1. THE GREEN COLLEGE 1

2. Centres of biomass research and education
– focus on Gyöngyös
Tibor Bíró
dean
Faculty of Natural Resources Management
and Rural Development

3. Green Energy Programme at the Faculty of Natural Resources Management and Rural Development of the Károly Róbert College
Education:
Engineer of agricultural environment management BSc programme: cultivation and technology aspects of biomass-based energy resources
Waste management technologist: energy recovery from food industry waste and agricultural waste
Assistant bioenergetics engineer: operating tasks related to renewable energies

4. Research: Energy crop plantations Biogas Algae oil
Green Energy Programme at the Faculty of Natural Resources Management and Rural Development of the Károly Róbert College
Research:
Energy crop plantations
Biogas
Algae oil

5. Our resources 80 hectares of energy crop plantations
Laboratory facility
Experimental farms
Unique set of remote sensors
Suitable staff

6. „Woody Energy Programme” of the Károly Róbert College TVK
GIS, remote sensing
soil
hydrology
climate
nature conservation
Land ownership management
Identification of areas suitable for plantations
location-specific range of varieties
Consulting
breeding using genomics tools
Plantation, cultivation, nutrient supply, irrigation, machinery
Agrotechnology
Remote sensing
Monitoring the health of plantations
Estimating biomass
Landscape protection

7. service of green energy
Modern sensors in the
service of green energy
Active sensor: The LIDAR technology can measure vegetation and terrain in 3D fast, in large areas and with a high resolution. Sensing multiple-level reflection allows the identification of the structure of vegetation and the rate of coverage.

8. service of green energy
Modern sensors in the
service of green energy
Passive sensor: The hyperspectral aerial sensors detect several hundred channels, from the visible range to the thermal. In addition to the large number of channels, the high resolution ( m) of shots allows classification on species level. In addition to qualitative assessments, data can be used for estimating plant biomass as the differences due to the amount of biomass can be estimated with good accuracy from the level of photosynthetic activity calculated by combining different wavelengths.

9. Aerial application of modern sensors
Measurements are carried out from a specially equipped
Piper Pa “Aztec” aeroplane.
Sensors used:
Eagle hyperspectral camera
LEICA Leica ALS50-II

10. What is LIDAR?
Emits laser impulses and detects reflected signals to determine the distance of the object scanned
Technology is similar to radar, with radio waves replaced with concentrated light (~laser) of various frequencies
LIDAR works in the ultraviolet, visible and infrared ranges 10

11. General technical parameters
- 3-6 points/m m bandwidth cm vertical and horizontal accuracy - flight speed: 60-75m/s
3D rendering of terrain model based on radar data (SRTM)
3D rendering of terrain model based on LIDAR data 11

12. Orthophoto of a forested sample area
LIDAR image of a forested sample area 12

13. Classified laser points: terrain surface, vegetation, buildings13

14. 2.5D rendering of a surface model with classified vegetation coverage14

15. Forestry applications
Support for single tree cutting for energy production
Optimal stock density
Consideration of health conditions
Long-term planning
Estimating waste from logging (cutting)

16. Determining individual tree sizes and shapes
Height of the highest point:
675.21m ASL
Height of the lowest point: m ASL
Tree height ~ 32.66m
Foliage shape ~ Convex
Volume of foliage ~ 11.55m
These points can be used in analysis directly
In this image I’ve zoomed into the points that describe a riverside tree

17. 3D assessment of the structure of vegetation based on LIDAR impulse intensity17

18. 3d assessment of the structure of vegetation based on LIDAR impulse intensity18

19. Features to be determined by processing LIDAR data
Tree height
Volume of foliage
Vertical foliage profile
Rate of coverage
Biomass (kg/m2)
Foliage density
Digital terrain model (DTM) 19

20. Biomass estimation based on hyperspectral technology
Source: Specim

21. Characteristic spectra
Forest
Building
Soil
Water

22. Assessing the rate of coverage and the amount of biomass

23. Rate of coverage (%) = 157.92*NDVI – 2.37 (R2 = 0.78, n=21)

24. Processing: Image classification

25. Vegetation - 3D structure and biomass
Integrated biomass map
Vegetation - 3D structure and biomass
Upland conifer
Lowland conifer
Northern hardwoods
Aspen/lowland deciduous
Grassland
Agriculture
Wetlands
Open water
Urban/barren
Vegetati on Type
30 kg/m2
Biomass
0 kg/m2

26. R+D activities in the service of Biogas
Pre-treatment of raw materials
Recipe testing
Increasing biological gas yield
Utilisation of fermentation sludge
Desulphurisation

27. Biogas industry Breakthrough opportunities: Cheap raw materials
Process optimisation
Biological yield enhancement

28. Pre-treatment of raw materials
Physical and microbiological pre-treatments
start-up and supplementary inoculants for specific recipes
making the metabolic processes in the hydrolysis phase controllable, re-programming of the catabolic pathways, increasing the efficiency of carbon source utilisation

29. Waste as raw material

30. Tasks in the research for increasing biogas yield
using genomic methods to track the formation and dynamics of biogas-producing consortia
investigating the main metabolic pathways of biogas consortia
improving the tolerance of methanogenic bacteria to environmental stress

31. Process optimisation

32. Recipe testing
Relationship between the C/N ratio in fermenting reactor
and the biogas yield
y = 9,6668x R 2
= 0,2477
14500,00
15500,00
16500,00
17500,00
18500,00
19500,00
20500,00
21500,00
22500,00
23500,00
11,43
11,47
11,51
11,55
11,60
11,63
11,65
11,67
11,69
11,70
11,71
11,72
11,73
11,75
11,77
11,80
11,83
11,85
11,86
11,88
11,96
12,06
12,16
12,21
12,25
12,30
12,32
12,34
12,35
12,36
12,37
12,38
12,40
12,42
12,49
12,56
12,61
12,64
C/N ratio 3

33. Releasing of carbon sources

34. Processing anaerobe sludge (biological sludge)
releasing additional sources of carbon
recycling biological sludge
alternative uses of biological sludge
microbiological treatment to reduce the nitrogen content of anaerobic sludge: metabolising ammonium, nitrite, nitrate using algae, cyanobacteria, then utilising the resulting biomass (cosmetics industry, biodiesel production etc.)

35. Microalgae oil
Microalgae grow at a much higher rate than land crops. Algae oil yield per area is estimated to range from 4700 to m3/km2/year.
The conditions in Hungary are suitable for the production of algae oil, which could even be combined with the utilisation of thermal water

36. Competences of KRC in the field of algae oil production
Reliable theoretical calculations:
efficiency calculations for the production of algae oil under special conditions, using solar radiation
detailed cost efficiency calculations,
cost calculations for laboratory-size and industrial size systems,
development of mathematical models and performance of bioreactor simulations via the programmed adjustment of essential parameters,

37. Competences of KRC in the field of algae oil production
Strong background in microbiology and molecular biology:
well-equipped microbiology laboratory,
access to special algae catalogues (over 1000 strains),
the ability to identify and monitor the significant metabolic processes of oil-producing strains,
we are using the newest achievements in genomics and information technology to improve efficiency,
we can re-programme the metabolic pathways involved in fatty acid production and catabolism.

38. Competences of KRC in the field of algae oil production
Development of technology, designing reactors
design of automated photo-bioreactors,
development of sensor systems, communication protocols and regulating processes for bioreactors optimised for biodiesel production.

39. Thank you for your attention