Agric. sci. dev., Vol(5), No (1), March, 2016. pp. 1-5
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ISSN:
Agriculture Science Developments
2306-7527
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Simulation of Control System in Environment of Mushroom Growing Rooms using Fuzzy Logic Control Sina Faizollahzadeh Ardabili * Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
Asghar Mahmoudi Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
Tarahom Mesri Gundoshmian Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Mohaghegh Ardabili, Ardabil, Iran.
Hosein Behfar Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran. *Corresponding author:
[email protected]
Keywords
Abstract
Comparing parameters Energy consumption Fuzzy control Influenced parameters Mushroom Real system
Investigation of the influenced parameters(temperature, relative humidity and carbon dioxide concentration in mushroom production rooms can be one of the main factors to reduce energy consumption or increase the quantity and quality of the product. Overcoming on difficulties such as controlling temperature, humidity and CO2 concentration plays the main role on successful mushroom growing. Mushroom production involves several stages. Each stage requires different temperature, humidity and carbon dioxide concentration. This paper has been modeled based on theoretical and experimental research on a mushroom production company. Modeled system is working based on fuzzy logic controller. Fuzzy logic controller was designed by obtained data from optimal condition on growing hall using precise sensors and loggers during specific periods of time. Parts of system such as growing hall and actuators were modeled on Simulink part of Matlab software and the designed fuzzy controller were applied on modeled system and output of the modeled system compared with the real system output using comparing parameters such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage/Relative error (MAPE) and Pearson Correlation (R). Obtained results from both system showed that the mean values of modeled system for temperature and relative humidity are closer to set point than real system and also correlation between real and model system for three parameters indicates that the model and real system are working close.
A h m D L U T q Cp m k Nu
1.
heat transfer area (m2) heat transfer coefficient (W/℃m2) mass (kg) diameter (m) length (m) Overall heat transfer coefficient(Kw/m2C) Temperature (℃) heat transfer(w) specific heat (Kj/Kg K) mass flow (Kg/s) thermal conductivity (W/m·K) Nusselt number
Re Reynolds number Pr Prandtl number Subscribes a Air Sa supply air W1 east and west walls W2 south and north walls r roof amb ambient z zone in internal ex external
Introduction
Nowadays increasing population and limited agricultural inputs such as land and water, have shown necessity of attention to the modern methods and high-yield production of agricultural products. Greenhouse production is the one of modern methods on agricultural producing. Mushroom production principles is too close to greenhouse producing principles. Successful mushroom growing involves overcoming difficulties such as temperature, humidity control and CO2 concentration. Studies showed the polysaccharides and complex of polysaccharideprotein in the mushroom has anti-cancer properties [3] [5]. Due to the numerous advantages of mushroom using this product in the diet is necessary. Fast growing of computer applications in agriculture was followed a need to use more advance controllers to reach the best and most affordable greenhouse climate control systems [7]. Mushroom production involves several stages. Each of these steps requires different temperature, humidity and carbon dioxide concentration. Investigation of the implicated parameters in mushroom production halls can be one of the main factors to reduce energy consumption or increase the quantity and quality of the product. The parameters implicated in environment control of mushroom production halls can be influenced by factors such as weather conditions, operator error and above all, uncontrolled entrance into the mushroom production halls in order to control parameters and etc. The lack of appropriate control system makes, uncontrolled entry operator to control the parameters. This action in addition to stir the atmosphere, can cause contamination the inside atmosphere of the hall. Researchers used many control techniques in different fields. From the conventional control such as: proportional integral and derivative (PID) controllers, artificial intelligence (AI) include fuzzy logic systems (FLS), artificial neural networks (ANNs) and genetic algorithms (GAs) to advanced techniques like predictive, adaptive, robust and non-linear control [1] [9]. Using fuzzy logic control is simple if incorporated with analog-to-digital converters, and 4-bit or 8-bit one-chip micro controllers. Improving performance can easily do by changing rules or adding new features to the system. In many cases, fuzzy control can be used to improve existing controller systems by adding an extra layer of intelligence to the current control method. In a research conducted by, an energy saving of about 6% was achieved in greenhouse set point determination using fuzzy sets. [7]. The purpose of this study is to design a fuzzy control system and comparing with real condition that the controller is working on
Sina Faizollahzadeh Ardabili *, Asghar Mahmoudi, Tarahom Mesri Gundoshmian, Hosein Behfar
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Agriculture Science Developments Vol(5), No (1), March, 2016.
real growing hall on temperature, relative humidity and indoor carbon dioxide density parameters. This paper, has been modeled based on theoretical and experimental research on a mushroom production company in Ardebil province of Iran. The cold climate of region affected on the methods of controlling the parameters. Precise control can be achieved by developing, in a first stage a good prediction model of the inside parameters (temperature, relative humidity and CO2 concentration) and in the second stage a controlling law which permit to output values to follow values that depends on the plants nature. In this paper, we are interested only in the modeling phase.
2.
Methodology
2.1 Actuators modeling Air handling system is the main used actuator in this study. This system is able to perform heating and cooling of indoor temperature, needed moisture supply to the hall and control the concentration of carbon dioxide. Output of Indoor carbon dioxide on mushroom growing halls are enough and decreasing on CO2 density is momentarily, so there is no need to external source to compensation carbon dioxide. Model of Heating, Ventilating and Air Conditioning (HVAC) system has three separate parts. The first part is heating or cooling coils, the second part is moisture supply system to required relative humidity of growing hall and the third part is the fresh air and circulation air dampers to decreasing the relative humidity and CO2 concentration. We provided modeling of each part, separately. For coil modeling, we use internal and external equations on heat exchangers follow as [4]: 0.0668(D )Re d Pr L
Nu in 3.66 +
(1)
2
(1 + 0.04[(D ) Re d Pr] 3 L h
Nu K D
(2) 1
Nu ex = C m C 2 Re d,max Pr 0.36 (Pr Pr ) 4
(3)
s
U=
1 (1
hi
)+( 1
ho
)
NTU UA
(4)
C min (Th,i - Th, o ) (1 - exp{-NTU[1 + (C min C min )]}) = (Th,i - Tc,i ) (1 + (C min C max ) )
(5)
q = q max
(7)
(6)
Eq.1 and Eq.3 help us to find Nusselt number for internal and external flow, respectively. Eq.2 gives us convection coefficient that find using Nusselt number. Using the Eq.4 give overall heat transfer coefficient (U). After this steps, using Eq.5, Eq.6 and Eq.7 give us the overall heat transfer (q). 2.2 Measurement Three PT-100 sensors, one HIH-4000 sensor and one CO2 meter, equipped with data logger, were used in order to measuring temperature, relative humidity and density of CO2 on real growing hall, respectively. All data were recorded via DAQ Master, the interface software of temperature measuring device, and a data logger for recording relative humidity and density of CO 2 data. The mean value of three temperature was used on this study. 2.3 Zone model The zone model is taken based on zone model presented by Tashtoush and Molhim (Eq.8) [8]. In this model, thermal balance of hall, on the South and North walls (Eq.9), East and West walls (Eq.10) and roof (Eq.11) were calculated separately. dTz = m sa C p,a (Tsa - Tz ) + 2U w1 A w1 (Tw1 - Tz ) + U r A r (Tr - T z ) + 2U w2 A w2 (Tw2 - Tz ) + q(t) dt dTw1 = U w1 A w1 (Tz - Tw1 ) + U w1 A w1 (Tamb - Tw1 ) dt dTw2 = U w2 A w2 (Tz - Tw2 ) + U w2 A w2 (Tamb - Tw2 ) dt dTr = U r A r (Tz - Tr ) + U r Ar (Tamb - Tr ) dt
(8) (9) (10) (11)
2.4 Fuzzy control One of the most successful applications of fuzzy logic methods is fuzzy knowledge-based systems. The simplicity and flexibility of fuzzy systems enable it to describe knowledge using the rules and perform theoretical developments in this field. Fuzzy controlling and modelling provide a framework for modelling the non-linear complex relations, using a rule-based approach [6]. The used fuzzy controller has 3 inputs include temperature, relative humidity and CO 2 density values and 4 outputs include water flow in coils, position of fresh air and circulation air dampers and water flow of moisture supply pump. This fuzzy system designed based on mamdani inference system. Triangular Fuzzifier functions are used in fuzzy controller and center of gravity defuzzifier used on deffuzziering output values of fuzzy system. The fuzzy control rules were raised based on experimental measured data on mushroom growing hall. Table1 shows the outputs of fuzzy controller and their relationship with the actuators:
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Simulation of Control System in Environment of Mushroom Growing Rooms using Fuzzy Logic Control Agriculture Science Developments Vol(5), No (1), March, 2016.
Table 1. Outputs of fuzzy controller and their relationship with the actuators. Controller Temperature-Relative humidity
CO2
Output Fresh air damper Circulation air damper Water flow valve Pump(sprayer) Fresh air damper Circulation air damper
2.5 Control performance on real system On real system, there is no specific control system and operators control actuators manually i.e. they control water valves, pump flow and dampers manually by using analog and handheld devices to obtain optimal conditions. This condition can be influenced by factors such operator careless, operator fatigue, lack of precision on measuring tools of parameters and operator forgetting to visit the growing halls status, furthermore operator entrance and exit into the hall may contaminate growing halls. 2.6 Simulink Simulink is a graphical programming language that uses mathematical relations for modeling, simulation and analysis of dynamic systems. The main Simulink interface is based on a graphical block diagram tool and set of customizable blocks on block library. In this section Simulink models for actuators and growing hall are presented. Fig. 1 shows the modeling for heating or cooling system based on water flow in heating or cooling coil that is designed on Matlab simulation software (based on Eq.1 to Eq.7). Dampers, humidification system performance and hall model are indicated on Fig. 4, Fig. 5, respectively (based on Eq.8 to Eq.11). As seen on Fig. 1 heating or cooling system that works based on water flow in coils, has three inputs (water flow, input air temperature and water temperature) and two outputs (overall rate of heat transfer and output temperature of system). Changing the value of input parameters will change output values.
Figure 1. Modeling for heating or cooling system based on water flow
Fig.2 indicates that damppers model has eight inputs include, indoor and outdoor temperature, relative humidity and CO2 density and percantege of open or close of dammpers, and has three outputs include, supplied air temperature, relative humidity and CO2 density of supplied air.
Figure 2. Model of dampers performance system
Sina Faizollahzadeh Ardabili *, Asghar Mahmoudi, Tarahom Mesri Gundoshmian, Hosein Behfar
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Agriculture Science Developments Vol(5), No (1), March, 2016.
Fig.3 shows the growing hall and humidification system modeling on Simulink software. This model has 5 inputs include, ambient temperature, supply air temperature, overall rate of heat transfer, volumetric rate of input air and volumetric rate of sprayer (pump) and three outputs include temperature, relative humidity and CO2 density. Simulation model for the whole system is shown on the Fig. 4.
Figure 3. The hall and humidification model system
Figure 4. The whole model system based on fuzzy control system
3.
Results and discussion
The following figures show the output of each parameters on real and model controlling system. The blue lines represent model system performance, red lines represent the real controlling system and black lines indicated the set point value of each parameter.
Figure 5. Result of temperature controlling
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Simulation of Control System in Environment of Mushroom Growing Rooms using Fuzzy Logic Control Agriculture Science Developments Vol(5), No (1), March, 2016.
Figure 6. Result of CO2 density controlling
In order to evaluating the system performance, some of performance comparison parameters such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage/relative error (MAPE) and Pearson Correlation (R) were determined between outputs of model and real system performance Table 2. Table 2. The obtained results from t-paired test Parameter
Setpoint
Temperature (centigrade)
25
Relative Humidity (%)
92
Density CO2 (ppm)
6500
Model Real Model Real Model Real
Mean 24.97 25.15 92.06 91.8 7056.2 6256.9
maximum 25.1 25.4 92.5 92.5 7200 7250
minimum 24.8 24.65 91.1 91 6900 5600
R
MAE
MAPE
RMSE
0.67**
0.232
0.0092
0.253
0.905**
0.281
0.003
0.347
0.921**
755.9
0.11
859.15
** Significant at 1% level
The obtained results from both real and fuzzy model controlling system showed that the mean values of model system for temperature and relative humidity are closer to setpoint than real system and also correlation between real and model system for three parameters indicates that the model and real system are working close but based on table 2 the band pass of model system is smaller than real system.
4.
Conclusion
Modeling control system and comparing with real controlling system for three influencing parameters include temperature, relative humidity and carbon dioxide density in order to keep the desired value was the main purpose of this study. Inputs and outputs data were collected from target HVAC and growing hall and controlling systems were developed based on Fuzzy control system. The developed model had high accuracy and was able to control parameters precisely and close to setpoint values. The small band pass of developed model will has less Atmospheric tension in the hall.
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