the glazing, W m2 Heat flux loss due to conduction through glazing, W m2 Heat flux loss due to ventilation, W m2 Heat loss due to infiltration, W m2 Heat loss due to roof ventilation, W m2 Heat loss due to forced exhaust fan ventilation, W m2 Heat flux loss due to evapotranspiration process in plants inside the greenhouse, W m2 Heat flux loss due to evaporative cooling system, W m2 Heat flux gained by heating system, W m2 Relative humidity outside the greenhouse, % Relative humidity inside the greenhouse, % Universal gas constant, J·mol1 K1, 8.314472 percentage of shade, % Temperature outside the greenhouse,  C Temperature inside the greenhouse,  C Temperature outside the greenhouse, K Vapor pressure deficit outside the greenhouse, mB Saturated vapor pressure outside the greenhouse, mB Partial vapor pressure outside the greenhouse, mB Partial vapor pressure outside the greenhouse, Pa Volume of greenhouse, m3 Ventilation rate of devices specified by j, m3 m2 s1 Total ventilation rate of greenhouse, m3 m2 s1 Partial vapor pressure inside greenhouse, Pa Saturated vapor pressure inside the greenhouse, mB Greenhouse vapor pressure deficit, mB Operating factor of shade screen Operating factor of ventilation devices specified by j Operating factor of cooling system Operating factor of heater Solar radiation transmittance of glazing material. Specific mass of air kgdryairm3, 1.2

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Please cite this article in press as: Pahuja R, et al., Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller, Engineering in Agriculture, Environment and Food (2015), http://dx.doi.org/10.1016/j.eaef.2015.04.009

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Please cite this article in press as: Pahuja R, et al., Implementation of greenhouse climate control simulator based on dynamic model and vapor pressure deficit controller, Engineering in Agriculture, Environment and Food (2015), http://dx.doi.org/10.1016/j.eaef.2015.04.009