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Designing Temperature Control System for Mushroom Cultivation
DESIGNING TEMPERATURE CONTROL SYSTEM FOR MUSHROOM CULTIVATION
Budi Indra Setiawan
Department of Agricultural Engineering Faculty ofAgricultural Technology, Bogor Agricultural University P.D. BOX 220, Bogor 16002, Indonesia [email protected]
Abstract: Mushrooms prefer cold temperature with high humidity. In the past, mushroom growing were restricted in highlands with average temperature below 25°C. Recently, growers can cultivate mushrooms inside growth chambers equipped with refrigeration system for maintaining the favorable temperature. The objective of this research was to design a temperature control system using fuzzy logic to meet the optimal condition for mushroom cultivation in growth chambers. Theoretical approach was used to clarify the effectiveness of the designed control system and experimental works were undertaken using two varieties of mushrooms subjected to three different temperature levels. The control system could function as it was designed for and was capable to maintain the set points even under high fluctuation of atmospheric temperatures and frequent disturbances. The mushroom production was found to be very satisfactory as the yields were almost two times higher than that of the conventional practices. Copyright@ 2000 [FAC Keywords: agriculture, temperature, and fuzzy logic control
I.
INTRODUCTION
Mushrooms can grow easily in tropical climatic countries with high level of relative humidity. Farmers generally cultivate mushrooms in plant shelters with the objective to obtain appropriate environmental condition for mushroom growth. Kuping mushroom (Auricularia sp.) and Tiram mushroom (Pleuratus astreatus) can grow well in a temperature range of 25 to 30 °C and relative humidity of above 80%. The optimum growth condition is at a temperature range of 16 to 22 "c (Daryani. 1999). Efforts to maintain constant temperature during the growth of mushrooms have been done. for example, Wijaya (1999) used computer with fuzzy control system . At a controlled temperature condition the average diameters of the cap and stem reached 8.89 cm and 1. 11 cm, respectively. Whilst, at uncontrolled condition the average diameters reached only 5.78 cm and 0.78 cm. respectively (Paramita, 1999). The early stage of mushroom growth was faster at a controlled temperature of21 °C, but the highest yield was obtained at a controlled temperature of 17 0c. For Kuping mushroom and Tiram mushroom, the
average yields were 424 grams and 391 grams, respectively, per log bag (planting medium). The objective of this research was to design a temperature control system through a theoretical approach and to test its performance for mushroom cultivation.
2.
THEORETICAL APPROACH
1.1. Heat TransferModel In this experiment a model of growth chamber was built in the form of a cubical space box with walls made of transparent material, ventilation, and floor made of thermal proof material. This growth chamber model was equipped with electric lamp for lighting. Cold air was obtained from refrigeration machine. With the assumption that the room temperature was uniformly distributed (Paramita, 1999) and the temperatures of the plant and growth medium was equal to the room temperature, the heat transfer process occurred in the room was approached using Equation ) (Nelwan et. al., 1999; Wijaya, 2000):
158
m/p,s
dl
=mCp)TO
-~) -L vA(~ -To)+Q,.a~Q,.ej
.... .............. ..... ................. .. ... ................ ............. ( I)
Where ms was the mass of the system calculated as the sum of the air mass in the room and the mass of the mushroom growth medium (kg), Cp., and Cp.a were the specific heats of the system and air, respectively (J/kg.0C), m was the air mass flow rate (kg/s), Vi and AI were the overall heat 2 transfer coefficient (W/m2.0C) and surface area (m ) of the room , respectively (i= 1 for wall and i=2 for floor),. T, was the inside temperature and To was the outside temperature (0C), Q,ad and Q,,! were respectively lighting power and cooling power inside the room (Watt), and t was time Ch). The temperature outside the growth chamber (To) was obtained by measuring the temperature for 24 h at 1 h interval using mercury based thermOmeter. The relation between To and t was interpolated using Cubic Spline function (Setiawan, 1997). The integration process of Equation 1 was done numerically with Runge-Kutta method (Jeffrey, 1990). Iteration process was initially conducted to obtain the time interval (M) that could insure the stability and accuracy. In this experiment the time interval was found to be I s. To start the calculation, the value of T, was set equal to the initial To.
1.2. Control System
Control was conducted for various levels of optimum temperature for mushroom growth (Tsp). The control system worked on the basis of temperature difference between the actual and the optimum temperatures (£r), and the change in the actual temperature on time (,d£r). With the assumption that tJ.1 was constant, each was defined as follows:
= T'.1 - T"P , Er .I-M 7,.I = Er .
El' .I M
1··· .... · ...... · ....... .. .. .. .. .. .. · .. · .. ··· ..
I -
The specific heat of the mushroom cultivated was 3.S9 kJ/kg.oC. The mass flow of the cooling air was assumed to be constant at 10.8 kg/h blown by the refrigeration machine with a power of 300 W. The mass of the system was 10.323 kg with a specific heat of I.OS kJ/kg.oC and a 5 Watt electric lamp was used for the lighting of the growth chamber. The process of the temperature control was done by setting up three conditioning temperatures (T.p ) of22, 19, and 16 DC. The three temperatures were the optimum temperatures for mushroom growth especially in the tropics. Monitoring of the actual temperatures (T,) was done at interval of I s. The maximum operational duration of the refrigeration machine was 20 min and the minimum was 0 min.
4.
Table 1. Standard Deviation of the inside temperatures at various powers of refrigeration machine Refrigeration Tsp 16°C T. p 19 °C Tsp 22°C Power (W) 14.92 100 1I61.3 I 373 .95 0.27 0.43 200 0.72 300') 1.09 0.29 1.09 1.45 1.45 400 1.46 I.S2 1.82 500 I.SI .) IndIcates the best performance WIth the smallest SO for all T,p
..................... . ......... ... .. (3 )
MATERIALS AND METHOD
The dimensions of the growth chamber model (Paramita, 1999) were the following: 65 cm in length , 55 cm in width, and 75 cm in height. The
RESULT AND DlSCUSION
Table I is the simulation result showing the performance of the control system at various powers of the refrigeration machine used, i.e., 100,200,300, 400, and 500 W. The higher the power of the refrigeration machine was, the faster the achievement of the stable condition was reached. However, this also caused T, to run so fast that it passed over T.p (overshoot). Ifthe power of the refrigeration machine were too low, on the other hand, T, would never reach T.p '
(2)
Successively, the fuzzification process was conducted for Er and ,dEr. The type of the decision matrix and the defuzzification followed those used by Satyanto ( 1995), whereas the output was the duration of the operation of the refrigeration machine. The refrigeration machine would operate only when the output had positive value or larger than zero.
3.
walls of the growth chamber were made of lmm thick fiberglass framed with 9mm thick multiplex 2 pl~ood. Each wall had an area of 1.67 m and 0.49 m for the long sides and the short sides, respectively. The values of the overall heat transfer coefficients were 3.72 W/m 2 .oC and 3.13 W/m2.0C, respectively.
As shown in Table I, the refrigeration machine with a power of 300 W indicates better performance as compared to other levels of power. It is shown that the values of the Standard Deviation (SO) at this power level for the three levels of T,p are the lowest. Whilst, the power of 100 W never reaches the three levels of T,p' The 400 Wand 500 W levels result in even higher overshoots.
159
The result of the test on the possible existence of noise, for instance that was usually occurred when the door of the growth chamber was opened for maintenance activity, showed that the control system was capable of returning the temperature Tr to Tsp in a very short time. Accumulation of the cooling energy requirement for each Tsp was different but all followed straight-line patterns. Table 2 shows linear regression functions expressing the relation between energy requirement and time for each Trp at transient and stable condition. The coefficient of regression for all of the functions approaches the value of I.
temperature was overshot and the 17 QC temperature was difficult to reach but relatively more stable. The air relative humidity, as the result of the calculation using wet bulb and dry bulb temperatures, ranged from 75 to 90 %. The lower the room temperature was, the lower was the relative humidity.
100,--------------,
Table 2. Energy requirement for growth chamber coo I'mg Refrigeration Energy (Watt hour) Temperature Stable set point Transient 0.02 t - 3.72 22 0.08 t - 0.08 0.08 t 0.03 t - 34.6 19 I 0.04 t - 23.7 0.08 t - 0.08 16 I
i
eC)
Simulation results of the temperature control for the three set points (Tsp) indicated that each managed to approach Tsp although in different length of time. The lower the TfP was, the longer the time was required to approach. The decreasing temperature from the initial control to the T,p followed almost straight line. To reach the T,p levels of 22, 19, and 16°C, each required the times of around 16.8, 26.4, and 30 min, respectively. After the Tsp had been reached, the room temperature was stable enough with minor oscillation having a deviation of -0.0 I °C for the three levels of T,p' 24,------------------------, 23
22
Q' ~
50
o
30 45 60 Time (minutes)
75
90
Fig. 2. Relative Humidity inside the growth chamber under controlled condition at temperatures of 17, 19, and 21°C. Factors influencing the occurrence of the room temperature fluctuation among others were leakage and open-shut activity of the door when water spraying was carried out. Tn general, however, the expected condition of the temperature and relative humidity inside the room were satisfying enough to meet the requirement for mushroom cultivation. Harvest result indicated that this temperature control technique could increase the productivity around one and a half to two times as compared to that without any control system.
21
~
5.
:::>
~ 20 ~ 19 E Q)
r-
15
CONCLUSIONS
I. The designed control system managed to control
18
17
16 0
15
30
45
60
75
90
Trrre (ninutes)
Fig. I. Temperature inside the growth chamber under controlled condition at temperatures of 17, 19, and 21°C. Figure I and Figure 2 each shows the temperature and relative humidity, respectively, of the air inside the growth chamber as the results of the experiment using 300 W refi"igeration machine. The condition of 21°C room temperature was easier to approach as compared to the two other temperatures. The 19 QC
the temperature inside the growth chamber by means of regulating the on-off of the refrigeration machine. 2. In this research, the refrigeration machine with 300 W power indicated the best performance in controlling the temperature, especially for the set points of 16, 19, and 22°C. 3. This control system is good and responsive enough in handling disturbance such as sudden change in temperature.
REFERENCES Chang, S.T. and W.A. Hayes (1978). The Biological and Cultivation of Edible Mushrooms. Mc Graw Hill., New York.
160
Daryani, S. (\ 999). Pertwnbuhan jamur Kuping (Auricularia auriculae) dan jamur Tiram (Pleurotus ostreatus) dalam rumah tanaman dengan suhu terkendali. Skripsi (Thesis). Jurusan Teknik Pertanian. Fateta-IPB. Jeffrey, A. (1990). Linear Algebra and Ordinary D!fferentiai Equation, pp. 378~380. Blackwell Scientific Publications. Boston. Nelwan, L.O., K. Abdullah and B.I. Setiawan (1999). Temperature control of a ghe solar dryer using fuzzy logics. Proceedings of the First AsianAustralian Drying Conference. Bali, October 24th--27th, 1999. Paramita, F.P. (1999). Distribusi suhu dan kelembaban udara dalam ruang tumbuh jamur terkendali. Skripsi (Thesis). Jurusan Teknik Pertanian. Fateta-IPB.
Satyanto K. Saptomo, B.1. Setiawan, M.A. Iskandar dan S. Sarwono (1996). Pengatur suhu dengan pengontrol fuzzy. 1. Teknol. Ind Pert. Vol 6(2) 110-117. Setiawan, B.L (1997). Penerapan cubic spline interpolation dalam penentuan debit sungai. Jurnal Jeknik Pertanian. ISSN 0853-3695. Vol. 5(1). Wijaya, D.A. (2000). Simulasi sistem pengendaJian suhu dengan logika fuzzy pada rumah tanaman menggunakan program komputer. Skripsi. (Thesis) Jurusan Teknik Pertanian. Fateta-JPB. Wijaya, K. (1999). Pengendalian suhu pada rwnah tanaman Jamur dengan sistem kendali fuzzy. Skripsi (Thesis). Jurusan Teknik Pertanian. Fateta-IPB.
161
Budi Indra Setiawan
Department of Agricultural Engineering Faculty ofAgricultural Technology, Bogor Agricultural University P.D. BOX 220, Bogor 16002, Indonesia [email protected]
Abstract: Mushrooms prefer cold temperature with high humidity. In the past, mushroom growing were restricted in highlands with average temperature below 25°C. Recently, growers can cultivate mushrooms inside growth chambers equipped with refrigeration system for maintaining the favorable temperature. The objective of this research was to design a temperature control system using fuzzy logic to meet the optimal condition for mushroom cultivation in growth chambers. Theoretical approach was used to clarify the effectiveness of the designed control system and experimental works were undertaken using two varieties of mushrooms subjected to three different temperature levels. The control system could function as it was designed for and was capable to maintain the set points even under high fluctuation of atmospheric temperatures and frequent disturbances. The mushroom production was found to be very satisfactory as the yields were almost two times higher than that of the conventional practices. Copyright@ 2000 [FAC Keywords: agriculture, temperature, and fuzzy logic control
I.
INTRODUCTION
Mushrooms can grow easily in tropical climatic countries with high level of relative humidity. Farmers generally cultivate mushrooms in plant shelters with the objective to obtain appropriate environmental condition for mushroom growth. Kuping mushroom (Auricularia sp.) and Tiram mushroom (Pleuratus astreatus) can grow well in a temperature range of 25 to 30 °C and relative humidity of above 80%. The optimum growth condition is at a temperature range of 16 to 22 "c (Daryani. 1999). Efforts to maintain constant temperature during the growth of mushrooms have been done. for example, Wijaya (1999) used computer with fuzzy control system . At a controlled temperature condition the average diameters of the cap and stem reached 8.89 cm and 1. 11 cm, respectively. Whilst, at uncontrolled condition the average diameters reached only 5.78 cm and 0.78 cm. respectively (Paramita, 1999). The early stage of mushroom growth was faster at a controlled temperature of21 °C, but the highest yield was obtained at a controlled temperature of 17 0c. For Kuping mushroom and Tiram mushroom, the
average yields were 424 grams and 391 grams, respectively, per log bag (planting medium). The objective of this research was to design a temperature control system through a theoretical approach and to test its performance for mushroom cultivation.
2.
THEORETICAL APPROACH
1.1. Heat TransferModel In this experiment a model of growth chamber was built in the form of a cubical space box with walls made of transparent material, ventilation, and floor made of thermal proof material. This growth chamber model was equipped with electric lamp for lighting. Cold air was obtained from refrigeration machine. With the assumption that the room temperature was uniformly distributed (Paramita, 1999) and the temperatures of the plant and growth medium was equal to the room temperature, the heat transfer process occurred in the room was approached using Equation ) (Nelwan et. al., 1999; Wijaya, 2000):
158
m/p,s
dl
=mCp)TO
-~) -L vA(~ -To)+Q,.a~Q,.ej
.... .............. ..... ................. .. ... ................ ............. ( I)
Where ms was the mass of the system calculated as the sum of the air mass in the room and the mass of the mushroom growth medium (kg), Cp., and Cp.a were the specific heats of the system and air, respectively (J/kg.0C), m was the air mass flow rate (kg/s), Vi and AI were the overall heat 2 transfer coefficient (W/m2.0C) and surface area (m ) of the room , respectively (i= 1 for wall and i=2 for floor),. T, was the inside temperature and To was the outside temperature (0C), Q,ad and Q,,! were respectively lighting power and cooling power inside the room (Watt), and t was time Ch). The temperature outside the growth chamber (To) was obtained by measuring the temperature for 24 h at 1 h interval using mercury based thermOmeter. The relation between To and t was interpolated using Cubic Spline function (Setiawan, 1997). The integration process of Equation 1 was done numerically with Runge-Kutta method (Jeffrey, 1990). Iteration process was initially conducted to obtain the time interval (M) that could insure the stability and accuracy. In this experiment the time interval was found to be I s. To start the calculation, the value of T, was set equal to the initial To.
1.2. Control System
Control was conducted for various levels of optimum temperature for mushroom growth (Tsp). The control system worked on the basis of temperature difference between the actual and the optimum temperatures (£r), and the change in the actual temperature on time (,d£r). With the assumption that tJ.1 was constant, each was defined as follows:
= T'.1 - T"P , Er .I-M 7,.I = Er .
El' .I M
1··· .... · ...... · ....... .. .. .. .. .. .. · .. · .. ··· ..
I -
The specific heat of the mushroom cultivated was 3.S9 kJ/kg.oC. The mass flow of the cooling air was assumed to be constant at 10.8 kg/h blown by the refrigeration machine with a power of 300 W. The mass of the system was 10.323 kg with a specific heat of I.OS kJ/kg.oC and a 5 Watt electric lamp was used for the lighting of the growth chamber. The process of the temperature control was done by setting up three conditioning temperatures (T.p ) of22, 19, and 16 DC. The three temperatures were the optimum temperatures for mushroom growth especially in the tropics. Monitoring of the actual temperatures (T,) was done at interval of I s. The maximum operational duration of the refrigeration machine was 20 min and the minimum was 0 min.
4.
Table 1. Standard Deviation of the inside temperatures at various powers of refrigeration machine Refrigeration Tsp 16°C T. p 19 °C Tsp 22°C Power (W) 14.92 100 1I61.3 I 373 .95 0.27 0.43 200 0.72 300') 1.09 0.29 1.09 1.45 1.45 400 1.46 I.S2 1.82 500 I.SI .) IndIcates the best performance WIth the smallest SO for all T,p
..................... . ......... ... .. (3 )
MATERIALS AND METHOD
The dimensions of the growth chamber model (Paramita, 1999) were the following: 65 cm in length , 55 cm in width, and 75 cm in height. The
RESULT AND DlSCUSION
Table I is the simulation result showing the performance of the control system at various powers of the refrigeration machine used, i.e., 100,200,300, 400, and 500 W. The higher the power of the refrigeration machine was, the faster the achievement of the stable condition was reached. However, this also caused T, to run so fast that it passed over T.p (overshoot). Ifthe power of the refrigeration machine were too low, on the other hand, T, would never reach T.p '
(2)
Successively, the fuzzification process was conducted for Er and ,dEr. The type of the decision matrix and the defuzzification followed those used by Satyanto ( 1995), whereas the output was the duration of the operation of the refrigeration machine. The refrigeration machine would operate only when the output had positive value or larger than zero.
3.
walls of the growth chamber were made of lmm thick fiberglass framed with 9mm thick multiplex 2 pl~ood. Each wall had an area of 1.67 m and 0.49 m for the long sides and the short sides, respectively. The values of the overall heat transfer coefficients were 3.72 W/m 2 .oC and 3.13 W/m2.0C, respectively.
As shown in Table I, the refrigeration machine with a power of 300 W indicates better performance as compared to other levels of power. It is shown that the values of the Standard Deviation (SO) at this power level for the three levels of T,p are the lowest. Whilst, the power of 100 W never reaches the three levels of T,p' The 400 Wand 500 W levels result in even higher overshoots.
159
The result of the test on the possible existence of noise, for instance that was usually occurred when the door of the growth chamber was opened for maintenance activity, showed that the control system was capable of returning the temperature Tr to Tsp in a very short time. Accumulation of the cooling energy requirement for each Tsp was different but all followed straight-line patterns. Table 2 shows linear regression functions expressing the relation between energy requirement and time for each Trp at transient and stable condition. The coefficient of regression for all of the functions approaches the value of I.
temperature was overshot and the 17 QC temperature was difficult to reach but relatively more stable. The air relative humidity, as the result of the calculation using wet bulb and dry bulb temperatures, ranged from 75 to 90 %. The lower the room temperature was, the lower was the relative humidity.
100,--------------,
Table 2. Energy requirement for growth chamber coo I'mg Refrigeration Energy (Watt hour) Temperature Stable set point Transient 0.02 t - 3.72 22 0.08 t - 0.08 0.08 t 0.03 t - 34.6 19 I 0.04 t - 23.7 0.08 t - 0.08 16 I
i
eC)
Simulation results of the temperature control for the three set points (Tsp) indicated that each managed to approach Tsp although in different length of time. The lower the TfP was, the longer the time was required to approach. The decreasing temperature from the initial control to the T,p followed almost straight line. To reach the T,p levels of 22, 19, and 16°C, each required the times of around 16.8, 26.4, and 30 min, respectively. After the Tsp had been reached, the room temperature was stable enough with minor oscillation having a deviation of -0.0 I °C for the three levels of T,p' 24,------------------------, 23
22
Q' ~
50
o
30 45 60 Time (minutes)
75
90
Fig. 2. Relative Humidity inside the growth chamber under controlled condition at temperatures of 17, 19, and 21°C. Factors influencing the occurrence of the room temperature fluctuation among others were leakage and open-shut activity of the door when water spraying was carried out. Tn general, however, the expected condition of the temperature and relative humidity inside the room were satisfying enough to meet the requirement for mushroom cultivation. Harvest result indicated that this temperature control technique could increase the productivity around one and a half to two times as compared to that without any control system.
21
~
5.
:::>
~ 20 ~ 19 E Q)
r-
15
CONCLUSIONS
I. The designed control system managed to control
18
17
16 0
15
30
45
60
75
90
Trrre (ninutes)
Fig. I. Temperature inside the growth chamber under controlled condition at temperatures of 17, 19, and 21°C. Figure I and Figure 2 each shows the temperature and relative humidity, respectively, of the air inside the growth chamber as the results of the experiment using 300 W refi"igeration machine. The condition of 21°C room temperature was easier to approach as compared to the two other temperatures. The 19 QC
the temperature inside the growth chamber by means of regulating the on-off of the refrigeration machine. 2. In this research, the refrigeration machine with 300 W power indicated the best performance in controlling the temperature, especially for the set points of 16, 19, and 22°C. 3. This control system is good and responsive enough in handling disturbance such as sudden change in temperature.
REFERENCES Chang, S.T. and W.A. Hayes (1978). The Biological and Cultivation of Edible Mushrooms. Mc Graw Hill., New York.
160
Daryani, S. (\ 999). Pertwnbuhan jamur Kuping (Auricularia auriculae) dan jamur Tiram (Pleurotus ostreatus) dalam rumah tanaman dengan suhu terkendali. Skripsi (Thesis). Jurusan Teknik Pertanian. Fateta-IPB. Jeffrey, A. (1990). Linear Algebra and Ordinary D!fferentiai Equation, pp. 378~380. Blackwell Scientific Publications. Boston. Nelwan, L.O., K. Abdullah and B.I. Setiawan (1999). Temperature control of a ghe solar dryer using fuzzy logics. Proceedings of the First AsianAustralian Drying Conference. Bali, October 24th--27th, 1999. Paramita, F.P. (1999). Distribusi suhu dan kelembaban udara dalam ruang tumbuh jamur terkendali. Skripsi (Thesis). Jurusan Teknik Pertanian. Fateta-IPB.
Satyanto K. Saptomo, B.1. Setiawan, M.A. Iskandar dan S. Sarwono (1996). Pengatur suhu dengan pengontrol fuzzy. 1. Teknol. Ind Pert. Vol 6(2) 110-117. Setiawan, B.L (1997). Penerapan cubic spline interpolation dalam penentuan debit sungai. Jurnal Jeknik Pertanian. ISSN 0853-3695. Vol. 5(1). Wijaya, D.A. (2000). Simulasi sistem pengendaJian suhu dengan logika fuzzy pada rumah tanaman menggunakan program komputer. Skripsi. (Thesis) Jurusan Teknik Pertanian. Fateta-JPB. Wijaya, K. (1999). Pengendalian suhu pada rwnah tanaman Jamur dengan sistem kendali fuzzy. Skripsi (Thesis). Jurusan Teknik Pertanian. Fateta-IPB.
161