Micro climate control in greenhouses based on phytomonitoring data

Transcription

1 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, Berlin, Germany climate control innovations phytomonitoring concept From Phytomonitoring to Phytocontrol prime examples ventilation and fog control irrigation control screen control CO 2 control dehumification control Conclusion Founded by

2 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

3 innovations in climate control disturbation Actuator Process PID controller Sensor control value measurement set value higher control precision by process adapted control algorithms self-learning algorithms for the estimation of P-, I-, D-gain factor disturbation Actuator Process SARA controller Modell control value calculation set value model leaded process control with model adaptation thermo-dynamical models or neuronal network models

4 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: Predictive open loop control with artificial neuronal networks Salazar, R. et. al. 2010, Neuronal Networks Models for Temperature and CO 2 Control, Int. Journal of agricultural Research 5 (4):

5 innovations in climate control disturbation Actuato r Process PID controller ventilation control disturbation Process Sensor control value measurement set value disturbation Actuato r control value Process PID controller heating control Sensor control value measurement set value fusion of different control circuits handling of restrictions intelligent management of the interaction of different technical systems Actuato r Sensor CO2 control PID controller measurement set value disturbation Process control value screen control Actuato r PID controller Sensor measurement set value

6 Working with new control variables air temperature thermal radiation light conditions relative humidity heating system energyscreen fog system relative humidity ventilation air temperature vapour pressure aerial environment transpiration thermal radiation leaf-, fruittemperature plant Growth sap flow light conditions stomatal aperture photosynthesis respiration Artificial light shading CO 2, concentration CO 2 enrichment irrigation CO2/O2 gas exchange Watermanagement rhizosphere environment nutrient supply fertilization

7 AC Power only a few seriously approaches for continuous measurements over the entire cultivation period Solar Panel Adapter 12 V Battery 1 REF 36 LPM Auto PM-48M Photosynthesis Monitor RS232 Auto/Man Pump 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)

8 Introduction - The ZINEG network closed greenhouses maximum greenhouse isolation, integration climate control CO2 neutral heat supply in foliage greenhouses heat protection glass Berlin/Hannover economical evaluation founded by

9 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

10 result: climate control ventilation and fog control with net photosynthesis

11 PAR [W/m²] vapour concentration difference [g/kg] stomatal conductivity [cm/s] light use efficiency [%] SCHMIDT, U., 2004: Decision support for greenhouse climate control using a computerised Mollier Diagram. Acta hort. 654,

12 PAR [W/m²] vapour concentration difference [g/kg] stomatal conductivity [cm/s] light use efficiency [%] SCHMIDT, U., 2004: Decision support for greenhouse climate control using a computerised Mollier Diagram. Acta hort. 654,

13 PAR [W/m²] vapour concentration difference [g/kg] stomatal conductivity [cm/s] light use efficiency [%] SCHMIDT, U., 2004: Decision support for greenhouse climate control using a computerised Mollier Diagram. Acta hort. 654,

14 material and method: estimation of the microclimatic comfort zone method: mollier-plot-analysis by SCHMIDT ventilation set point: 25 C

15 June 13 June 12 June 11 Pannovy 30 C 25 C 20 C Collector 30 C 25 C 20 C Reference Encore 30 C 25 C 20 C 30 C 25 C 20 C Pannovy 30 C 25 C 20 C 30 C 25 C 20 C

16 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,

17 light use efficiency [%]

18 light use efficiency [%]

19 irrigation control

20 Phytomonitoring for water use prediction and irrigation control LT: leaf transpiration PM CT=f(LT;LAI) 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

21 estimated daily water consumption [kg] estimated daily water consumption [kg] result: irrigation control with measured leaf transpiration electronically tilt-tray sensors under the gullies for overflow measurement electronically water meter for water input measurement y = 0,8029x + 129,77 R² = 0, y = 0,7897x + 122,17 R² = 0, measured daily water consumption [kg] 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).

22 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 measured overflow [%] result: irrigation control with measured leaf transpiration 1,2 overflow percentage 1 0,8 0,6 average overflow: 63 % 0,4 0,2 standard deviation: 16 % 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

23 result: climate control thermal screen control

24 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] screen closure [%], radiation [W/m² PAR ]] discussion: Control of screen opening using net photosynthesis data photosynthesis collector 11 photosynthesis reference PAR 200 screen closure collektor 9 screen closure reference morning opening 15:45 18:00 closure do to photosynthesis % Opening for descending could air closure do to PAR screen closure at photosynthesis below 3 µmol/m²s -200

25 ratio of energy saving to light reduction efficiency threshold with a benefit above Energy savings in % annual heating costs portion % relative light reduction in % annual light portion % 50 Energy savings in comparison to greenhouse without screens Light reduction in comparison to greenhouses without screens , , , Januar Februar März April Mai ,8 5,2 3,1 4,3 4,0 3, ,7 3,1 2 2,6 2 2,3 2 2,2 2,6 Januar Februar März April Mai 5 0 Efficiency with different screen control strategies day-night strategy efficiency threshold with light dependent returns hour later opening 1 hour earlier closure 2 hours later opening 2 hours later closure efficiency threshold with constant returns dynamic strategy closure if heating is necessary Januar Februar März April Mai 5 0 Schuch, I., Kläring, H.P., Schmidt, U. Transparente Energieschirme auch am Tage?, Gemüse - Das Magazin für den professionellen Gartenbau, 10(49), S 10-13

26 Control of screen opening using net photosynthesis data Minderung Heizenergie, Minderung Photosynthese Time difference between photosynthesis and radiation caused closure Radiation at closure time by photosynthesis 14,00% 12,00% 10,00% 8,00% 6,00% 2:15 70 W/m² 1:15 50 W/m² 2: W/m² relative saving Einsparung of the daily Wärmeenergie heat consumption relative reduction Minderung of the der daily Photosynthese net photosynthesis 0:59 94 W/m² 1:04 46 W/m² 1:42 75 W/m³ 4,00% 2,00% 0,00% Mittelwert

27 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 Next year In the Berlin ZINEG project further functions in the process control will controlled with phytometric information s (CO 2 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.

28 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] dew point distance [ C], VPD [g/kg] temperature [ C] relative humidity [%] heat performance [kw] cooling capacity heating capacity in the collector greenhouse electrical power for CHP heating capacity reference greenhouse overall heating capacity inside air temperature canopy leaf temperature dew point temperature inside relative humidity outside relative humidity finned pipe condensation canopy transpiration VCD g/kg dew point distance to canopy 89.9 l 88.6 l ,5 1 0,5 0-0,5-1

29 Thank you for your attention Humboldt-Universität zu Berlin Faculty for Agriculture and Horticulture Biosystems Engineering Division Dr. Uwe Schmidt Professor for Horticultural Engineernig