Determination of Potential of Biomass Energy and Logistics Infrastructure

Transcription

1 Determination of Potential of Biomass Energy and Logistics Infrastructure * Mehmet Metin ÖZGÜVEN 1, Tekin SUSAM 2, Sefa TARHAN 3 1 Gaziosmanpaşa University Agricultural Faculty Department of Biosystems Engineering, Tokat, TURKEY 2 Gaziosmanpaşa University, Faculty of Engineering and Natural Science, Departmant of Topograhical Engineering, Tokat, TURKEY 3 Gaziosmanpaşa University, Faculty of Engineering and Natural Science, Departmant of Mechatronics Engineering, Tokat, TURKEY metin.ozguven@gop.edu.tr Abstract: Energy needs in Turkey increase depending on population growth and the development of industry and a significant portion of the energy produced are provided from imported energy sources; efforts to obtain energy from renewable energy sources, especially biomass have been accelerated. Biomass resources are used to obtain energy with direct combustion and biofuel production. Many studies to determine the potential of biomass have been accomplished In Turkey having rich biomass resources and in almost all of these studies, various calculations are made using agricultural production statistics data. In addition, studies made to determine of agricultural, forested areas using remote sensing and geographic information systems techniques have been conducted. In this study, utilization methods of biomass resources as energy were described, application possibilities of remote sensing and geographic information system techniques to determine of biomass potential and logistics infrastructure were described. Effective biomass logistic chain and appropriate location of biomass conversion facilities should be determined by using remote sensing and geographic information system. Key words: Biomass, potential of biomass, logistics, remote sensing, geographic information system INTRODUCTION Considering our country's energy sector growth figures, the development seems to be quite high in comparison to the developed countries. In 2010 per capita energy consumption, when compared to the previous year, increased to 1482 kep by 1.3%, electricity consumption has risen to 2,347 kwh by 8.56% (Anonymous, 2011). Biomass resources are comprised of the unfossilised substances whose origins are the living creatures like agricultural, animal and urban wastes, specially grown agricultural, forest and aquatic products (Ergüneş and Tarhan, 2009). Used for energy purposes, the most important source of biomass is materials like timber, poles, industrial wood, pulpwood, fibre-chip wood, poles, rods, and firewood obtained from trees, shrubs and bushes of all kinds in the forests. Logistics is a planning process which is carried out in order for its movement from the source of raw material to the end point of the product consumption in the supply chain to be more economical and quickly conveyed. Logistics, in procuring biomass energy contains work processes of all kinds like collection, loading, unloading, transporting, and storing of the biomass sources and also constitutes the most important cost item in obtaining energy from biomass. Biomass logistics studies are comprised of the detection, renewability, and distance of resources, determination of raw material requirements, order process, and transportation stages. These studies, by covering such stages of all kinds as the determination of the location of the facility to be set up or which processes will be implemented before the arrival of the raw material at the facility, will make contribution to the formation of the optimal model to obtain the highest economic benefits from the biomass energy. 122

2 Conversion Techniques Used in Biomass Energy Production Energy in the forms of solid, liquid and gas can be produced from Biomass through various conversion techniques. These conversion techniques are as follows: Direct Combustion Direct Combustion of Biomass has a low heating value due to low mass density and high moisture content. Furthermore, conveyance and storing problem of Biomass come into being. Pyrolysis Pyrolysis is a process in which Biomass is decomposed through thermochemical reaction under high temperature in the absence of oxygen. Pyrolysis processes result in gases, various liquid products and solids materials with high carbon content. Gasification Gasification is a process in which biomasscontaining solids is converted to combustible gases through thermochemical decomposition under high temperature. Hydrogen, methane, carbon monoxide, carbon dioxide and nitrogen gases are obtained at the end of this process. Biophotolysis Biophotolysis is a disruption process of some algae into hydrogen and oxygen with the help of solar energy. Anaerobic Digestion Anaerobic digestion is process in which organic and inorganic substances are disrupted by microorganisms under anaerobic conditions. As a result of the digestion process, manure, methane gas and carbon dioxide come into being. Fermentation Fermentation is a process of conversion of carbohydrate, protein and fat, by being disrupted, to CO2, acetic acid and soluble volatile organic substances through the action of enzymes produced by certain microorganisms. Carbonization Carbonization is a process in which a variety of gases is released through that organic substances like wood undergo chemical disruption. Briquetting and Pelletizing Briquetting and pelletizing is a process of increasing the mass density in order to facilitate storing and conveyance. Biomass Potential Determination Methods Classical Techniques of Biomass Potential Determination The statistical data have been used in many studies in order to determine the biomass potential in our country and biomass potential has been determined by performing various calculations with these statistical data. In their review article Sakıcı et al. (2004), have reported that the classical methods used to estimate the biomass amount related to trees are Unit Area Method, Mean Tree Method and Regression Method. In Unit Area Method, through determining biomass of all the trees in the sample area and converting these to hectare values; in Mean Tree Method, through determining the mean tree that represent the sample area and calculating the biomass amount related to this tree and multiplying this amount by the number of trees found in the area, converting the results into hectare values; in Regression Method, by using separate regression models of the biomass amounts related to the roots, trunk, branches, leaves and barks of the trees, they also stated that they were usually estimated as being breast diameter or the function of breast diameter and tree height. Using the statistical data related to the biomass potential of our country, Balat (2005) has stated that the biomass potential as 32 MTEP/year and the total convertible bioenergy potential as MTEP/year in his study. By obtaining the statistical data, regarding the total area in which the cereals, pulses, industrial crops, oil seeds and tuberous plants are cultivated, Koçer and Ünlü (2007), from TSI (TÜİK) calculated the mean dry biomass amount pursuant to the total cultivated area as ton and also calculated the mean 123

3 heating value of the dry biomass amount, which depends on this. Kuş (2009), estimated bioethanol potential for Sakarya agricultural areas of 109,625 ha from wheat and sugar beet. Wheat yield is taken as 8 tons/ha/year and sugar beet yield as 53 tons/ha/year, m 3 of bioethanol could be produced from 1 ton of wheat and m 3 of bioethanol from 1 ton of sugar beet. As a result 294,672 lt/year of bioethanol could be obtained from wheat and 627,493 lt/year of bioethanol from sugar beet. In their study, Kurt and Koçer (2011) calculated that an average of 27.5 tons of dry biomass could be obtained from a hectare of land per year and calculated the heating value average by taking the heating value of the dry biomass as an average of 4000 kcal/kg. Karayılmaz et al. (2011), in their review article, reported that an average of tons of wet or tons of dry biomass per year could be obtained from a mid-yield land with an average of annual precipitation amounting to 250 mm and the productivity could rise to a level of 40 tons of biomass per hectare in sub-tropical regions that is more appropriate with respect to climatic conditions. In their studies, Topal and Topal (2012a, 2012b) summarized their retrieval step of TSI data in the Figure 1, taking into account the field crops such as pulses, industrial crops, cereals, forage crops, tuberous plants and oil seeds. The remote sensing techniques can be utilized for the determination of the biomass resource amounts by considering agricultural, pastoral and forest areas. In addition, biomass logistics and cost analysis depending on the distance between biomass energy production plant, and biomass production can be determined by using remote sensing and geographic information system techniques with the purpose of effective usage of biomass resources. Using 2003 Landsat-7 ETM satellite images and GIS in accordance with the CORINE classification system, Urfa and Altınbaş (2006), classified Bakırçay delta and its surrounding in Izmir province into 5 main groups. They are artificial-cultural surfaces, agricultural areas, forests/semi-natural areas, wetlands and water surfaces. They are further classified into 10 sub-groups; continuous urban construction, mine-dumps and infrastructure facilities, arable fields, perennial vegetation, pasture, multicultural agricultural areas, forests, shrubs and grasslands, coastal wetlands and marine water. In their analysis 14 earth elements were considered (Figure2). Figure 1. Data retrieval steps Biomass Potential Determination from Satellite Images Figure 2. Study area corine classification system result map 124

4 Using Geographical Information System with the help of Landsat TM 2006 image, In their study, Genç and Bostancı (2007), determined the change of land use and vegetation between 1987 and 2006 in TROIA National Park within the boundaries of Canakkale province (Table 1). Figure 3 shows the land use and vegetation map in 1987, Figure 4 shows land use and vegetation belonging to the year of Table 1. Land classifications and coverage areas Class 1987 (Ha) 2006 (Ha) Active Agriculture Grassland Forest Water Total Figure 4. Land use and vegetation map in 2006 Classifying Landsat satellite images belonging to the years of 2006, 2007, and 2008, Genç et al. (2010), obtained the vegetation map consisting of Forest and Scrub, Meadows, Agricultural and Residential areas and Bare Lands of Bozcaada district (Figure 5). Figure 3. Land use and vegetation map in 1987 Figure 5. Vegetation maps obtained from the classification of the images 125

5 Günlü et al. (2011), performing Landsat 7 ETM+ satellite image-controlled classification on the forest areas within the boundaries of Kastamonu Regional Directorate of Forestry, Cide Forestry Administration, Kızılcasu Operational Chieftainship and determined 5 land use classifications including coniferous forests, deciduous forests, mixed forests, degraded forests, and open-agricultural forests; 4 classes of development ages; 4 classes of closed forests (Figure 6, 7, 8). Figure 7. Development age classification a) Stand type map b) Landsat 7 ETM+ satellite images Figure 6. Land use classification a) Stand tyoe map b) Landsat 7 ETM+ satellite images Figure 8. Closed classifications a) Stand type map b) Landsat 7 ETM+ satellite images Determination of Logistics Infrastructure Depending on what purpose the biomass will be used for, during the modelling with the purpose of setting up biomass energy production plant, the 126

6 issues to be taken into account can be classified as follows (Sokhansanj et al., 2006; Ravula et al., 2008; Eker et al., 2010): 1.Condition of biomass (moisture content, productivity characteristics, time (harvest time for agricultural products, etc.) and so on. 2.Performance of the facility to be constructed and tool /machine type to be purchased, 3.Capacity of storing building and job performance, 4.Collection/Conveyance vehicles' performance collection/conveyance costs 5.Process being done (briquetting, pelletizing, baling, chipping, drying, etc.) 6.Climatic conditions, 7.Change of dry substance amount, 8.Economic analysis (cost analysis, sensitivity analysis, etc.) Sokhansanj et al., (2006), simulated biomass flow through a collection network to develop a biomass logistics model as seen in Figure 9. Figure 9. Simulation of biomass flow through a collection program RESULTS and DISCUSSION Remote sensing and geographic information system have been increasingly used in agriculture and industry in recent years. The determination of effective biomass logistics and appropriate location of biomass conversion facilities should be done by using Remote sensing and geographic information system. Otherwise, the determination of biomass potential from statistical data cannot guarantee the cost effectiveness and sustainability of biomass energy production. REFERENCES Anonim, Elektrik Üretim Sektör Raporu. Elektrik Üretim Anonim Şirketi. Balat, M., Use Of Biomass Sources For Energy In Turkey and a View to Biomass Potential, Biomass and Bioenergy, 29, s Eker, M., Çoban, H.O. ve Alkan, H., Hasat Artıkları Tedarik Zincirine Yönelik Sistem Tasarımı, III. Ulusal Karadeniz Ormancılık Kongresi, Mayıs, Bildiriler Kitabı Cilt:II, s , Artvin. Ergüneş, G. ve Tarhan, S., Tarım Makinaları. Nobel Yayınları, Ankara. Genç, L., Bostancı, Y.B., TROİA Milli Parkı Arazi Kullanım ve Bitki Örtüsü Değişiminin Uzaktan Algılama ve Coğrafi Bilgi Sistemi Yardımıyla Belirlenmesi, Tekirdağ Ziraat Fakültesi Dergisi, 4(1), s Genç, L., Saçan, M., Turhan, H. ve Aşar, B., Arazi Örtüsünün Landsat TM Uydu Görüntüleri Yardımıyla Belirlenmesi. Tarım Bilimleri Dergisi. 16, s Günlü, A., Keleş, S., Kadıoğulları, A.İ. ve Başkent, E.Z., Landsat 7 ETM+ Uydu Görüntüsü Yardımıyla Arazi Kullanımı, Meşcere Gelişim Çağı ve Meşcere Kapalılığın Tahmin Edilmesi; Kastamonu-Kızılcasu İşletme Şefliği Örneği. I.Ulusal Akdeniz Orman ve Çevre Sempozyumu, Ekim 2011, Kahramanmaraş. Karayılmazlar, S., Saraçoğlu, N., Çabuk, Y. ve Kurt, R., Biyokütlenin Türkiye de Enerji Üretiminde Değerlendirilmesi, Bartın Üniversitesi, Orman Fakültesi Dergisi, Cilt: 13, Sayı: 19, s Koçer, N. N., Ünlü, A., Doğu Anadolu Bölgesinin Biyokütle Potansiyeli ve Enerji Üretimi, Doğu Anadolu Bölgesi Araştırmaları, s Kurt, G. ve Koçer, N.N., Malatya İlinin Biyokütle Potansiyeli ve Enerji Üretimi. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 26(3), s Kuş, U., Sakarya Tarım Üretim Potansiyelinin Bio-Yakıt Olarak Değerlendirme İmkanları. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi. Ravula, P.P., Grisso, R.D., Cundiff, J.S., Cotton logistics as a model for a biomass transportation system. Biomass and Bioenergy, 32: Sakıcı, O.E., Ercanlı, İ. ve Kahraman, A., Klasik Biyokütle Tahmin Yöntemleri ve Yeni Yaklaşımlar. Kafkas Üniversitesi Artvin Orman Fakültesi Dergisi, 3-4, s Sokhansanj, S., Kumar, A., Turhollow, A.F., Development And Implementation of Integrated Biomass Supply Analysis and Logistics Model (IBSAL). Biomass and Bioenergy, Topal, M. ve Topal, E.I.A., 2012a. Ürün Bitkilerinden Yenilenebilir Enerji Kaynağı Biyokütle Enerjisi Potansiyelinin Belirlenmesi: Afyonkarahisar İli Örneği ( ). AKÜ FEBİD, 12, s Topal, M. ve Topal, E.I.A., 2012b. Elazığ İli Biyokütle Enerji Potansiyeli Üzerine: Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi 3 (2), s Urfalı, N.E. ve Altınbaş, Ü., Yeryüzü Kaynak Potansiyelinin Uydu Verileri Bağlamında CORINE Sistemine Göre Belirlenmesi Üzerine Bir Çalışma, Ege Üniv. Ziraat Fak. Derg., 43 (3), s