Classification of energy models for bioenergy
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The overall target of his research is to provide information that will aid in the design of novel therapeutic strategies aimed at the prevention and/or treatment of these complicated infections. To achieve this objective, they are utilizing new technology, including Proteomics, Immunoproteomics, Mass Spectrometry, Next Generation Sequencing, Tetramers, Real-time PCR, Immunohistochemistry and Bioinformatic and Flow Cytometry Analyses to dissect the host-pathogen interactions in single or combined infections. Dr. El-Ashram's laboratory deciphers the formation and evolution of host specialization in the foodborne illnesses, such as Salmonella spp., Clostridium perfringens, Campylobacter jejuni and Bacillus cereus by building a genome-based phylogeny and studying the Whole genome sequencing (WGS) as an effective and rapid surveillance tool of foodborne disease.",institutionString:"Foshan University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Foshan University",institutionURL:null,country:{name:"China"}}}],coeditorOne:{id:"73465",title:"Dr.",name:"Guillermo",middleName:null,surname:"Téllez",slug:"guillermo-tellez",fullName:"Guillermo Téllez",profilePictureURL:"https://mts.intechopen.com/storage/users/73465/images/system/73465.jpg",biography:"Guillermo Tellez-Isaias was born in Mexico City, in 1963. 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The main research trends of biomass processing, decoupling of treatment and energy conversion, integration into existing infrastructures and energy systems, and optimal location and sizing of bioenergy facilities are reviewed. For this purpose, a description of supply chains modelling and research trends, technical options and related cost figures for the various steps of the biomass supply chains are overviewed. Moreover, the opportunities to integrate bioenergy into existing energy systems are explored, investigating the use of biofuels in combination with fossil fuels into existing plants and networks. Finally, the main research trends in the optimization of scale and location of the different steps of bioenergy routes are overviewed.
The term “biomass” includes several typologies of organic based materials that can be processed in a variety of methods to produce biofuels and bio-products suitable for several markets, such as energy, industry and food. An overview of bioenergy pathways is reported in Figure 1.
When evaluating bioenergy routes, a system perspective has to be taken, encompassing components such as biomass resource, supply management, processing and conversion systems, energy services. In fact, developing sustainable bioenergy from a economic, environmental and social point of view requires an optimization of the structure and functioning of the supply chain/networks, adjusted to the specific conditions of the production sytems (climate and topology, feedstock, technologies, infrastructures, energy end uses, etc). Steps such as biomass harvesting, storage, refining and transport are particularly relevant, and should be facilitated by suitable logistics of supply chains and operations management techniques.
Bioenergy models, as energy models in general, are useful in problems such as projecting future energy demand and supply, assessing the impacts of different energy technologies and energy efficiency measures, optimizing the operations of energy generators. In recent years, the total number of available energy models has grown tremendously, and various classification schemes that provide insight in the differences and similarities between energy models are available in literature, as reported in Table 1 [1-3]. One of the problems with classifying energy models is that there are many possible categories, while there are only few models that fit into one distinct category. In general, model design requires a trade-off between representational fidelity, model performance, and flexibility to multiple contexts. It is also evident that there is no energy tool that addresses all issues, but instead the ‘ideal’ energy tool is highly dependent on the specific objectives that must be fulfilled.
Simplified bioenergy conversion systems patrhways
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
1. Purposes of Energy Models | \n\t\t\tGeneral: potentials assessment, forecasting Specific: energy demand, biomass supply, impacts, appraisal, integrated approach, modular build-up | \n\t\t
2. The Model Structure: Internal & External Assumptions | \n\t\t\tDegree of endogenization, description of non-energy sectors, description end-uses, description supply | \n\t\t
3. The Analytical Approach | \n\t\t\tTop-Down or Bottom-Up | \n\t\t
4. The Underlying Methodology | \n\t\t\tEconometric, Macro-Economic, Economic Equilibrium, Optimization, Simulation, Spreadsheet/Toolbox, Backcasting, Multi-Criteria | \n\t\t
5. The Mathematical Approach | \n\t\t\tLinear programming, mixed-integer programming, dynamic programming | \n\t\t
6. Geographical Coverage | \n\t\t\tGlobal, Regional, National, Local, or Project | \n\t\t
7. Sectoral Coverage | \n\t\t\tEnergy sectors or overall economy | \n\t\t
8. The Time Horizon | \n\t\t\tShort, Medium, Long Term | \n\t\t
9. Data Requirements | \n\t\t\tQualitative, quantitative, aggregated/disaggregated | \n\t\t
Classification of energy models for bioenergy
In Table 2 the key factors in bioenergy modelling and biomass supply chains optimization are proposed. In particular, these factors include: (i) the biomass/biofuel chemical-physical properties (moisture, bulk density, LHV, ashes, metal contents, total solids and volatile solids percentages, etc), processing/handling properties (hydrofobicy, storability, grinding, odours, etc) and their influence on transport, storage, drying, conditioning and processing steps, (ii) the biomass seasonality and economic factors such as the relationships between quantity and timing of withdrawal and unitary supply costs. The integration of GIS based tools allows to assess the location over the territory of biomass potentials, transport, storage and processing infrastructures, and final energy demand sites. When estimating biomass potentials in bioenergy models, the factors that are commonly taken in account are the land uses, existing and competing uses of biomass, yield estimates and influence of environmental conditions (such as weather conditions). Moreover, sustainability issues such as direct and indirect land use change, energy inputs in biomass production, harvesting and processing steps and food vs no-food dynamics should also be accounted for. Logistics and infrastructure aspects are also crucial factors. In particular, both the various biomass/biofuel transport modes (ship, road, rail) and biofuel/energy distribution options (pipelines, networks, road) should be taken in account. Moreover, biomass storage and processing infrastructures should be considered, both in the case of existing and new facilities. In the processing and energy conversion steps, both the biomass to biofuel and the biofuel to energy technologies should be modelled. In order to take in account the trade-offs between large/small biomass supply radius (and related transport costs) and large/small biomass processing and conversion facilities, including the potentials of decentralized small scale plants, factors such as scale economies and influence of size on process efficiencies at various conversion technologies should be considered. Moreover, the presence of existing energy infrastructures and the options for biomass co-refining or biomass co-firing in existing fossil fuel plants should be considered, in order to evaluate the opportunities of integration of bioenergy into existing energy systems. Bioenergy modelling should also take in account the options of coupling vs decoupling of processing and energy confersion plants, as discussed in next section. When investigating these integration opportunities, an accurate modelling of biofuel properties and their suitability for dual-fuelling in conventional plants is particularly important. Finally, in order to favourite bioenergy plants locations near to the energy demand, thus maximizing the energy, environmental and economic benefits of these routes, a proper modelling of the energy demand and its suitability for biomass/biofuel uptake is very important. The assessment of potential energy demand regards both stationary applications (heat/cool/power) and fuels for transports. In the first case, the optimization of biomass fired cogeneration or trigeneration (heat/cool/power) plants (in terms of size, locations and technologies) requires, other than the previously mentioned factors, a proper modelling of: (i) energy demand patterns (daily and seasonal variation of energy demand), (ii) quality of heat demand (temperature of heat/cool required), (iii) existing energy supply systems and related costs (baseline scenarios), (iv) subsidy regimes for bioenergy.
In order to address the specific issues of bioenergy, several methods have been used to model and analyse different aspects of the agricultural and forestry biomass logistics system. A number of basic models have been developed in literature to calculate the costs and compare different handling chains and strategies [4-6]. The recent development of advanced computational tools strongly contributed to the improvement of mathematical models for analysis and optimization of such complex supply and logistic systems [7-12], even if the contribution of these methods in biomass logistics could be limited by the high complexity and dynamic environment of bioenergy.
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
Temporal biomass availability (seasonality) | \n\t\t\tBiomass supply location over the territory | \n\t\t\tTransport systems | \n\t\t\tBiomass to biofuel technologies | \n\t\t\tEnd use typology | \n\t\t
Biomass quality | \n\t\t\tAccessibility issues and available transport modes | \n\t\t\tStorage and processing infrastructures | \n\t\t\tBiofuel to energy technologies | \n\t\t\tBaseline energy scenario | \n\t\t
Handling properties | \n\t\t\tLand uses and biomass yields estimates | \n\t\t\tEnergy infrastructures and integration options (district heating, gas networks, pipelines) | \n\t\t\tEconomies of scale, efficiencies | \n\t\t\tEnergy demand patterns | \n\t\t
cost vs quantity biomass | \n\t\t\tinfluence of environmental conditions | \n\t\t\t\n\t\t\t | Processing- conversion coupling vs decoupling | \n\t\t\tQuality of energy demand | \n\t\t
\n\t\t\t | Alternative and competing uses of biomass | \n\t\t\t\n\t\t\t | Biofuel suitability for conversion processes | \n\t\t\tSubsidy regimes for bioenergy | \n\t\t
Classification of key factors in bioenergy modelling
Moreover, although many researches have an energy system approach, few actually use models that account for the many trade offs and the alternative handling options in the design of whole biomass supply chains. A detailed dynamic simulation program for harvesting, storage, pre-processing and transport of biomass, the IBSAL model, is proposed in [13]. It assumes time and space dependent availability of biomass under the influence of weather conditions and predicts the number, size and location of equipment needed to meet a certain demand. It also calculates the biomass supply costs, energy inputs and emissions, taking in account factors such as the operational parameters of the machines and storage constraints. One of the major innovations consists on the use of non-linear equations to describe these dependencies, e.g. a third-degree polynomial to represent the moisture content as a function of number of days since the start of harvest, or a gamma distribution to simulate the time dependent biomass availability during the harvesting period. However, the methodology is applied to corn stover supply and the implementation to different typologies of feedstocks and agricultural machinery systems would require specific experimental data to inform the model. Moreover, the model is only focused on the supply side and does not include any biomass to energy conversion process or final end uses. To partially overcome these limits, an evolution of the IBSAL model is proposed in [14]. The improved model assesses the logistics of multi-biomass supply and related storage issues to fed a cellulosic ethanol production plant, by a stochastic model with variable input data, such as weather, yields and machine breakdowns. The specific research problem is, in this case, to evaluate how the feedstocks daily demand of the plant can be met throughout the year, what is the cost of the agricultural logistic system, and what are the possible bottlenecks of the supply chains. However, the research does not propose an explicit storage and transport optimization strategy, that could be useful in order to minimize the supply area to meet a given demand, define the optimal location and sizing of storage facilities or scheduling for transport operations. Moreover, the research is focused on a single end-user facility and tailored for a very large straw supply chain and ethanol plant (capacity of 70 million litres/year). Specific issues arising from dispersed and small scale farming techniques, tortuosity of transport networks, land accessibility and ground slope, different storage techniques or other techno-economic factors should be captured when implementing this approach in different agricultural scenarios.
In [12] the storage and transport issues of biomass are assessed and the application to relevant case studies is proposed. In particular, the storage problem and the advantages of a multi-biomass supply chain on the logistic costs are evaluated. The use of intermediate storage locations between the fields and the power plant is often required for several logistic, economic, agronomic and environmental reasons. On the other side, the option of settling the storage facility next to the biomass power plant requires a storage layout with biomass drying capability using dumped heat from the power plant. This concept aims at reducing faster the biomass moisture content and prevents material decomposition as well as fungus and spores formation. In [12] three biomass storage solutions are compared, in terms of total system cost. The concept of multi-biomass is adopted in its simplest form, since two locally available biomass types are considered. The biomass supply chain modelling considers the seasonal availability of the resource, which requires very large storage of biomass for a significant time period, if year-round operation of the power plant is desired. The limited time frame for collecting a large amount of biomass leads also to significant seasonal need of resources, both equipment and workforce. This seasonal demand may increase the cost of obtaining these resources, while leading to suboptimal utilization of resources, particularly of the storage space. The multi-biomass approach may reduce these problems significantly, if the biomass availability is properly shifted over the time. Another characteristic of the biomass supply chain is that it has to deal with low-density materials. As a result, there is increased need for transportation and handling equipment, as well as storage space. This problem is enhanced by the low heating value, which is partly due to the moisture of most agricultural biomass types. The low density of biomass increases further the cost of collection, handling, transport and storage stages of the supply chain. Finally, several biomass types require customized collection and handling equipment, leading to a complicated structure of the supply chain.
In [15], a linear mixed-integer model is proposed, that includes resources, handling/processing, storage and end uses. It is based on the wider
In [17] another methodology for optimization of agricultural supply chains by dynamic programming is described, to find the lowest cost from harvest to end use. The model explicitly deals with the product properties (quality and appearance), which are influenced by handling, processing, transport and storage actions. In particular, agricultural commodities are described according to the appearance states (describing if a product is (un)packed, (un)wrapped, (un)labelled or cut into pieces) and quality states (describing the quality which can be expressed as microorganism infestation, ripeness, moisture content, colour, taste). The types of actions in agrichains are thus: i) handling (actions which modify the appearance states of a product, such as wrapping, cutting and labelling); ii) processing (actions which modify the quality states of a product, such as cooling and drying); iii) transport and storage (actions which alter the quality states of a product. Chain optimisation refers to the construction of routes defining which actors should perform which actions (handling, processing, transportation and storage) at which process conditions, in order to achieve minimum total chain costs while achieving targets.
Another MILP model for the optimal design and operation of biofuel supply chains is proposed in [18] and applied to biodiesel supply chains in Greece. The model incorporates both the optimization of raw materials-feedstocks and biofuel production plants location. It includes the possibility to choose between the domestic biomass production and the import of biomass and-or biofuels to meet given bioenergy targets. However, the model is tailored for a single biofuel production process, it does not take in account storage, transport and environmental issues and costs and it represents the demand side as a fixed quantity of biodiesel to be produced in the whole investigation area.
The work presented in [19] describes an environmental decision support system based on three modules: a GIS-based interface for the characterization of the problem and for the determination of the parameters involved in the formulation of the problem; a database where data characterizing the problem is stored; the optimization module, subdivided into strategic planning, tactical planning and the operational level. The necessity of taking into account different levels derives from the different time scales to be considered and from the different decisions to be performed. Long-term decisions refer to plant sizing, location, and selection among the various technology options. Tactical level decisions refer to planning over a medium- short-term horizon, and are generally considered within a discrete-time setting, with the assumption that the plant capacity and the facilities are known. Finally, the operational level is based on the explicit modelling of the supply-chain process as an ordered sequence of the operations that should be performed from biomass collection to energy conversion. In this case, a non-linear mixed-integer programming optimization is proposed. The main focus is the optimal planning of forest biomass use for energy production.
Another non-linear decision support model is proposed in [20]. The problem considered is optimal exploitation of biomass resources with several harvesting sites and a few centralized combustion plants on a regional level. The aim is to find the optimal capacity of heat and power generation as well as the optimal utilization of biomass resources and transport options. The time horizon considered is one year so that the model is capable of giving long-term decision support.
Another decision support system (DSS) for bioenergy applications, with special reference to harvesting wood for energy from conventional forestry and short rotation forestry, is proposed in [21]. In particular, the work addresses the calculation of delivery costs for wood fuel from conventional forest in the UK. Moreover, an exhaustive review of topics related to the problems of modelling bioenergy supply systems is provided. The same research group proposed other DSSs: the Coppice decision support system (CDSS), a spread- sheet model that can be used to model the costs of growing short rotation coppices under UK conditions, and the Coppice harvesting decision support system (CHDSS), which models the supply chain from the standing Coppice crop through harvesting, storage and transport. These DSSs, as well as other models, have been linked together to produce a bioenergy assessment model (BEAM), which is a comprehensive biomass to electricity model.
Biomass transport modelling is essential to optimize bioenergy supply chains, plant size and locations. Various typologies of biomass transport models are available in literature. A first type is a simple continuous model [22,23], which is suitable for idealized situations; a second type is a discrete model with defined grid road systems [24,25]; a third type is a complete discrete model incorporating GIS [26,27]. Road tortuosity in the first and second type of models are generally based on assumptions without carrying out road system evaluations. In the last type, the road network is rasterised and then continuous grids of distance and transportation costs to the plant sites are computed using functions of Euclidean distance and allocation. Moreover, in case of on-farm biomass transport, previous studies [28] show that the haulage cost is also dictated by farm landscape attributes and infrastructure.This section overviews the biomass and biofuel transport systems and related costs with different supply route scenarios. The available handling, loading and transport technologies for the various categories of biomasses are assessed. The selection of transport modes is influenced by the typology of biomass feedstocks and supply chain dimension, and a possible biomass/biofuel classification for this purposes can be as follows: (i) forestry products and urban green; (ii) agricultural energy crops and by-products, (iii) urban and agro-industrial bio-wastes with high moisture content; (iv) waste vegetable oils and liquid biomass; (v) long distance transport of solid and liquid biomass; (vi) gaseous biofuels, including biogas, syngas, biomethane. The main trade-offs of road, rail, ship, pipeline transport systems are investigated in the following, and the key factors influencing the optimal choice of the transport mode are discussed.
Transportation is a cost element in any energy project, but this is especially true for biomass because of the lower energy and bulk density compared with fossil fuels. Several studies have shown that truck transport cost of agricultural residues biomass ranges from 20% to greater than 40% of total delivered cost, depending on distance traveled and mode of transportation [22]. Long-distance transport of biomass including the use of trucks and ships has been addressed in literature [23,24], proving that, despite the long shipping distance, the costs of Latin America wood chips in the receiving European harbour can be as low as 40 Eur/ t or 2.1 Eur/GJ, and the crop’s costs account for 25–40% of the delivered costs. The relatively expensive truck transport from production site to gathering point restricts the size of the production area, so that a high biomass yield per hectare is vital to enable large-scale systems.
Many studies have shown that the optimum size of biomass processing and conversion plants is large when abundant biomass is available, and low-cost transport systems are used; on the contrary, when the specific biomass transport cost increases, because of low energy density of the feedstock and long transport distances, and scale economies and conversion efficiencies are less influenced by the size, the optimal plant size tends to be lower [25-29].In addition, many field sources of biomass are, by their nature, remote from the population centers that will use the produced energy. Thus, developers of such biomass projects will have the alternative of moving the biomass to a plant near the energy consumer, or moving the produced energy from a remote biomass processing plant, and the selection of optimal plant location is based on the relative costs and energy losses of biomass, biofuels and energy transport and intermediate storage. Moreover, both at a large scale and in urban areas, biomass transport by truck may not be physically possible owing to traffic congestion and resulting community opposition. Rail transport of biomass reduces the frequency of loads and offers better environmental performances in comparison to road transport. A specific comparison of rail vs truck transport of biomass is proposed in [30], and the minimum shipping distance for rail transport above which lower costs/km offset the incremental fixed cost in comparison to truck is estimated in the range of 145-170 km for wood chips and straw in a North American setting. Pipeline transport would deliver biomass with minimum ongoing community impact, but is feasible only for liquid and gaseous biomass [31], and will be discussed in the next section.
In [32] the relative cost of transportation by truck, rail, ship, and pipeline for three biomass feedstocks, by truck and pipeline for ethanol, and by transmission line for electrical power is assessed, for various plant sizes. Distance fixed costs and distance variable costs (including power losses during transmission), are calculated for each biomass type and mode of transportation. The results show that pipelining is competitive only at large scale, while transhipment is feasible for distances higher than 1,000-3,000 km, on the basis of the typology of biomass.
In [33] the delivery cost of different combinations of multiple forms of lignocellulosicfeedstocks including agricultural and woody biomass is analysed. In particolar, three types of biomass i.e., wheat, straw, corn stover and forest biomass were considered in different forms such as loose biomass, bales/bundles, chopped/chipped and pellets. It was found that the delivery cost of a combination of woody and agricultural biomass feedstocks is lower than that for a single type of biomass, and traffic congestions resulting from biomass supply to a large facility could be significantly reduced by increasing the density of biomass.
However, selection of a transportation mode cannot be based on only one issue. Economical, environmental, social, and technical parameters should be integrated to select the best system [34].
Transportation costs for biomass and its products have a distance fixed component (DFC) that is incurred regardless of the distance travelled, and includes loading-unloading costs depreciation, insurance, interests and the administrative cost of biomass transport, and a distance variable component (DVC) that includes costs of fuels, repair, tire, lubrication and labor. DFC depends on the type of biomass being transported and the equipment and contractual arrangements involved, which are both case specific, and vary based on the specific form of biomass to a far greater extent than DVC. For example, large round bales of stover or straw would require different treatment for transhipment from truck to rail than woodchips or pellets. The impact of DFC on overall transportation cost diminishes with increasing distance. Moreover, biomass transportation costs are often referred to the total number of actual metric tons as road limits, and in this case the calculated transport cost per dry metric ton will vary for every biomass source. For truck, rail, and ship transport, mass is the primary factor setting the cost of shipment, although for low density loads volume can become the limiting factor. For pipelines transporting a single phase liquid, for example ethanol, liquid volume is the primary factor, whereas for two- phase slurry pipelines carrying biomass the amount of dry matter is the primary factor, because moisture level reaches equilibrium during transport. For both ship, road and rail transport modes, the DFC for low density biomass (straw) is significantly higher than for chips, pellets or TOP. Infact, chips and pellets lend themselves to bulk handling by methods such as conveying or pneumatic transfer, whereas straw/stover is moved as a large bale.
\n\t\t\t\t | \n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
1 | \n\t\t\tTruck-small | \n\t\t\tSolid-liquid biomass | \n\t\t\t15 m3 | \n\t\t\t5 t | \n\t\t\t2-4 Eur/t | \n\t\t\t0.2 Eur/km m3 | \n\t\t\temission levels, traffic congestions, road suitability (for large trucks) | \n\t\t\t[23,24,30,32,35\n\t\t\t | \n\t\t
Truck-medium | \n\t\t\t35 m3 | \n\t\t\t25 t | \n\t\t\t0.15 Eur/km m3 | \n\t\t|||||
Truck-large | \n\t\t\t100 m3 | \n\t\t\t40 t | \n\t\t\t0.1 Eur/km m3 | \n\t\t|||||
Liquid-tank truck | \n\t\t\tBio-oil | \n\t\t\t30 m3 | \n\t\t\t35 t | \n\t\t\t5.7 $/m3 | \n\t\t\t0.18-0.07 $/km*m3 | \n\t\t\t[23,32,41] | \n\t\t||
Liquid tank trailer | \n\t\t\t60 m3 | \n\t\t\t70 t | \n\t\t\t5.6 $/m3 | \n\t\t\t0.15-0.05 $/km*m3 | \n\t\t||||
2 | \n\t\t\tRail | \n\t\t\tSolid-liquid biomass | \n\t\t\t2,500 m3 | \n\t\t\t1000 t | \n\t\t\t5-14 $/t | \n\t\t\t0.02-0.03 $/km t | \n\t\t\tRail network availability | \n\t\t\t[30,32] | \n\t\t
3 | \n\t\t\tShip | \n\t\t\t6,700-105,000 m3 | \n\t\t\t4,000-63,000 t | \n\t\t\t11-34 $/t | \n\t\t\t0.01 $/km t | \n\t\t\tLarge scale storage capacity, long distance emission levels, ships avalability | \n\t\t\t[23,24,32] | \n\t\t|
4 | \n\t\t\tPipeline-1 | \n\t\t\tBio-oil, biodiesel | \n\t\t\t156 m3/day | \n\t\t\t\n\t\t\t | 0.1 | \n\t\t\t0.29 | \n\t\t\tInvestment costs, refurbishment costs in case of existing infrastructures, energy losses (DH) | \n\t\t\t[41,42] | \n\t\t
Pipeline-2 | \n\t\t\t469 m3/day | \n\t\t\t\n\t\t\t | 0.04 | \n\t\t\t0.12 | \n\t\t||||
Pipeline-3 | \n\t\t\t1000 m3/day | \n\t\t\t\n\t\t\t | 0.02 | \n\t\t\t0.07 | \n\t\t||||
Pipeline-4 | \n\t\t\tethanol | \n\t\t\t1000 m3/day | \n\t\t\t\n\t\t\t | 0 | \n\t\t\t4.13 C-0.5885 $/km t | \n\t\t\t[32] | \n\t\t||
5 | \n\t\t\tGas network | \n\t\t\tgas | \n\t\t\tHighly variable on the basis of pipeline diameter | \n\t\t\t\n\t\t\t | 50-150 kEur/km | \n\t\t\t\n\t\t\t | [61-63] | \n\t\t|
6 | \n\t\t\tDistrict heating | \n\t\t\t90* / 120° heat | \n\t\t\t\n\t\t\t | 350-450 kEur/km | \n\t\t\t\n\t\t\t | [43-46,50,64,65,99,112] | \n\t\t
Biomass, biofuels and bioenergy transport modes: technical parameters and cost figures
Notes: Variable transport cost figures are composed by fuel cost, transport manteinance and spare parts costs, personnel costs; fixed costs are given by loading-unloading costs and all the other costs that are not dependent on the transport distance;
Pipeline-1: capacity bio-oil plant 250 t/day, density 1,2 t/m3, transport capacity 156 m3/day, pipeline diameter 5.1 cm, distance between booster 9.1 km; 65 MW capacity delivered energy; Pipeline-2: capacity bio-oil plant 750 t/day, density 1,2 t/m3, transport capacity 469 m3/day, pipeline diameter 7.6 cm, distance between booster 9.4 km; 195 MW capacity delivered energy; Pipeline-3: capacity bio-oil plant 1600 t/day, density 1,2 t/m3, transport capacity 1000 m3/day, pipeline diameter 9.9 cm, distance between booster 8.1 km; 416 MW capacity delivered energy; C = capacity of bio-ethanol pipeline t/day
Pipeline costs include installation costs
The techno-economic parameters reported in Table 3 are obtained from an overview of literature data on capacities and costs of various biomass, biofuels and energy transport routes. However, cost figures are affected by a relevant range of uncertainties. As regards truck transport of wood chips and straw, as an example, fixed and variable transport costs range between 3.8-4.9 $/dry t and 0.11-0.15 $/t km in the Northern America scenario, as discussed in [35], while data for wood chips in Brazil [36] and Sweden [37] and mixed agricultural and forest residues in Thailand [38] present cost variations in the range of 50%.
The truck operating cost can vary because most of the cost components are region specific, and influenced by fuel taxation. A small change in the equipment use would have large impact on the costs [39]. Driver and fuel costs have wider range of tolerance within them [40]. The firm size from where truck or trailer are rented also affect the cost. Some costs are lower for small farms (such as wages, administrative costs) but these are offset by economics of scales of costs for equipment, tire and consumables which lead to large variations of total costs. There are also many different sizes and types of trucks available. In the specific case of small transport distances, which is typical of the integration of bioenergy in urban areas, the data are obtained from official prices of transports from operators in Italy. The data for medium and large truck are also referred to the Italian scenario (fuel taxation level and fixed costs).
Liquid biomass, both in the form of pyrolysis bio-oil, row vegetable oil, bio-ethanol, biodiesel or other BTL fuel, present an higher energy density in comparison to solid biomass and can be transported by trucks, rail, ship and pipelines. Specific transport issues arise in case of high viscosity and corrosive bio-oils, such as pyrolysis oils, that require stainless steel tanks with an average 14% increase in transport costs [23]. Transport of conventional liquid fuels (per tonne) is also assumed to be 25% higher than for solid fuels [23]. Costs for liquid biomass by trucks are reported in Table 3, according to [41] and considering pyrolysis bio-oil. In case of biodiesel and bioethanol these costs could be reduced, because of the lower viscosity (that means quickier loading/unloading rate) and absence of corrosive materials for tanks.
Pipeline transport can be an economically interesting option for large scale transport of bio-oil and over long distances. Today, most of the crude oil is transported by pipeline, and the transport costs benefit from economy of scale in capital cost. Traffic congestion problems are also mitigated. Pipeline transportation of liquid fuels has been used over several decades. Recently, several studies have been carried out on the pipeline transport of raw biomass in the form of a slurry [31,32,35]. Bio-oil and liquid biofuels in general can be transported by pipeline in larger capacities and over longer distances. Current practice is to transport bio-oil by trucks from the production plant. An important characteristic of bio-oils is their high viscosity, that decreases when increasing temperature. In the case of pyrolysis bio-oil, at about 45 °C, its viscosity for pipeline transportation is 15 cSt which is similar to crude oil. To maintain the bio-oil in the pipeline over 45 °C, the pipeline has to be insulated. In the case of low pH bio-oil, the corrosion to carbon steel requires the use of high density polyethylene (HDPE). Similar to truck transportation cost, pipeline transportation cost has both fixed cost (FC) and variable cost (VC). Fixed cost of pipeline transport includes capital cost of inlet and outlet stations. Inlet station refers to the terminal where bio-fuel moves from the storage tank to the pipeline through pumps. Outlet station refers to the terminal where it moves from the pipeline to the storage tank. The inlet station costs include: capital cost of storage tank, building and foundation cost, fittings and valves cost, inlet pump cost and access road cost. Similarly, the outlet station costs include storage tank cost, fittings, valve and small distribution pump cost and building cost. In [42], investment cost figures for inlet and outlet station for a bio-oil pipeline at a transport capacity in the range of 156-2,000 m3 per day (corresponding to a bio-oil plant using 250-3200 dry tonnes of biomass per day and a pipeline energy transport capacity of about 65-830 MW) are reported. Variable cost of pipeline transport includes capital cost of pipeline, installation and construction cost, operating cost of pipeline, booster station cost, maintenance cost of pipeline and pumps, communication line cost, insulation costs and road access cost. Operating cost of the pipeline includes labor required for running the system and electricity required for pumps. For transport of bio-oil over longer distances, booster stations are required to overcome the frictional losses during the transport. The variable cost for the same bio-oil pipeline capacity range, including the booster station and a length of 100 km are proposed in [42]. These cost figures have been used to inform a detailed techno-economic model based on discounted cash flow analysis, in order to calculate the cost of pipeline transport ($/m3) of bio-oil for different capacities of pipeline (m3/day) at various lengths of pipeline. These cost figures are reported in Table 3. The results report that the pipeline transport cost decreases with the increase in capacity of pipeline and is directly proportional to the distance of transport. Although the pump power increases with the increase in the capacity, the total cost of pipeline transport of bio-oil ($/m3) decreases with the capacity, predominantly due to the benefits from the economy of scale in the capital cost of pipeline. Because of the lower fixed transport costs of pipeline in comparison with truck systems, for short distances and large quantity of delivered fuels, the pipeline option could be more promising. For long distances, the bio-oil heating requirements to mantain the viscosity and the power consumption of the pumps due to the fiction losses should be carefully assessed. However, it should be noted that pipeline costs are highly influenced by the specific installation area, since in densely popolate urban areas, where most of the energy demand is concentrated, the costs can be even 5 times higher than in rural areas.
In [43] the life cycle assessment of transportation of bio-oil by pipeline and by truck are compared. The scope of the work includes the transportation of bio-oil by truck or pipeline from a centralized plant to an end-user. Two cases are studied for pipeline transport of bio-oil: the first case considers a coal based electricity supply for pumping the bio-oil through a pipeline; the second case considers an electricity supply from a renewable resource. The two cases of pipeline transport are compared to two cases of truck transport (truck trailer with capacity 30 m3 and super B-train truck with capacity 60 m3). The results report values of 345 and 17 g of CO2/m3 km, respectively in the case of coal based and renewable electricity, and similar values for transport by trailer and super B-train truck are 89 and 60 g of CO2/m3 km, respectively. Energy input for bio-oil transport is 3.95 MJ/ m3 km by pipeline, 2.59 MJ/m3 km by truck and 1.66 MJ/ m3 km by super B-train truck.
In the case of liquid biofuels, other than the previous transport systems, pipelines can be used. In the case of high viscosity bio-oils, the pipelines should be probably heated in order to achieve acceptable transport yields. The advantages of pipeline systems are in terms of avoided congestion during delivery, avoided air emissions from trucks, and reduced operational costs. However, sometimes it is not possible to install pipelines, in particular in urban areas with planning constraints or high refurbishment costs. The solution of centralized biomass processing facilities and decentralized energy conversion plants is based on the concept that the high density biofuel can be easily stored and transported to the CHP plants near to the loads by means of efficient distribution systems as pipelines, eventually integrated into existing ones. The costs and the energy losses of biofuels distribution networks would be in most cases lower than that one of district heating networks.
The biomass handling, storage and pretreatment are crucial steps for an optimal development of bioenergy supply chains. Different biomasses require specific treatments and the seasonality of supply increases the complexity of dimensioning and optimal operation of these facilities.
The storage requirements of various biomass and biofuel typologies and the technical options currently adopted are reviewed in the following, together with cost figures of different storage systems. These costs could be particularly relevant when low energy density biomasses, with high seasonality and particularly complex storage requirements have to be stored.
The biomass supply chain presents several distinctive characteristics that diversify it from a typical supply chain. One of them is the need to store the biomass in a proper way, because of its seasonal availability and the necessity of continuous operation of biomass conversion plants. Moreover, in case of imported biomass (wood chips, bio-oils) the transport logistics constraints and the possibility to purchase and hence store large quantities of biomass are crucial issues in order to favourite trading and achieve good market prices. The biomass storage is a particularly important task, both for the relevant investment costs of some storage technologies and for the biomass and energy losses and safety issues related to the selection of poor storage systems. Since most of the biomass-to-energy applications to date concern single biomass use, there is a need of storing very large amounts of biomass for a significant time period, if year-round operation of the power plant is desired. The limited time frame for collecting a large amount of biomass leads also to significant seasonal need of resources, both equipment and workforce. This seasonal demand may increase the cost of obtaining these resources, while leading to their suboptimal utilization, particularly as regards storage space. The problems introduced by the seasonality of biomass availability may be avoided, if a biomass that is available year- round is used, which is very rare in practice. The multi-biomass approach may smooth significantly these problems and is quite often applied in real cases. Another characteristic of the biomass supply chain is that it has to deal with low-density materials. As a result, there is increased need for transportation and handling equipment, as well as storage space. This problem is enhanced by the low heating value, which is partly due to the increased moisture of most agricultural biomass types. The low density of biomass increases further the cost of collection, handling, transport and storage stages of the supply chain. Finally, several biomass types require customized collection and handling equipment, leading to a complicated structure of the supply chain. For example, there are different requirements on handling and transportation equipment and storage space configuration if biomass is procured in the forms of sticks, chips, round bales, plastic bags, etc. Moreover, in case of wet biomass for biogas plants, storage issues are particularly relevant since the mass and energy losses during a not accurate storage can be very relevant. Other typologies of biomass can not be easily stored without a preliminary pre-treatment (drying), because of odour problems and health and safety regulations (i.e. wet olive cake). Liquid biomass (bio-oils) should be also stored in a proper way in order to avoid acidification and deterioration of the biofuel. Therefore, the typology of biomass and the form in which the biomass will be procured often determines the investment and operational costs of the respective bioenergy exploitation system, as it affects the requirements and design of the biomass supply chain.
In case of solid biomass for thermochemical applications, on-field storage is a low-cost option, with the drawback of high biomass losses, difficult control of moisture content, risk of auto-ignition, health and safety issues, and finally land occupation that can hinder next cropping. The use of intermediate storage between field and energy conversion plant is also an option, that implies double biomass transport and often higher total delivery costs [57]. In case of long distances, the use of road-rail transport systems could be integrated with intermediate storage [22]. Storage location at the premises of biomass upgrading and biofuel conversion plants could facilitate the drying process, by means of dumped heat from the process plants, thus preventing material decomposition and health and safety risks.
As regards solid biomass for termochemical conversion systems, three typologies of storage are assessed in [11]: i) closed warehouse with biomass drying capability, by hot air injection generated by dumped heat of the CHP plant which helps to avoid quality degradation of the biomass while simultaneously increasing the energy content of the biofuel; ii) covered storage facility of a pole-frame structure having a metal roof without any infrastructure for biomass drying where a 0.5% material loss/month rate has been assumed; iii) ambient storage of biomass, covered only with a plastic film presenting the highest material loss rate, which is assumed to be 1% material loss/month.
In Table 4 the main characteristics and costs of the available storage systems are described.
Biomass drying provides significant benefits in case of thermochemical conversion systems, such as increased boiler efficiency,lower air emissions, improved boiler operations. The three main options for lignocellulosic biomass drying are rotary dryers, flash dryers and superheated steam dryers. The first types of dryers are less sensitive to biomass size and are the most common option, even presenting the greates fire hazard. Flash dryers are more compact and easier to control, but require small particle size, while superheated steam dryers present the best energy efficiency performances with very low air emission levels. The dryer selection is dependent on the biomass typology, opportunity of integration into biomass processing systems, required air emission levels, availability of waste heat. The biomass drying technologies required in case of thermochemical energy conversion processes are reviewed in [66-68]. In particular, in [66] a detailed description of dryer technologies and heat recovery systems for biomass drying are provided. Guidelines about optimal selection of drying technology and size on the basis of the specific process and feedstocks are also provided, including cost figures, environmental performances and safety issues for each option under investigation.
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
Open storage | \n\t\t\t1-3% | \n\t\t\t20-50 Eur/m2\n\t\t\t | \n\t\t\t4 | \n\t\t\t3-4 | \n\t\t\tSolid biomass | \n\t\t\tRisks of ignition | \n\t\t
Covered storage | \n\t\t\t0,5-1% | \n\t\t\t100-150 Eur/m2\n\t\t\t | \n\t\t\t4 | \n\t\t\t6-8 | \n\t\t\tSolid biomass | \n\t\t\t\n\t\t |
Closed wharehouse | \n\t\t\tnegligible | \n\t\t\t200-300 Eur/m2\n\t\t\t | \n\t\t\t5 | \n\t\t\t6-8 | \n\t\t\tSolid biomass | \n\t\t\tPossible integration with drying systems and biomass treatments | \n\t\t
Plastic covered storage | \n\t\t\t0,5-2% | \n\t\t\t50-100 Eur/m2\n\t\t\t | \n\t\t\t4 | \n\t\t\t6-8 | \n\t\t\tWet biomass for biogas | \n\t\t\t\n\t\t |
Depressurized wharehouse | \n\t\t\tnegligible | \n\t\t\t300-500 Eur/m2\n\t\t\t | \n\t\t\t\n\t\t\t | 6-8 | \n\t\t\tSolid biomass | \n\t\t\tRequired to minimize odours emissions of biomass | \n\t\t
Silos | \n\t\t\tNegligible | \n\t\t\t25-35 Eur/m3\n\t\t\t | \n\t\t\t\n\t\t\t | 6-8 | \n\t\t\tLiquid-solid biomass | \n\t\t\t\n\t\t |
Storage tank | \n\t\t\tnegligible | \n\t\t\t40-50 Eur/m3\n\t\t\t | \n\t\t\t\n\t\t\t | 6-8 | \n\t\t\tWet solid-liquid biomass | \n\t\t\tRequired to minimize pre-fermentation of wet biomass in biogas plants | \n\t\t
In case of wet biomass, overall efficiency can often be improved by dewatering prior to thermal drying. On the downside, mechanical dewatering equipment itself can consume a large amount of energy and have high maintenance requirements, which must be weighed against the reduction in drying energy. Dewatering equipment includes drying beds, filters and screens, presses, and centrifuges. Depending on the material and the specific type of equipment, mechanical dewatering equipment may reduce moisture content to as little as approximately 50% [67]. Passive dewatering methods, such as using filter bags that are impervious to rain but allow moisture to seep out, can achieve moisture contents as low as 30% at low cost, but long periods of time – on the order of two to three months – may be required. An overview of dewatering and drying technologies on the basis of biomass properties is proposed in [67,68], incuding cost analyses, energy performances, health and environmental ssues.
Technologies such as natural drying, solar drying, gas or biomass fired rotating kilns, drying systems coupled to CHP plants with heat recovery systems are compared.
The biomass treatment and upgrading processes are required to obtain high energy density biofuels, which can be easily transported, stored, and that are suitable for high efficiency energy conversion processes, possibly at the premises of the energy demand. In Table 5, the commercially available and the most promising biomass treatment processes are described, to produce solid, liquid and gaseous biofuels. In most cases, these processes are implemented near to the biomass production sites, in order to minimize the transport costs, facilitate the trade on the market and the storage issues. However, when integrating biomass routes into existing energy systems, the specific logistics, economic and environmental constraints of energy demand in tertiary and residential sectors imply the necessity to locate these processing facilities in industrial areas, eventually decoupling them to the final energy conversion of biofuels near to the loads. Moreover, locating these processes in industrial areas could facilitate the implementation of biorefineries approaches and the integration of multiple processes.
The most promising biofuels are pellets (and in particular torrefied pellet with higher LHV), bio-oils (both from FAME and 2nd gen thermochemical processes on lignocellulosic biomass) and bio-methane (from AD biogas upgrading or 2nd gen FT processes on lignocellulosic biomass).
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
Solid biofuel | \n\t\t\t\n\t\t | |||
1 | \n\t\t\tPellet | \n\t\t\tChipping-drying-pelletization | \n\t\t\tLignocellulosic biomass | \n\t\t\t[69-71] | \n\t\t
2 | \n\t\t\tTOP (torrefied pellet) | \n\t\t\tTorrefaction-pelletization | \n\t\t\tLignocellulosic biomass | \n\t\t\t[24,72-74] | \n\t\t
3 | \n\t\t\tChip | \n\t\t\tChipping-drying | \n\t\t\tLignocellulosic biomass | \n\t\t\t\n\t\t |
4 | \n\t\t\tTOP (torrefied pellet) | \n\t\t\tHydrotreatment-drying/dewatering | \n\t\t\tWet lignocellulosic biomass | \n\t\t\t[75-77] | \n\t\t
Liquid biofuel | \n\t\t\t\n\t\t | |||
5 | \n\t\t\tBio-oil | \n\t\t\tMechanical or chemical refining / oil hydrotreatments | \n\t\t\tVegetable oils and fat oils | \n\t\t\t[78-80] | \n\t\t
6 | \n\t\t\tPyrolysis oil (BTL) | \n\t\t\tPyrolysis and thermochemical processes on lignocell biomass | \n\t\t\tLignocellulosic biomass | \n\t\t\t[47,81-83] | \n\t\t
7 | \n\t\t\tBiodiesel | \n\t\t\tEsterification of FAME (fatty acid methyl esters) | \n\t\t\tVegetable oils and fats | \n\t\t\t\n\t\t |
8 | \n\t\t\tBiodiesel-FT | \n\t\t\tGasification coupled to FT biodiesel process | \n\t\t\tLignocellulosic biomass | \n\t\t\t[84-86] | \n\t\t
9 | \n\t\t\tBioethanol | \n\t\t\t2nd gen process from lignocellulosic biomass | \n\t\t\tLignocellulosic biomass | \n\t\t\t[87-89] | \n\t\t
Gas biofuel | \n\t\t\t\n\t\t | |||
10 | \n\t\t\tSyngas | \n\t\t\tGasification of lignocellulosic biomass | \n\t\t\tLignocellulosic biomass | \n\t\t\t[90-92] | \n\t\t
11 | \n\t\t\tBiogas | \n\t\t\tAnaerobic Digestion | \n\t\t\tWet fermentable biomass | \n\t\t\t[93,94] | \n\t\t
12 | \n\t\t\tBiomethane-AD | \n\t\t\tAD and biogas upgrading | \n\t\t\tWet fermentable biomass | \n\t\t\t[95,96] | \n\t\t
13 | \n\t\t\tBiomethane-FT | \n\t\t\tGasification+syngas upgrading | \n\t\t\tLignocellulosic biomass | \n\t\t\t[97,98] | \n\t\t
14 | \n\t\t\tBio-hydrogen | \n\t\t\tDark fermentation-AD processes | \n\t\t\tWet fermentable biomass | \n\t\t\t[89,100-102] | \n\t\t
15 | \n\t\t\tBio-hydrogen-FT | \n\t\t\tCatalytic synthesis from FT processes | \n\t\t\tLignocellulosic biomass | \n\t\t\t[103-106] | \n\t\t
Biomass processing technologies for heat and power generation
The biofuels can then be converted into energy for stationary applications by means of several technologies. The heat generation is the cheapest and most profitable conversion system for solid biomass and in absence of specific incentives for bio-electricity. The district heating (DH) option is interesting in case of high heat demand density (i.e. new buildings or refurbishment of existing ones), and possibility to increase the networks load factor by district cooling with adsorption chillers. The CHP option with solid biomass can be attractive in case of high electricity costs, incentives for biomass electricity, favourable rules for on-site generation and net metering, presence of suitable heat/electricity demand and possibility to manage the logistic constraints of the biomass transports and storage. The technological options are ORC plants up to 1-2 MWe [107, 108] and ST, possibly in cofiring, for higher size [109]. In the case of liquid and gaseous biofuels, the options of internal combustion engines (ICE) and gas turbines (GT) [46], also in cofiring with natural gas, are available and allow minimizing the biomass transport, storage and air emission constraints which are typical of large solid biomass boilers and make their diffusion difficult in urban areas. In perspective, the use of small scale ICE, but also microturbines (MT) [110] and fuel cells (SOFC) [111], fired by high quality biofuels (bioethanol, biomethane, biohydrogen [89,106]) for heat and power, could be a very promising option, in particular if connected to a centralized biofuel distribution network, and integrated with the gas network.
One of the key issues when implementing competitive and sustainable bioenergy routes is the integration with existing energy systems and infrastructures.
In this context, there are several promising opportunities of repowering existing fossil fuel plants (brownfield plants) for biomass cofiring, both in the case of CHP and district heating systems [113-115]. Moreover, new power plants can be installed in dual-fuel configurations, in order to increase plant operation flexibility, reduce the problems of biomass storage, handling, seasonality, transport of relevant quantities of biofuels, that are typical of single fuel plants. On the contrary, when a power plant is designed to fire both biofuels and fossil fuels, the typical technical and economic problems of only biomass-fired power plants can be drastically reduced, and large scale (and hence higher conversion efficiencies) can be achieved avoiding the use of huge quantities of biomass. ICEs are typical technologies that can be fed by multi-fuels; in particular Diesel engines are suitable for diesel/gas operation with a maximum gas (or biogas) quantity of 75% [116] and a slight efficiency reduction. Also gas turbines can be fed by natural gas in combination to bio-oils, biodiesel, or bio-ethanol. As an example, GE’s LM6000-PC aeroderivative gas turbine can be fired by natural gas, ethanol, biodiesel fuels size 35-60 MWe.
Another interesting energy systems integration opportunity regards the use of existing infrastructures for biofuels and fossil fuels processing (co-refining) and the transport. In the latter case, the potentials to use existing natural gas to transport biomethane from thermochemical synthesis or anaerobic digestion processes are particularly promising.
The biomass processing and pre-treatment facilities are influenced by scale economies and in most cases large processing plants can minimize the biofuel production costs, in particular when efficient biomass transport systems are implemented and the variable component of transport cost (dependent on the biomass collection distance) is not dominant. Moreover, biomass processing plants require large sites for biomass storage and handling, and the amenity issues related to the presence of these industrial facilities are often not compatible with residential areas. On the contrary, the final biofuel energy conversion should be located at the premises of the energy demand, and in particular where it presents the highest costs, such as in residential areas, in order to minimize the energy distribution costs. This is particularly relevant in the case of heat (and eventually combined cool generation by adsorption chillers) or CCHP plants. For this reason, several researches on bioenergy are focused on decoupling of biomass processing and biofuel energy conversion, to favourite the integration of bioenergy into urban and peri-urban energy systems.
As an example, in [47] it is described how systems de-coupling applied to fast pyrolysis and diesel engines can distinguish itself from the other conversion technologies, since several remote generators are much better served by a large fast pyrolysis plant that supplies fuel to de-coupled diesel engines than by constructing an entire close- coupled system at each generating site. Another advantage of de-coupling is that the fast pyrolysis conversion step and the diesel engine generation step can operate independently, with intermediate storage of the fast pyrolysis liquid fuel, increasing overall reliability. Peak load or seasonal power requirements would also benefit from de-coupling since a small fast pyrolysis plant could operate continuously to produce fuel that is stored for use in the engine on demand. A similar approach, but related to Fisher-tropsh liquids production at a centralized catalytic synthesis facility with the two options of direct biomass transportation and gasification to centralised plant or preliminary distributed processing of biomass by fast pyrolysis to bio-oil is proposed in [27]. The results show that, for large biomass collection radius, the intermediate and distributed processing of biomass to bio-oil presents lower total production costs, because of the lower biomass delivery costs that offsets the higher operation and biomass costs. A similar approach, related to torrefactionvs fast pyrolysis bio-oil vs wood pellets pre-treatment and long distance transport to FT liquid or power plants is proposed in [24], including a detailed assessment of overall chain efficiency in long distance biofuel transport, increased energy conversion efficiency of high quality biofuels, and sensitivity to the main techno-economic parameters. The results report that torrefaction coupled to pelletization to feed BIGCC or cofiring power plants allows minimizing the energy production costs. Another research proposed in [48] compares the production of wood thinning chips, pellet, fast pyrolysis bio-oil and bio-methanol with the further options of cofiring or cogeneration, in order to define the best biomass convesrion strategies, and the benefits of densification in case of long distance transport are enhanced. Finally, in [49] the options of HTC treatment of lignocellulosic biomass vspelletization and coupling these facilities to CHP plants are investigated; the results show that HTC can be a very interesting option for wet biomass, competitive to drying-pelletization.
Bioenergy plants can be also conducted at a wide range of capacities. The problem of optimal size calculation of biomass-to-energy conversion plants has been widely addressed in literature, on the basis of the trade off between the high conversion efficiencies and economies of scale of large size plants and the low biomass collection radius, transport costs and feedstocks collection and management requirements of small size plants [27, 29,51-54]. Factors such as feedstock availability and spatial distribution, terrain and road conditions, biomass transport specific costs, storage costs, existing energy infrastructures, biomass seasonality issues, conversion plant scale factors and efficiencies influence this optimization problem. Logistic aspects are particularly relevant when low energy density and highly dispersed feedstocks are used. Moreover, small scale plants can facilitate the use of excess heat generated, that can match local loads, if a cogeneration configuration is selected. In [55,56], two generic analytical frameworks are proposed, to calculate the optimal conversion plant size for biogas plants.
This chapter overviewed the logistic issues of bioenergy routes for stationary applications, discussing the supply chain modelling approaches proposed in literature, the various options for storage, transport, processing and energy conversion of the biomass, and the research trends in order to improve the sustainability and economics of biomass for heat and power.
One of the most interesting research areas regards the optimal location and sizing of biomass processing and conversion facilities, on the basis of the biomass resource, the logistics of supply and conditioning, the final energy end-user typology and existing energy infrastructures for bioenergy integration.
The following main considerations can be drawn: i) high quality biofuels (pellet, bio-oils, biomethane) should be used in order to minimize transport, storage and environmental issues and facilitate the energy coinversion of biomas at the premises of energy loads by CHP plants; ii) decoupling of biomass upgrading and biofuel energy conversion near to the loads is a very promising option; iiii) small boilers are suitable for rural areas and low heat density zones, while DH is feasible with high energy density loads or when cooling distribution can be introduced to increase the network load factor; iv) integration into existing infrastructures is a key factor (i.e. possibility to use existing gas networks for bio-methane); v) solid biomass CHP implies large storage, transport and air emission issues, should be integrated into DH schemes and localized where space and logistics of transport are not a constraint; vi) large CHP plants should be integrated as possible into brownfield plants and using cofiring options to maximize energy conversion efficiencies while limiting the amounts of biomass required; vii) the most reliable technological option currently available for small scale biomass CHP in urban and periurban areas are ORC plants fed by solid biofiuels and ICE fed by liquid or gaseous biofuels, while promising technologies for small scale on site biofuel CHP are microturbines and fuel cells. In conclusion, the economic competitiveness of bioenergy routes in CHP schemes is strongly influenced by the subsidies available for bio-electricity, while biomass heating and cooling can be, at some extent, competitive with fossil fuels even without incentives.
Increasing pace of production to meet the demands of growing population is resulting in overexploitation of both renewable and non-renewable resources and accumulation of contaminants in environment, as wastes. Amongst other things, more emphasis is now being placed on recycling technology to prevent the depletion of resources and to limit environmental degradation due to overloading of soil and atmosphere with residues/byproducts of chemical and physical processing industries. Initially, this technology dealt with protection of environment through waste water treatment, disposal of human excreta, use of crop residue for improving soil fertility, etc. The field has now widened to control environmental pollution, waste land reclamation and conversion of wastes into industrially valuable products. In this context, bioconversions/bio transformations through microbes are attracting greater attention, since enzymes carry out very specific reactions under mild conditions, larger water insoluble molecules can be transformed and biogeochemical cycling do not require external energy inputs [1].
Soil and water conservation has been practiced extensively by settled agriculturist traditionally to maintain the productivity of any country. When the population pressures increase, the farming systems break down as there is an upper limit for any land water system to support increasing number of human beings and livestock without affecting the ecological balance. Over the years the population pressure has been increased and per capita availability of arable land is decreased. Thus soil and land resources, in recent years, are under tremendous pressure with highly conflicting and competing demands of rising population. The increased claims on land for settlement, urban growth, industrialization and other development purposes apart from increased demands for food, fodder and fibre, has set in an imprudent trend of land utilization with disregard to risks of damage to the ecosystem. The apathy for the harmonious use of land in accordance with potentialities and capabilities of soils has given rise to a multitude of serious problems. The major soil problems in some tropical and subtropical countries are salinity, alkalinity, waterlogging, acidity and soil fertility. Soil degradation thus refers to appreciable loss of productivity and is defined as a process which lowers the current and/or potential capability of soil to produce goods or services [2].
Chemical degradation of soil is mediated through processes that induce leaching of bases, development of acidity, deficiencies of certain nutrient elements or accumulation of metallic ions in quantities toxic for plant growth. Reducing conditions of soil following prolonged waterlogging may generate toxic levels of organic constituents and certain metallic ions. It also includes the processes that help accumulate harmful levels of salts of various kinds in the soil.
Excessive removal of bases from surface soil leads to disorder in crop plants grown in highly acid soils met under different pedogenic environments. In high altitude soils of cold climate, soil acidity and base unsaturation is associated with soil forming processes normal to the given climate and local conditions. Apart from lime needs soil degradation is not a major threat to agricultural productivity in the above regions. In high rainfall tropical areas excessive soil acidity may assume discernible proportions in soil types like laterite, lateritic, red and even alluvial soils borne on different parent materials. In the high rainfall forest soils of the hilly region, high organic matter and rainfall tend to produce acidity and base unsaturation in soils with concomitant increase in exchangeable Al of the soil. Often local conditions like rise in water table along with high organic matter and rice cultivation may lead to high active iron in the soil with its content as high as 3%. Besides toxicity of iron, the crops may experience deficiency of phosphorus and zinc. Often toxic concentration of Mn may also be encountered in such soils [3]. All these factors, lead to loss in soil productivity.
Prior to the orogeny of the Himalayan ranges, the Tethys sea extended in a large part of the present Indo Gangetic plains, Western Rajasthan and the Kuchh. With uplift of the Himalayas the foredeep was filled up with detritus. Some of the sea salts precipitated during the process of deposition while other entrapped in the alluvium are present mainly in the arid parts of the country [3]. In the lower Himalayas the rock salt deposits of the pre-cambrian saline series in Himachal Pradesh and the salt range of Pakistan and numerous brine springs in the Shivalik region rich with as much as 3243 mg l−1 chlorides, are important sources of salts [3].
The only source of in-situ release of salts is weathering of minerals constituting the soil regolith below it. Its ample evidences are available in peninsular regions. The alluvial zone in the north is composed of strongly saline sedimentary rocks which originated during the Territory and Pleistocene times. Their decomposition is incomplete in the arid climate. Hence there are constant sources of salts under environments which favour their further break down. For example almost 44% of light minerals in sand dunes of Rajasthan contain orthoclase feldspars which undergo weathering and release minerals like illite and montmorillonite [4]. Salts are natural by products of such weathering.
Run off waters pick up salts on their way to natural depressions. In some areas, the natural settings are such that they have centripetal drainage which attracts salt bearing over land flows from the surrounding areas. Minor channels feeding these basins owe their salinity to salt releasing marine lithological formations [5]. Even rivers flowing in the regions pick up lot of salts. In Luni basin of Rajasthan, India flash flood deposited thick layer of sediments composed of very fine sandy to coarse silty material charged with salts over highly productive agricultural lands rendering them so saline that cultivation had to be abandoned [6].
In the peninsular regions, much of salinity in valley lands is traced to salts travelling from uplands laterally along the interface between the soil and the underlying ‘murrum’ or through the porous ‘murrum’ itself. Irrigation canals and channels are on the ridge causing seepage water to pick-up salts and move down to valley lands. The morphogenesis of salt lakes and palayas in Western Rajasthan of India is traced to the confluence of prior drainage channels. Rain water sinks through their beds and flows subterraneously along buried channel patterns carrying soluble salts washed down from the catchment. Evaporation of water from the bed concentrates salts in the path raising their salinity to as much as 3.2 gkg−1 of salts [7].
The rain in salt water originate either from strong winds that sweep over oceans or salts picked from dust storms causing salt content of rain water to vary with locality and season. Salt additions from rains and winds may thus constitute an important source of salts in soils [7, 8].
Coastal regions experience tidal floods especially through backwater creeks and rivers which spill over lands during high tide. Sub-surface intrusion of sea water in coastal areas is threatening agriculture in those areas [8].
All irrigation waters, irrespective of their source contain some soluble salts (Table 1). Even rivers of the Indo-Gangetic system which are snow fed may carry salty sediments during rainy season and significant salt loads during the lean discharge period. But in the absence of any exit, even small additions of salts over long periods can render the soil saline. In many arid and semi-arid regions, where canal water is scarce or not available, ground water is the sole source of irrigation. Even tank and lakes of many areas carry large salt loads. Prolonged use of such water in low rainfall areas, where natural leaching of salts fall short of their input into the soil, may render irrigated soils saline.
Irrigation source | Surface waters | ||
---|---|---|---|
EC (dS/m) | pH | SAR | |
Ganges system | 142–647 | 7.6–8.4 | — |
Indus system | 370–420 | 7.2–7.7 | — |
Krishna-Godavari | 725–1392 | — | — |
Vedavathi, Karnataka | 1900 | 8.8 | 12.8 |
Gosikere, Karnataka | 1400 | 8.6 | 10.8 |
Etah (U.P.) | 3752 | 9.0 | — |
Kanpur (U.P.) | 1766 | 8.5 | — |
Dodherde (Karnataka) | 1400 | 8.6 | 10.8 |
Nannewa (Karnataka) | 1900 | 8.6 | 26.8 |
Ground waters | |||||
---|---|---|---|---|---|
State | No. of samples tested | % distribution in ECiw (dS/m) classes | |||
Up to 3 | 3–5 | 5–10 | >10 | ||
Punjab | 12,500 | 68.0 | 20.0 | 8.0 | 4.0 |
Haryana | 3637 | 58.5 | 17.5 | 13.2 | 10.8 |
U.P. (Aligarh) | 390 | 78.4 | 18.7 | 2.9 | — |
A.P. (Coastal) | 1082 | 78.2 | 16.1 | 5.7 | — |
Karnataka (Bijapur) | 404 | 87.6 | 8.9 | 3.2 | 0.2 |
Gujarat (Ahmedabad-Kheda) | 505 | 84.0 | 100 | 5.7 | 0.2 |
Salt load of some irrigation waters in India [8].
EC, electrical conductivity; SAR, sodium adsorption ratio; iw, irrigation water.
A major portion of coastal saline soils occurs in the deltaic regions of major rivers, for India, falling either into the Bay of Bengal or the Arabian sea. A relatively smaller area of coastal saline soils occurs as narrow strips of lands along the sea coast and along the water lakes such as Chilika lake in Odisha. The soils of deltaic regions, usually have flat topography and finer texture, than the other types of coastal soils, depending on the geomorphology of flood plains; coarse texture soils may also be found in the deltaic regions. The coastal soils of deltaic region are usually formed from the indirect deposits of alluvial materials going to the sea and transported back by the tides and redeposited in the estuarian/deltaic regions. The coastal saline soils have saline ground water at shallow depth. Both the ground water and the soils are rich in chlorides and sulphates of sodium, magnesium and calcium. The soil salinity and the depth to ground water vary with the season. Soil salinities are maximum in dry season and minimum in monsoon months (Indian Society of Coastal Agricultural Research 1987;5:1-14). The clay minerals vary with the region. The pH of the soils usually varies from slightly acidic to slightly alkaline except that the soils with high content of pyritic materials become strongly acidic on drying. Some such acidic soils are present in the coastal regions of Kerala, Sundarbans delta of India and in Andaman and Nicobar islands. Many a time, the exchangeable sodium percentage (ESP) of coastal saline soils is more than 15, but because of high salt content, it does not show strong alkali soil characters. The soils under cultivation are deficient in available nitrogen and organic carbon varying from 0.1% to 1.0% (Indian Society of Coastal Agricultural Research 1990;8:61-78). The organic matter content and its humic components also differ in different landforms as shown in Table 2. The humic acid (H.A.) and fulvic acid (F.A.) fractions of organic matter for a coastal soil of West Bengal, are given in Table 2. Humus is the major soil organic matter component making up 75–805 of the total [10]. Fractionation of organic matter showed that the fraction of H.A. was the highest (0.31%) in depressed low (DL) soil and the fraction of F.A. was the lowest (0.10%) in the surface layer of the same soil. On the other hand, the F.A. fraction was the highest in non-cultivated deltaic (NCD) soil (0.12%) for which these soils were more capable of infiltration. DL soil with greater fraction of insoluble humic acid exhibited less cumulative infiltration. Mud flat (MUD) soils showed intermediate values (0.11%). In the lower soil layers also H.A. percentage was higher in the DL soils (0.27–0.29). The H.A./F.A. ratio decreased with depth (0.7–0.5 for NCD and 3.1–3.0 for DL land soils) [11, 12]. Presence of humic acid in soil generally decreases volumetric water content of soil. Decline in water repellency of soil is due to the presence of water soluble fulvic acid [13, 14]. In general, with increase in EC values, there was a decrease in organic carbon content. This may be attributed to the decrease in activity of organic matter sequestering organisms. The organic C percentage was high at EC values 4–4.5 (dS/m) which may be because of addition of F.Y.M. (Figure 1).
Name of soil | Total organic matter | H.A | F.A. | H.A/F.A. ratio |
---|---|---|---|---|
0–20 cm | ||||
NCD | 2.1 | 0.08 | 0.12 | 0.7 |
MUD | 1.8 | 0.18 | 0.11 | 1.7 |
DL | 1.1 | 0.31 | 0.10 | 3.1 |
20–40 cm | ||||
NCD | 1.9 | 0.08 | 0.13 | 0.6 |
MUD | 1.1 | 0.16 | 0.10 | 1.6 |
DL | 0.93 | 0.29 | 0.09 | 3.0 |
40–60 cm | ||||
NCD | 0.88 | 0.07 | 0.11 | 0.5 |
MUD | 1.1 | 0.14 | 0.09 | 1.5 |
DL | 0.87 | 0.27 | 0.09 | 3.0 |
Humic acid, fulvic acid content of organic matter and their ratio [9].
NCD, non-cultivated deltaic; MUD, mudflat/mangrove; DL, depressed low land.
Soil salinity and organic carbon relationship [
The distinguishing characteristics of a sodic soil are high exchangeable sodium percentage (ESP) sufficient to interfere with plant growth; high sodium adsorption ratio (SAR) of saturation extract; presence of large concentration of sodium carbonate type salts, and low permeability. In sodic soil the ESP is more than 15, ECe (electrical conductivity of saturation extract) is less than 4 dS/m and pH of the saturation paste is usually more than 8.5. The work at the Central Soil Salinity Research Institute, Karnal, India suggests that for diagnostic purposes, pH 8.2 of the saturation paste may be taken as the lower limit of pH. In literature such soils have often been referred to as alkali soils. Characteristics of a typical sodic soil are presented in Table 3.
Soil | Saturation extract (mmolL−1) | ||
---|---|---|---|
pHs | 10.3 | Na+ | 37.0 |
ECe | 3.4 | K+ | 0.1 |
CEC [cmol (p+) kg−1] | 7.7 | Ca2+ | 1.4 |
Exchangeable [cmol (p+)kg−1] | Mg2+ | 0.6 | |
Na+ | 5.1 | CO32− | 12.0 |
K+ | 0.3 | HCO3− | 5.4 |
Ca2+ | 1.2 | Cl−1 | 10.2 |
Mg2+ | 0.7 | SO42− | 12.6 |
ESP | 66.2 | SAR | 37.0 |
Characteristics of a typical sodic soil from Karnal, Haryana, India.
Source: Report No. 8, Central Soil Salinity Research Institute, 1978, p. 72. pHs refers to pH of saturation soil paste.
The problem of sodic soils is the high exchange sodium percentage. Obviously, the basic principle underlying reclamation of these soils is to adopt those ameliorative measures by which the exchangeable sodium will be replaced by calcium and the exchangeable sodium thus released as sodium salts will be leached out of the root zone. Because of low cost and easy availability, gypsum (CaSO4.2H2O) has been used widely and intensively as an amendment for reclamation. Hydraulic conductivity of sodic soils is very significantly increased by gypsum application and this result in increased yield of crops. An example of increase in crop yield by gypsum application is given in Table 4.
Gypsum** (t/ha) | Wheat cultivation at 1970–1971 | Rice 1971 | Wheat 1971–1972 | Rice 1972 | Wheat 1972–1973 |
---|---|---|---|---|---|
0 | 0 | 4390 | 1520 | 7180 | 1350 |
7.5 | 1890 | 6210 | 2790 | 7230 | 1960 |
15.0 | 3490 | 6390 | 3450 | 7130 | 2430 |
22.5 | 4160 | 7080 | 3720 | 7170 | 2260 |
30.0 | 3790 | 6660 | 3980 | 7030 | 2740 |
CD (p = 0.05) | 640 | 810 | 70 | NS | 500 |
Effect of gypsum treatments on the yield of wheat and rice (kgha−1).
Gypsum was applied to wheat in 1970.
Source: Soil Science 1970;127:79.
Waterlogging of soils occurs when water balance of an area gets disturbed due to external inputs of water. Important source are heavy rains, overland flows, seepage from canals, tidal flooding especially through back water canals and coastal lakes. The soil situations favouring excess water concentration in a given area are basin type of topography with no natural outlet of water. Waterlogging creates anaerobic condition in soil which hampers the activity of beneficial soil microbes [14].
Soil microflora, just like higher plants, depends entirely on soil for their nutrition and growth.
Water is a major component of protoplasm in a microbial cell and is essential for growth. In the presence of excess water, say waterlogging, the environment becomes anaerobic because of lack of soil aeration, the aerobes becomes suppressed and inactive and anaerobic bacteria dominates. Nitrogen-fixing algae like
Temperature is the most important factor influencing the biological processes and the microbial activity. The optimum temperature range at which a particular microorganism grows is narrow. Most of the soil organisms are mesophiles and grow well between 15 and 45°C.
Microbes consume oxygen from soil air and give out carbon di oxide. Waterlogging reduces soil aeration and carbon di oxide is accumulated in soil which is toxic to the microbes.
Bacteria, in general prefer near neutral to slightly alkaline reaction between pH 6.5 and 8.0; fungi grow in acidic reaction between pH 4.5 and 6.5.
A soil in bad physical condition as in degraded soil has not having good aeration and water supplying capacity which affect optimal microbial activity.
Continuous use of inorganic fertilizers coupled with depletion of organic matter results in deterioration of soil structure and soil productivity. It leads to reduced input/output ratio unless soils are replenished with organic matter through green manure, FYM, compost or through microbial activity. Due to repeated application of BGA biofertilizer, soil organic carbon content is not only maintained but enriched too [15, 16]. The increase in carbon content of saline soils has been shown to be up to 22%. The microbial polysaccharides are regarded as the most important natural products in the formation and stabilization of soil aggregates. Presence of excess neutral soluble salts or high level of sodium in soils leads to rise in soil pH and soil finally become saline or sodic. These soils usually give poor crop yields or crops altogether fail. The crop failure is brought about either by nonavailability of plant nutrients or by the toxic effect of sodium ions per se. Repeated application of suitable BGA strains in such soils helps to bring down the level of soluble salts, pH towards neutrality and sodium content in exchange complex [17]. The cumulative effect of reduction in soil pH, electrical conductivity and exchangeable sodium, improvement of soil aggregation and permeability of air and water, together with enrichment of soil carbon content brings an overall improvement in soil health and thus productivity.
Large numbers of field trials were conducted in different agroclimatic regions of India to assess the effect of algalization on rice yield. Agencies involved were state department of agriculture, All India Coordinated Rice Improvement Project (AICRIP), Hyderabad and progressive farmers [18]. In areas where chemical N-fertilizers are not used for various reasons, algal inoculation enhances the crop yield with a minimum of 45 and a maximum of 32.8% in different places with an Indian average of 16.1% (Table 5). Even at the recommended levels of chemical nitrogen fertilizer being used in different areas application of BGA bio-fertilizer results in an increased yield of about 8.85%. Depending upon the level of nitrogen fertilizer and agro-climatic zones the yield increase varies from less than 1% to 12.28% (Table 6).
State/organization | No. of trials | Yield (t/ha) | % increase over control | |
---|---|---|---|---|
Control | BGA | |||
Andhra Pradesh | 1 | 3.64 | 4.44 | 21.9 |
AICRIP | 20 | 3.04 | 3.37 | 10.8 |
Bihar | 1 | 2.13 | 3.06 | 32.8 |
J & K | 3.75 | 3.90 | 4.0 | |
Madhya Pradesh | 1 | 2.48 | 2.85 | 14.9 |
Maharashtra | 161 | 3.05 | 3.91 | 28.1 |
Orissa | 91 | 2.97 | 3.71 | 24.6 |
Punjab | 1 | 5.04 | 5.27 | 4.5 |
Uttar Pradesh | 1 | 3.29 | 3.82 | 16.1 |
Total trials | 17 | |||
Average | 294 | 3.28 | 3.81 | 16.1 |
Yield of rice due to algalization in absence of inorganic nitrogen fertilizer [18].
State/organization | No. of trials | Nitrogen kg/ha | Average yield t/ha | % increase |
---|---|---|---|---|
J & K, M.P., U.P. | 7 | 20 20 + BGA | 3.78 4.15 | 9.74 |
AICRIP, Maharashtra | 43 | 25 25 + BGA | 3.71 4.09 | 10.21 |
J & K, Kerala, U.P. | 15 | 30 30 + BGA | 3.49 3.92 | 12.28 |
J & K, M.P., U.P. | 7 | 40 40 + BGA | 4.27 4.51 | 5.54 |
A.P., J & K, Maharashtra | 29 | 50 50 + BGA | 4.38 4.90 | 11.93 |
Kerala, M.P., U.P. | 22 | 60 60 + BGA | 3.87 4.34 | 11.93 |
Maharashtra | 27 | 75 75 + BGA | 4.46 4.88 | 9.50 |
Punjab, U.P. | 6 | 80 80 + BGA | 5.56 5.58 | 0.23 |
Kerala, U.P. | 14 | 90 90 + BGA | 3.64 3.99 | 9.77 |
A.P., Maharashtra, T.N. | 46 | 100 100 + BGA | 5.02 5.38 | 7.22 |
Kerala, Punjab, U.P. | 15 | 120 120 + BGA | 4.73 5.03 | 6.25 |
A.P. | 1 | 150 150 + BGA | 5.84 6.52 | 11.60 |
Total trials Average % increase | 232 |
Effect of algalization on the yield of rice at different levels of nitrogenous fertilizers [18].
Use of microbes in resource management has been an age-old practice although the scientific reasoning for such practices has come to be known only recently. For instance, a common wetland plant,
Author acknowledges the contribution of Director, Central Soil Salinity Research Institute, Karnal, India for his encouragement during doing the study and research.
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