THE GREEN COLLEGE 1

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

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

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

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

„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

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.

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 (0.5-1.5 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.

Aerial application of modern sensors Measurements are carried out from a specially equipped Piper Pa-23-250 “Aztec” aeroplane. Sensors used: Eagle hyperspectral camera LEICA Leica ALS50-II

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

General technical parameters - 3-6 points/m2 - 600-1200m bandwidth - 2-10 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

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

Classified laser points: terrain surface, vegetation, buildings 13

2.5D rendering of a surface model with classified vegetation coverage 14

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)

Determining individual tree sizes and shapes Height of the highest point: 675.21m ASL Height of the lowest point: 707.87m 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

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

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

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

Biomass estimation based on hyperspectral technology Source: Specim

Characteristic spectra Forest Building Soil Water

Assessing the rate of coverage and the amount of biomass

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

Processing: Image classification

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

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

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

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

Waste as raw material

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

Process optimisation

Recipe testing Relationship between the C/N ratio in fermenting reactor and the biogas yield y = 9,6668x + 17367 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

Releasing of carbon sources

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.)

Microalgae oil Microalgae grow at a much higher rate than land crops. Algae oil yield per area is estimated to range from 4700 to 18000 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

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,

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.

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.

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