Estabilidad local del sistema (6-4)

Like BCS IV, the fifth biomass conversion system encompasses a mobile pyrolysis system as an upgrading unit. For the conversion, however, a different technology is assumed. After transportation of both pyrolysis products bio-oil and bio-char to a central conversion site, only the bio-oil is used to generate electricity, i.e. in a direct-injection gas turbine at a central conversion site. The bio-char is not used to generate electricity. Instead, it is assumed to be sold to agro-chemical companies as a fertiliser additive.

For the upgrading unit, the same assumptions as for BCS IV are made, i.e. up to 416kg of biomass at a moisture content of 10% can be fed into the system, yielding 208kg of bio-oil and 113kg of bio- char (55wt.% and 30wt.% respectively based on dry biomass, i.e. dry basis). The conversion unit is based on a Tarsus 60 X1 combined-cycle gas turbine plant – refer to the Gas Turbine World Handbook (2010) and Farmer and De Biasi (2010). Due to its high oxygen content and the presence

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of a significant proportion of water, the heating value of bio-oil is much lower than for fossil fuel (Venderbosch and Prins, 2010: 197). These and other differences in fuel properties result in relatively low conversion efficiencies for bio-oil in gas turbines. For this study, a conservative 26.0% was assumed, similar to values found in Lupandin et al. (2005), resulting in a net biomass requirement of 60 358 tonnes per year at 10% moisture content. Around 3.3% energy input on an electrical energy output basis is required for the system. Besides the electrical energy output of 5MWel, a thermal excess energy of 12.5MWth is generated (thermal excess energy ratio of 2.5).

Another by-product is the marketable bio-char, which amounts to 16 461 tonnes per year.

The conversion unit generates electricity for 24 hours on 330 days per year, whereas the upgrading units produce bio-oil and bio-char only during two shifts, each of eight hours a day. Only bio-oil is used for generating electricity. Both, the time and product constraints, result in 27 mobile fast- pyrolysis units being required, ensuring continuous production of the conversion unit. Therefore, the total capital costs for the upgrading units are calculated at R319.91 million.

In order to conform to the proposed 5MW electrical energy output requirements, the six-tenth factor rule had to be applied for the conversion unit. Since the Tarsus 60 X1 has a proposed electrical energy output of 7.3 MW and a capital cost of R54.44 million ($6.89 million), the base module costs for the gas turbine are calculated as R43.38 million, and together with installation costs of R34.70 million (80% of the base module cost), the total capital expenditure for the conversion unit is R78.08 million.

Similar to BCS IV, one skilled operator is required to run a mobile fast-pyrolysis unit, resulting in 54 skilled operators being required for the upgrading units. They are supervised by two upgrading unit managers, as well as three engineers. One plant manager, one engineer and three operators are assumed to be required for the conversion unit. The total annual employment costs are calculated as R6.79 million. Expenditure for O&M for both the upgrading units as well as for the conversion units is assumed to be 2% of the base unit costs, i.e. R6.40 million for the mobile fast-pyrolysis systems and R0.87 million for the gas turbine, resulting in a total operating expenditure of R14.06 million per year.

For the LCA, the flue gas emissions of the mobile fast-pyrolysis units are as assumed for BCS IV (refer to column 9 in Table 45, above). Similarly, the emissions to air from the compressed combustion of bio-oil in a gas turbine are also per the combustion of bio-oil for BCS IV (refer to column ten in Table 45, above). However, the flue gas emissions per produced energy unit are less for BCS V than for BCS IV, due to the greater conversion efficiency.

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As mentioned above, the transmission, distribution, and use of the generated power are not covered. Similarly, the transport to and usage of bio-char by industrial consumers is not included in the assessment. Nevertheless, the bio-char is assumed to be sold to fertiliser companies for addition to soils. The stability of bio-char does vary with feedstock, processing, and environmental conditions. For this assessment, high yields of stable carbon are assumed. With this in mind, a conservative estimate of 80% of the C in the char as being stable is assumed (Lehmann et al., 2009; Baldock and Smernik, 2002). The remaining 20% of the C is labile and is released into the atmosphere as biogenic CO2 within the first few years of applying it to the soil (Roberts et al., 2010).

5.9 Conclusions

In the goal and scope definition (the first phase of a life-cycle assessment), as described in Chapter 4, a set of 37 lignocellulosic bioenergy systems using lignocellulosic biomass grown in short- rotation coppice systems as a feedstock was defined. This included a definition of the functional unit and system boundaries. Within the Cape Winelands District Municipality, which forms the geographical boundaries, four biomass procurement areas, differing in biomass productivity and availability of biomass production sites, were selected.

The second phase of a life-cycle assessment is defined as a life-cycle inventory analysis (LCI), as described in Chapter 5 involves data collection and calculation procedures to quantify the relevant inputs and outputs of a product system (ISO 14040, 1997). Thus, based on the LCA framework, each process/activity illustrated in Figure 11 leading to the set of 37 lignocellulosic bioenergy systems for the Cape Winelands District Municipality was specified, not only in terms of environmental input and output flows, as defined in the ISO standards 14040-14044, but also in terms of financial-economic and socio-economic performance. The financial-economic data comprises capital and operating expenditure for each unit-process, as well as expected revenues from selling electricity, the main product, and from selling by-products such as thermal energy for cooling or heating, or bio-char to the fertilising industry. Furthermore, for each system, the amount and type of the direct employment creation potential, a socio-economic indicator, were determined. Since each LBS consists of at least five production phases, namely, primary biomass production; harvesting and forwarding; biomass pretreatment including comminution, drying and fast- pyrolysis; secondary transport; and biomass upgrading and electricity generation, a myriad of information and data across the four biomass procurement areas has been collected and processed. The following chapter encompasses the life-cycle impact assessment (LCIA), the third phase of an LCA, which is – from conventional LCA perspective– aimed at assessing the results of the life- cycle inventory to better understand their environmental significance by translating the

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environmental loads of each LBS into environmental impacts, such as global warming potential or eutrophication potential.

Furthermore, the relevant data for the financial-economic assessment is translated by means of multi-period budgeting into key parameters, such as internal rate of return or risk of investment in terms of cost, describing the financial performance of each LBS per biomass procurement area. Similarly, the socio-economically relevant data describing the potential of creating direct employment is translated into three income categories, also to allow a comparison of the LBSs.

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6 CHAPTER: LIFE-CYCLE IMPACT ASSESSMENT

6.1 Introduction

The previous chapter covers the life-cycle inventory, based on the LCA framework. This involves data collection and calculation procedures to quantify the relevant inputs and outputs occurring during the production phases for each of the lignocellulosic bioenergy systems (LBSs) considered. This chapter deals with the life-cycle impact assessment (LCIA), which is the third phase of the life-cycle assessment as described in the international standard (ISO 14040, 1997). The purpose of the LCIA is to assess a product system’s life-cycle inventory results, to better understand their environmental significance (ISO 14042, 2000). The impact assessment is achieved by translating the environmental loads from the inventory results into environmental impacts, such as acidification, ozone depletion, and global warming potential (Baumann and Tillman, 2004: 129). There are several reasons for translating environmental loads into impacts, such as to make the results more environmentally relevant, comprehensible and easier to communicate, as well as to improve the readability of the LCI results. The number of result parameters for the latter can range from 50 to 200 or even more (Baumann and Tillman, 2004: 129). Through the LCIA, the number of parameters can be reduced by grouping the environmental loads of the inventory results into environmental impact categories. The LCIA is also useful for making results more comparable, which is particularly relevant when comparing a set of alternatives, as is the case in this study. Other important considerations in terms of environmental impacts are, for instance, the effects of introducing bioenergy systems on biodiversity, as well as on water balance. The biodiversity intactness index or the water footprint are assessment methods which have the potential to determine such environmental impacts, but since they are not included in the commonly accepted LCIA methods, only a general discussion is given below. In addition, both environmental impacts have been dealt with a priori in a land suitability assessment by means of geographic information systems (GIS).

Furthermore, using the LCA framework as a guideline, a set of financial-economic and socio- economic criteria are defined, against which the LBSs are assessed. By means of multi-period budgeting (MPB), financial-economic data is translated into key parameters describing the performance of each LBS, making them more comparable. The financial-economic criteria are used to describe the LBSs’ profitability and cost structures, the former being an indicator of overall performance, and the latter being an important consideration in terms of risk of investment. This

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allows for a comparison of LBSs, as well as for a comparison of each LBS in terms of biomass procurement area.

The socio-economic impact of the LBSs in terms of employment creation potential are subdivided into three income categories, based on the productivity data of each production phase used in the MPB models. Similar to the environmental impacts biodiversity and water balance, food security, another socio-economic impact, is briefly discussed.