Python Notes for mca i year students osmania university.docx
Agricultural Bioenergy Non Food Options_Brussels2008
1. Non-Food Options at the Farm Level Agricultural Bioenergy Options in ENFA * Chrystalyn Ivie Ramos, Uwe Schneider + Edward Smeets, Iris Lewandowski, André Faaij * Research Unit Sustainability and Global Change, Hamburg University + Department of Science, Technology and Society, Utrecht University Final Meeting ENFA, 23-24 April 2008, Brussels, Belgium Copernicus Institute Sustainable Development and Innovation
2. Table 1: Food/Non-Food Production Lines in ENFA Biogas Paper, Bioelectricity Bioheat, Biofuels (Methanol, FT-diesel, Hydrogen) Biodiesel Bioethanol Biomaterials Bioenergy generation Hot water energy Biofuels, Bioelectricity, Bioheat, Biomaterials Non-food product applications UHH, UHOH, INRA Methane Manure treatment UHH Livestock options OCE, ECBREC, JR Pulp, Timber Forest Management UHOH, ECBREC, INRA, JR Oilseeds Rape, Sunflower UHOH, UUTR, INRA, JR Oilseeds Maize, Sugar beet, Potatoes A&F Fiber and shive products Hemp, Flax, Kenaf SLU, ECBREC, INRA, AUA, JR Pellets, Chips Willow, Poplar, Eucalyptus, Giant reed, Cardoon SLU Pellets and briquettes Red canary grass UUTR, A&F, INRA, IGER, AUA, TCD, JR Pellets, Bales Miscanthus, Switchgrass Contributor(s) Non-food product options Land use options
3.
4.
5. Table 2: Miscanthus & switchgrass applications selected Bioelectricity, Bioheat, Biofuels, Biomaterials Non-food product applications UUTR, A&F, INRA, IGER, AUA, TCD, JR Pellets, Bales Miscanthus, Switchgrass Contributor(s) Non-food product options Land use options Poly Trimethylene Tetraphalate Fischer-Tropsch diesel Gasification and co-combustion with coal Medium Density Fiberboard Small scale heating Poly Lactic Acid Hydrogen Indirect co-combustion with coal Ethylene Ethanol Integrated gasification and combined cycle Dimethyl Ether Methanol Combustion combined heat and power Biomaterials Biofuels Bioenergy
6.
7.
8. Miscanthus and switchgrass production – an overview Table 3: Number of application of production stages over the m iscanthus and switchgrass lifetimes* * Assumed to be applicable in all EU regions
9.
10.
11.
12.
13.
14.
15.
16. Figure 3: Total production, storage & transportation costs of miscanthus and switchgrass yields Yield (ton/ha/yr) 0 20 40 60 80 100 SVE LAT LIT POL HUN SVA CZE MAL EST BEL FRA AUS ITA GER LUX UKI SPA POR GRE SWE IRE NET FIN DEN Euro/ton Transportation Storage Production 20 15 10 5 0 25 0 20 40 60 80 100 120 SVE LAT LIT POL HUN SVA CZE MAL EST BEL FRA AUS ITA GER LUX UKI SPA POR GRE IRE SWE NET FIN DEN Euro/ton Transportation Storage Production 20 10 5 0 25 15 0
17.
18.
19. Figure 5: Chemical production costs (Biomaterials) Production costs conventional processes (Euro/ton) Ethylene PET PTT 724 1200 1177 0 500 1000 1500 2000 2500 BELG DENM FINL FRAN GERM IREL ITAL LITH POLA PORT SLVN SWED UNIK Euro/ton Ethylene now Ethylene future PLA now PLA future PTT now PTT future
20. Figure 6: Biofuel production costs 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 now future now future now future now future now future now future CI CI CI, hybrid CI, hybrid SI SI SI, hybrid SI, hybrid CI CI CI, hybrid CI, hybrid FT diesel Ethanol DME Euro/km Estonia Germany Italy Latvia Lithuania Slovenia United Kingdom 0.00 0.01 0.01 0.02 0.02 0.03 0.03 0.04 0.04 now future now future now future now future now future now future now future SI SI SI, hyrbrid SI, hybrid CI CI CI, hyrbrid CI, hybrid FC, hybrid FC, hybrid FC FC FC, hybrid FC, hybrid Gasoline Diesel Methanol Hydrogen Euro/km Estonia Germany Italy Latvia Lithuania Slovenia United Kingdom
21. Results: Environmental performance evaluation P = production, PL = Poland - Lubelski, HU = Hungary - Del-Dunantal, UK = United Kingdom - Devon, IT = Italy – Lombardia, LI = Lithuania, M = miscanthus, S = switchgrass, B = baled, C = chopped. Figure 7: GHG emissions from miscanthus production 0 10 20 30 40 50 60 70 80 90 100 PL- M-B- 2004 PL- M-B- 2030 PL- M-C- 2004 PL- M-C- 2030 HU- M-B- 2004 HU- M-B- 2030 HU- M-C- 2004 HU- M-C- 2030 UK- M-B- 2004 UK- M-B- 2030 UK- M-C- 2004 UK- M-C- 2030 IT- M-B- 2004 IT- M-B- 2030 IT- M-C- 2004 IT- M-C- 2030 LI- M-B- 2004 LI- M-B- 2030 LI- M-C- 2004 LI- M-C- 2030 Transport Unloading Storage P - Machines production P - Machines use P - N2O N fertilizer P - Fertilizers and agrochem prod. P - Planting material 3.9 2.8 1.7 5.0 0 2.2 kg CO2 eq/ GJ 5.6 4.4 3.3 1.1 0.6 kg CO 2 eq/ton
28. Figure 13: Input data parameters for different bioenergy options
Editor's Notes
landless and pastoral production system are also possible, with various mixes of feed inputs. Within each system there are also differences in efficiency possible, due to differences in the efficiency and state of technology applied. The demand for pastureland is compared with the present pasture land use to calculate a shortage or surplus of pasture land. The demand for feed is added up with the demand for food crops and is translated into land use based on yields and suitable areas for crop production based on the level of agricultural technology and management applied. The result of this calculations is a surplus or shortage of cropland. In case of surplus cropland, a bioenergy production potential from bioenergy crops is estimated based the yields for woody bioenergy crops (such as poplar, eucalyptus, willow). The bioenergy production from surplus forest growth is estimated by comparison of the supply and demand for wood. The demand for wood is subdevided into fuelwood and industrial roundwood. The supply of wood comes from natural forest growth and plantations. For both the supply and demand various scenarios are included and a surplus of shortage of wood can be calculated. In addition, the harvesting and processing of food in each region generates harvesting and processing residues which can also be used as a source of bioenergy. Various studies indicate however, that the potentials from specialised bioenergy crops on surplus agricultural land has the largest potential. Therefor the focus in this study is on agricultural land use.
Many combinations of scenarios for various factors included in this study are possible. This table shows results for 4 scenarios in which the projected demand for food is met globally. Regions with a food deficit are compensated by means of imports from other regions. In all four scenarios a high fce is used (HERDEFINIEREN), a mixed or landless animal production system, a high level of technology for crop production, and rain-fed or irrigated agriculture. The data show that the largest potentials for bioenergy crops can be found in the developing regions, particularly sub-Saharan Africa and Latin America. Of the transition regions the CIS & Baltic States has a considerable potential, even in least efficient of the four scenarios. The data also indicate the impact of irrigation, since the global bioenergy production potential increases from 215 to 455 EJ and the impact of a landless industrialised production system instead of a mixed production system (455 tp 1101 EJ).
