Fuzzy Logic Controller of temperature and humidity inside an

Fuzzy Logic Controller of temperature and humidity inside an agricultural greenhouse Rim BEN ALI, Emna ARIDHI, Mehdi ABBES, Abdelkader MAMI Université de Tunis El Manar, Faculté de sciences de Tunis, Ecole Nationale d’Ingénieurs de Tunis, LR11ES20 Laboratoire de recherche Analyse, Conception et Commande des Systèmes (LACS), 1002, Tunis, Tunisie [email protected], [email protected], [email protected], [email protected]

Abstract— In this paper, a fuzzy logic controller is developed in order to control the inside temperature and relative humidity inside an agricultural greenhouse. First, a thermal dynamic model was developed to describe the greenhouse environment and to predict the inside temperature and relative humidity. The agricultural greenhouse was modeled using Matlab-Simulink environment. The simulation results showed the effectiveness of the controller to achieve a favorable inside climate. Keywords— Greenhouse; temperature; relative humidity; Fuzzy Logic Controller (FLC); dynamic model; simulation

I.

INTRODUCTION

Regarding the economic importance of the agronomic sector, many environmental studies have been interested in improving the crop production using greenhouses and covered areas. The greenhouse modeling is fast and aims to predict the evolution of the inside micro climate. It can be static [1], where the energy consumption is estimated from the global thermal losses. It is simple. However, its accuracy is very limited and the solar energy does not taken into account. Nevertheless, the dynamic models show a good accuracy, but they are complex and not easy to be simulated for long period. They need much computing time and require considerable resources. Hence, most of these studies used the dynamic model, given its accuracy, to model the inside climate of greenhouse. Generally, there are three big modeling categories that are the model of knowledge, from it, a simplified model can be developed using simplifications [2], [3-6], [9-16] and the model of behavior [7-8]. The inside greenhouse environment is represented by the evolution of climatic variables such as the temperature and the relative humidity. Many control strategies have been developed to optimize the greenhouse climate (Neural Network [17, 18], Fuzzy Logic Controller (FLC) [19-21], Adaptive Predictive Control, PID [22], Nonlinear Adaptive PID control [23], Optimal Control…). In the present paper, the Fuzzy Logic Controller

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was used to control the inside temperature and relative humidity by taking into account the dynamics of input and output variables inside the greenhouse using membership functions and a learning method. However, the control algorithm requires a model describing the greenhouse system, which interacts with external climatic conditions and controlled equipment installed inside it, such as heating system, ventilation system, humidifying and dehumidifying system. In this paper, a physical model of the greenhouse and the FLC controller are developed in section II. The simulation results are discussed in section III. The last section is devoted to conclude the paper. II.

PHYSICAL MODEL AND INTELLIGENT CONTROLLER OF GREENHOUSE

This section is devoted to present the physical model and the intelligent controller of the greenhouse. It is, generally, divided into four homogeneous parts: the cover, the internal air, the crop and the soil. Nevertheless, only three components are studied here, which are the cover, the internal air and the soil surface. The heat exchange of the plants is neglected. The interactions between the different components, taken into account to determine the climate evolution inside the greenhouse, are shown in Fig. 1.

Fig. 1. Transfer interactions between greenhouse components.

A. Heat balance A simplified internal heat balance equation is expressed by (1) [1], [4], [12-14], [16]. dT

ρa Ca V in = tQshort − Qconv,cond − Qinfilt − Qlong + d heater Q − Qventilation (1) where, Tin is the inside temperature, ρa is the internal air density (Kg. m−3 ); Ca is the specific heat of air (J. Kg −1 . K −1 ), and V is the greenhouse volume (m3 ). The short wave radiation absorbed by the greenhouse was calculated using the following equation. Qshort =αc τc SI (2) with, αc is the cover absorptivity of solar radiation, τc is the cover transmittance, S is the surface area (m2 ), and I is the solar radiation (W. m−2 ). The convection and conduction heat transfer rate was estimated with the following equation: Qconv,cond = US(Tin − Tout ) (3) where, Tout is the outside temperature and U is the overall heat transfer coefficient through the greenhouse walls (W. m−2 . K −1 ), it is computed using (4). U=[

1

ho

+

Lc Kc

1

+ ]−1

(4)

hi

with, Lc is the cover thickness (m), K c is the cover k (Polyethylene) (W. m−1 . K −1 ), ho and hi are, respectively, the convective heat transfer coefficients of the outside and inside greenhouse cover (W. m−2 . K −1 ). They are determined using (5) and (6) [13-14], [24]. ho = 2.8 + 1.2 Vw (5) hi = 1.52|Tin − Tout |1/3 + 5.2(

R

Sc.L

)1/2

(6)

with, Vw presents the evolution of the external wind velocity (m. s −1 ), R is the number of air changes per hour (m3 . s −1 ), Sc is the greenhouse section (m2 ), and L is the greenhouse length (m).

with, Nh is the number of heaters, R h is the capacity of the heating system (W. m−2 ). The thermal energy loss from the cooling system is represented by (11): Qventilation = Ca R v (Tin − Tout ) with, R v is the ventilation rate (m3 s −1 ). B. Mass balance The internal water balance is determined using (12) [5], [7]. ρa V

dHin dt

= E − Qinfiltration + Qhum − Qdehum

(Tin −Tout ) 3600

Qinfiltration inside the greenhouse was calculated using (13). Qinfiltration = R v . (Hin − Hout ) (13) with, Hin and Hout are, respectively, the inside and outside relative humidity. E is the vapor transferred to the inside air from the soil by evaporation. It is computed using (14). E = Ce Vwin (pout − pin ) (14) where, pin and pout are, respectively, the inside and outside saturated vapor pressure (Pa) and Ce is the transfer coefficient of water vapor in the air (Kg. m−2 . s −1 . Pa−1 ). The agricultural greenhouse studied in this paper, is located in the Research and Technology Center of Energy in Borj Cédria, Tunisia. It occupies an area equal to14.8 m2 (length = 400 cm; width = 370 cm; height = 300 cm), as shown in Fig. 2.

(7)

The long wave radiation absorbed by the greenhouse is given by (8): Qlong = ho S(1 − τc )(Tin − Tsky ) (8) where, Tsky is the sky temperature that is suggested by Swinbank and calculated using (9) [2]. Tsky = 0.0552(Tout )1.5

, Tout in K

(9)

The thermal energy provided by the heating system is defined as (10) [5]: Qheater =

N h Rh S

(10)

(12)

where, Qhum presents the rate of humidity provided by the humidifying system and Qdehum is the rate of humidity loss from the dehumidifying system (g H2 O s −1 ).

The heat loss due to the infiltration through the greenhouse was computed using (7). Qinfilt = ρa Ca R

(11)

Fig. 2. Structure of the external view [14].

These present rules of the inside temperature are selected in order to have the desirable inside temperature. Therefore, if the temperature is high, we must decrease it using a ventilation system. But, when it is low, we needing a heating system to increase this one. The membership functions of this FLC controller are written by taking into account the variation of the internal temperature to reach the desired one by activating the actuators at a suitable rate. Fig. 3. External dimensions of the greenhouse.

C. Fuzzy Logic Controller Many agricultural greenhouses are still controlled manually. Thus, the cultivator intervention is required. However, some of them are equipped with an intelligent control device. Indeed, the FLC is suitable to be used for that purpose. Generally, to control the inside temperature and relative humidity, the greenhouse model has both types of input data: -

The membership function of the only input variable, the temperature error, is plotted in Fig. 4.

The disruption variables (the external weather): the

external temperature, the external relative humidity, the solar radiation and the wind velocity. The actuators: heating system, ventilation, humidifying and dehumidifying system.

The FLC is initialized by the membership functions of the air temperature. The fuzzy rules of the inside temperature fuzzy controller are given in Table. I.

Fig. 4. Membership function of the temperature error.

The membership functions of the output variables, which are the ventilation and the heating rate, are shown in Fig. 5.

FUZZY RULES BASE OF THE TEMPERATURE CONTROL

TABLE I.

Temperature error NNN

Ventilation rate

Heating rate

H

Z

NN

M

Z

Z

Z

Z

PP

Z

M

PPP

Z

H

The input variable of the temperature fuzzy controller is the temperature error ∆T , where: ∆T = Tsetpoint − Tinside where: NNN: Negative Big. NN: Negative Medium. Z: Zero. PP: Positive Medium. PPP: Positive Big. The output variables are the ventilation and the heating rate, where: Z: Zero. M: Medium. H: High.

Fig. 5. Membership functions of the ventilation and the heating rate.

The same principle was applied in order to reach the desired inside relative humidity. The fuzzy rules of the relative humidity are summarized in Table II.

FUZZY RULES BASE OF THE RELATIVE HUMIDITY

TABLE II.

CONTROL

Humidity error NNN

Humidifying rate Z

Dehumidifying rate H

NN

Z

M

Z

Z

Z

PP

M

Z

PPP

H

Z

D. Developed model The dynamic model of the agricultural greenhouse controlled by the FLC was developed using Matlab-Simulink environment. It is illustrated in Fig. 8.

The input variable of the relative humidity fuzzy controller is the relative humidity error ∆H , where: ∆H = Hsetpoint − Hinside The membership functions of the input and output variables of the relative humidity are plotted, respectively, in Fig. 6 and Fig. 7.

Fig. 8. Dynamic model of the controlled greenhouse using Matlab-Simulink.

Two blocks of the FLC controller are used in this model. The first one controls the inside temperature and the second one to hold the inside relative humidity at its set point. The simulation is performed for an external weather and parameters of an agricultural greenhouse installed in Borj Cédria. The input parameters of the cover and the inside air, used in the model, are summarized in Table III [13, 14]. TABLE III.

Fig. 6. Membership function of the relative humidity error.

COVER AND INSIDE AIR CHARACTERISTICS.

Symbol

Numerical value

Units

Description

ρa

1.137

Kg. m−3

Density of internal air

Ca

1005

J. Kg −1 . K −1

Specific heat of air

αc

0.1

-

Cover absorptivity of solar radiation

τc

0.85

-

Cover transmittance

The variations of the external solar radiation and the external speed wind velocity during three days in winter are displayed, respectively, in Fig. 9 and Fig. 10.

Fig. 7. Membership functions of the humidifying and the dehumidifying rate.

Fig. 9. Solar radiation profile.

Fig. 10. Wind velocity profile.

III.

Fig. 13.Ventilation rate.

RESULTS AND DISCUSSION

The simulation results of the air temperature without and with the FLC control are plotted in Fig. 11 and Fig. 12, respectively.

Fig. 14. Heating rate.

The simulation results of the relative humidity without and with control are plotted in Fig. 15 and Fig. 16, respectively.

Fig. 11. The evolution of the inside and outside air temperature without control.

The internal air temperature varies according to the external condition, but the inside climate is not always favorable. For this reason, it is necessary to install actuators (ventilation, heating system), which are controlled by an intelligent controller such as the FLC controller, to reach the desired inside climate. The simulation results are shown in Fig. 12.

Fig. 15.The evolution of the inside and outside relative humidity without control.

During the night, the internal and external humidity increase to reach their maximum value nearly 100% and they decrease during the day to achieve their minimum value. However, to maintain the inside relative humidity as its desired set point, an FLC controller is used in order to control the operation of the actuators such as humidifying and dehumidifying systems, as shown in Fig. 16.

Fig. 12. The evolution of air temperature with FLC controller.

The set point temperature is chosen 24◦C during the day and 15◦C during the night. The inside air temperature of the greenhouse varies around the set point during the night by acting on the heating system (Fig. 14). However, it reaches 24°C due to the operation of the cooling system during the day (Fig. 13). Fig. 16.The evolution of the relative humidity with FLC controller.

The value of the relative humidity set point is defined as 70%. The dehumidifying system maintains the inside humidity around the set point in the night (Fig. 18). Therefore, the humidifying system increases the inside humidity to achieve 70% during the day (Fig. 17).

[5]

[6]

[7]

[8]

[9]

[10] Fig. 17. Humidifying rate. [11]

[12]

[13]

Fig. 18. Dehumidifying rate.

IV.

CONCLUSIONS

A simplified dynamic model of an agricultural greenhouse was developed and controlled with a Fuzzy Logic Controller in order to predict and control the inside climate behavior regarding the impact of the climate on the production and product quality. The simulation results showed that the FLC controller presents an effective solution to reach the favorable climate inside the greenhouse by acting on the controlled equipment installed inside.

[14]

[15]

[16]

[17] [18]

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