Micro climate control in greenhouses based on phytomonitoring data
Uwe Schmidt, Ingo Schuch, Dennis Dannehl, Thorsten Rocksch, Sonja Javernik
Humboldt University Berlin
Biosystems Engineering Division,
Albrecht-Thaer-Weg 3, 14195 Berlin, Germany
climate control innovations
phytomonitoring concept
From Phytomonitoring to Phytocontrol – prime examples
• ventilation and fog control
• irrigation control
• screen control
• CO2 control
• dehumification control
Conclusion…
Founded by
Innovations in Control Strategies:
 Unlimited set-points
 Accumulation set-point control
 Rate-of-change (proportional) set-point control
 Average, minimum or maximum set-point control
 Unlimited DIF settings
 Unlimited morning, afternoon, evening, night settings
plus any time in between
 Set control per day for the entire growth cycle
 Fuzzy logic provides “intelligent” control decisions
 Controls to manage energy consumption and stagger
equipment cycles to prevent power surges
innovations in climate control
disturbation
Process
Actuator
Sensor
PID
controller
disturbation
Process
Actuator
control value
SARA
measurement
set value
control value
Modell
calculation
controller
set value
• higher control
precision by
process adapted
control algorithms
• self-learning
algorithms for the
estimation of P-, I, D-gain factor
• model leaded
process control
with model
adaptation
• thermo-dynamical
models or
• neuronal network
models
innovations in climate control
model supported open loop
control with a combination
of steady state and dynamic
model approach
Schmidt, U.: 1996, Greenhouse
Climate Control with a Combine Model
of Greenhouse and Plant by Using
Online Measurement of Leaf
Temperature and Transpiration Acta
Hort. 406: 89 - 98
Predictive open loop
control with artificial
neuronal networks
Salazar, R. et. al. 2010, Neuronal
Networks Models for Temperature and
CO2 Control, Int. Journal of agricultural
Research 5 (4): 191-200
innovations in climate control
disturbation
control value
Process
Actuato
r
heating
control
Sensor
measurement
PID
controller
set value
disturbation
control value
Process
ventilation
control
Actuato
r
Sensor
measurement
PID
controller
disturbation
set value
control value
Process
Actuato
r
Sensor
CO2 control
measurement
PID
controller
set value
disturbation
control value
Process
screen
control
Actuato
r
Sensor
measurement
PID
controller
set value
• fusion of different
control circuits
• handling of
restrictions
• intelligent
management of
the interaction of
different technical
systems
Working with new control variables
air
temperature
thermal
radiation
light
conditions
aerial environment
relative
ventilation
humidity
energyscreen
thermal
radiation
air
temperature
plant
leaf-, fruittemperature
heating
system
light
conditions
shading
stomatal
aperture
relative
Growth
humidity
Artificial
light
photosynthesis
CO2,
concentration
transpiration
fog
system
respiration
vapour
pressure
…
CO2/O2 gas
exchange
irrigation
sap flow
…
Watermanagement
rhizosphere environment
CO2
enrichment
nutrient
supply
fertilization
only a few
seriously
approaches for
continuous
measurements
over the entire
cultivation
period…
Solar Panel
AC Power
Adapter
12 V Battery
PM-48M
Photosynthesis
Monitor
1 REF
36 LPM
Auto/Man
Auto
Pump
RS232
REF/LC
Radio
Communication
Channel
EPM gas exchange system
by Steinbeis GmbH
(developed by U. Schmidt,
HU Berlin)
PM Phytomonitor by
Phytech Ltd. Israel
(developed by Y. Ton)
Introduction - The ZINEG network
maximum greenhouse isolation,
integration climate control
closed greenhouses
heat protection glass
CO2 neutral heat supply in
foliage greenhouses
Berlin/Hannover
economical evaluation
founded by
The new prototype of the Berlin Plant Response Monitoring System Bermonis
permanent fixed leaf cuvette
installation bar for 10 cuvettes at different
positions hanging on the high wire
electronic and pneumatic unit
Bermonis embedded in the canopy
result: climate control
ventilation and fog control
with net photosynthesis…
PAR [W/m²]
stomatal conductivity [cm/s]
vapour concentration difference [g/kg]
light use efficiency [%]
SCHMIDT, U., 2004: Decision support for greenhouse climate control using a computerised Mollier Diagram. Acta hort. 654, 187 - 193
PAR [W/m²]
stomatal conductivity [cm/s]
vapour concentration difference [g/kg]
light use efficiency [%]
SCHMIDT, U., 2004: Decision support for greenhouse climate control using a computerised Mollier Diagram. Acta hort. 654, 187 - 193
PAR [W/m²]
stomatal conductivity [cm/s]
vapour concentration difference [g/kg]
light use efficiency [%]
SCHMIDT, U., 2004: Decision support for greenhouse climate control using a computerised Mollier Diagram. Acta hort. 654, 187 - 193
material and method:
estimation of the microclimatic comfort zone
method: mollier-plot-analysis by SCHMIDT
ventilation set point: 25 °C
Collector
Reference
June 11
Pannovy
30°C
30°C
25°C
25°C
20°C
20°C
June 12
Encore
30°C
30°C
25°C
25°C
20°C
20°C
June 13
Pannovy
30°C
30°C
25°C
25°C
20°C
20°C
global radiation [W/m²]
vapour concentration difference [g/kg]
Fruit increment [µm/h]
SCHMIDT, U., 2004:
Decision support for
greenhouse climate
control using a
computerised
Mollier Diagram.
Acta hort. 654, 187 193
light use efficiency [%]
light use efficiency [%]
irrigation control
Phytomonitoring for water use prediction and irrigation control
LT: leaf
transpiration
CT=f(LT;LAI)
PM
LAI model
ΣCT = ΣCT +CT
n
ΣCT
>setpoint
?
y
irrigation
IR
Overflow
Overfl%
ΣCT = 0
IR = ΣCT +
ΣCT*Overfl%
target: constant overflow after
all irrigation cycles
result: irrigation control with measured leaf transpiration
electronically
tilt-tray sensors
under the
gullies for
overflow
measurement
electronically
water meter
for water
input
measurement
2500
estimated daily water consumption [kg]
estimated daily water consumption [kg]
2500
y = 0,8029x + 129,77
R² = 0,711
2000
1500
1000
500
y = 0,7897x + 122,17
R² = 0,8035
2000
1500
1000
500
0
0
0
500
1000
1500
2000
measured daily water consumption [kg]
2500
0
500
1000
1500
2000
2500
measured daily water consumption [kg]
Correlation between measured daily water consumption of 500 tomato plants and estimated
water consumption based on transpiration measurement with the BERMONI system (March
to June, n = 109 days).
result: irrigation control with measured leaf transpiration
overflow percentage
1,2
measured overflow [%]
1
0,8
average
overflow: 63 %
0,6
standard
deviation: 16 %
0,4
0,2
10:56
6:10
15:03
12:33
9:56
19:20
15:29
12:15
8:08
15:15
12:03
8:35
15:20
13:02
9:58
17:33
14:20
12:18
13:27
17:52
10:20
9:03
15:40
13:10
13:48
16:50
15:50
12:22
18:03
10:08
14:39
7:59
12:29
13:37
13:15
15:15
0
4 weeks overflow in the hydroponic closed system – target: 60 % overflow
irrigation control: transpiration sum,
strategy: after 40 l transpiration 64 l irrigation
average overflow: 0.63
result: climate control
thermal screen control
discussion: Control of screen opening
using net photosynthesis data
13
250
photosynthesis collector
photosynthesis reference
11
PAR
200
screen closure collektor
screen closure reference
9
18:00
10 % Opening for
descending could air
100
closure do to PAR
5
50
3
0
1
-50
-1
0:00
0:30
1:00
1:30
2:00
2:30
3:00
3:30
4:00
4:30
5:00
5:30
6:00
6:30
7:00
7:30
8:00
8:30
9:00
9:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
19:30
20:00
20:30
21:00
21:30
22:00
22:30
23:00
23:30
net photosynthesis [µmol/m²s]
15:45
-100
-3
-150
-5
-200
screen closure at photosynthesis below 3 µmol/m²s
screen closure [%], radiation [W/m²PAR]]
closure do to photosynthesis
morning opening
7
150
Energy savings in comparison to greenhouse without
screens
Light reduction in comparison to greenhouses without screens
45
33
30
29
28
40
39
34
34
32
29
39
38
37
37
45
43
30
32
28
25
20
20
10
10
0
0
Januar
Februar
März
April
25
20
20
20,6
15
15
14,8
10
10
8,3
6,8
5
5
5,2
4,3
3,1
2 2,7
2
2 2,6
2 2,3
3,2
4,0
2 2,2 2,6
0
Mai
annual light portion %
46
relative light reduction in %
45
40
Energy savings in %
25
50
annual heating costs portion %
50
3,1
0
Januar
Februar
März
April
Mai
45
45
40
40
35
efficiency threshold with light
dependent returns
35
30
30
25
20
25
efficiency threshold with
constant returns
20
15
15
10
10
5
5
0
0
Januar
Februar
März
April
Mai
day-night strategy
efficiency threshold with a benefit above
ratio of energy saving to light reduction
Efficiency with different screen control strategies
1 hour later opening – 1 hour earlier closure
2 hours later opening – 2 hours later closure
dynamic strategy – closure if heating is necessary
Schuch, I., Kläring, H.P., Schmidt, U. Transparente
Energieschirme auch am Tage?, Gemüse - Das Magazin für
den professionellen Gartenbau, 10(49), S 10 - 13
Control of screen opening using net photosynthesis data
Time difference between photosynthesis and radiation caused closure
Radiation at closure time by photosynthesis
14,00%
Minderung Heizenergie, Minderung
Photosynthese
12,00%
2:15
70 W/m²
1:15
50 W/m²
2:55
116 W/m²
Einsparung
Wärmeenergie
relative saving
of the daily
heat consumption
relative reduction
of theder
daily
net photosynthesis
Minderung
Photosynthese
1:42
75 W/m³
10,00%
1:04
46 W/m²
8,00%
0:59
94 W/m²
6,00%
4,00%
2,00%
0,00%
11.3
12.3
14.3
15.3
17.3
Mittelwert
Conclusions and future perspectives
Conclusions:
1. Seasonal continuous measurements of plant transpiration and net
photosynthesis leads to an evaluation of the plant - microclimate interactions
witch are helpful to optimize climate control.
2. Phytometric data can used for process control to save energy and water – this is
an essential prerequisite for introduction of this technology in the greenhouse
automation marked
3. Up to now it seems to be beneficial to control ventilation, fog system, irrigation
and thermal screens with phytometric information’s.
Next Steps:
1. The BERMONIS prototype will produced by a Berlin enterprise (Pronova Limited)
beginning end of 2014.
2. Next year In the Berlin ZINEG project further functions in the process control will
controlled with phytometric information’s (CO2 enrichment, temperature control,
humidity control).
3. As the icing on the cake the phytomonitor concept will extended by a new
developed ethylene gas analyzer to detect plant stress or plant diseases.
heat performance [kW]
100
50
cooling capacity
heating capacity in the collector greenhouse
electrical power for CHP
heating capacity reference greenhouse
overall heating capacity
0
-50
-100
25
100
inside air temperature
22
80
inside relative humidity
70
outside relative humidity
60
21
50
20
40
19
40
30
2
35
30
25
finned pipe condensation
canopy transpiration
VCD g/kg
dew point distance to canopy
1,5
1
20
0,5
15
10
5
0
0
89.9 l
88.6 l
-5
-0,5
-1
dew point distance [°C], VPD [g/kg]
dew point temperature
23
relative humidity [%]
90
canopy leaf temperature
8:10
8:20
8:30
8:40
8:50
9:00
9:10
0:00
9:30
9:40
9:50
10:00
10:10
10:20
10:30
10:40
10:50
11:00
11:10
11:20
11:30
11:40
11:50
12:00
12:10
12:20
12:30
12:40
12:50
13:00
13:10
13:20
13:30
13:40
13:50
14:00
14:10
14:20
14:30
14:40
14:50
15:00
15:10
15:20
15:30
15:40
15:50
16:00
16:10
16:20
16:30
16:40
transpiration and condensation
water flow [l/h]
temperature [°C]
24
Thank you for your attention…
www.zineg.de/?q=en/node/50
www.plantputer.com
Humboldt-Universität zu Berlin
Faculty for Agriculture and Horticulture
Biosystems Engineering Division
Dr. Uwe Schmidt
Professor for Horticultural Engineernig
[email protected]