Economic development in Lebanon has required increased access to energy as both urbanization and industrialization create greater energy demands. Production of energy based on imported oil has become a constraint to development of the economy and improvement of the environment. Energy consumption leads to pervasive externalities, ranging from local pollution to global greenhouse gases that are not reflected in energy supply costs and planning efforts. In 1994, the total energy consumption in Lebanon was 28.276×10⁶ GJ (7854.5 GW h), of which industry used 10.14×10⁶ GJ (2816.6 GW h). Industrial sector electricity comes from two sources: 6.228×10⁶ GJ (1728 GW h) are generated by industry using diesel generators and 3.919×10⁶ GJ (1088.6 GW h) are purchased from the National Electric Utility, EDL (Electricite du Liban). The Lebanese manufacturing industry emitted about 4.7 million tonnes of CO₂ in 1994 from energy use and processes.

The industrial sector in Lebanon is quite complex and heterogeneous including all manufacturing and construction activities. Industries in Lebanon range from those that transform raw material into more refined form (e.g. steel, cement, plastics and glass) to those that produce highly finished products (e.g. the food processing, pharmaceuticals and paper industries). Many different processes are used to produce various products without proper qualified technical assessment. This complexity makes it difficult to conduct a very accurate assessment in a “bottom-up” approach. The development of the baseline scenario relied on available data on major plants’ outputs, and on reported amounts of fuels used by the industrial sector as a whole and by the major plants of specific industries. Information about specific processes used by a few industries were obtained, and discussions with several plants’ engineers in some industries clarified the current status of energy use in Lebanese industry and the wide opportunities for mitigation of greenhouse gases (GHG) emissions.

The first objective of this work is to establish, to the best of our knowledge, a realistic most likely scenario for the energy demand associated with the industrial sector. The development of the baseline scenario for the industrial sector will proceed by considering the cases of cement production and steel separately, to investigate mitigation options for those processes. Energy use in industry will be considered by lumping different types of industries in terms of US$, value and deriving the energy intensities per dollar value of output for each industrial sub-sector. Whenever actual production data, fuel and electricity consumption data are available for a given industry, they are separately considered.

The second objective of this work is to target by year 2005 a 15% reduction of CO₂ emissions from the base year value and a 20–30% reduction of CO₂ emissions by the year 2040. Several mitigation scenarios will be developed and evaluated in comparison with the two baseline scenarios of low and high economic growth. The mitigation options selected for analysis are screened on the basis of the GHG inventory and expert judgement of the viability of wide-scale implementation and economic benefits. Using macroeconomic assessment with a bottom up approach and energy price assumptions, the LEAP Software (long-range energy alternatives planning system) is used to compute the final estimates for potential GHG emissions and costs for the mitigation analysis. The mitigation options will be analysed and ranked by single effect and by multiple effect in combination with other options. Each mitigation option will be developed with multiple scenarios, reflecting different economic indices.

The GHG emissions from energy use in manufacturing industries and construction represent about 24% of the total emissions of the energy sector, and 24% of total CO₂ emissions from all sectors in Lebanon for the base year 1994. Energy sources for industrial use in Lebanon for the year 1994 are divided into four major sources: electricity, gas oil/diesel, residual fuel oil, and liquid propane gas (LPG). The amounts of fuels that were used by the industry in the base year are given in Table 1, as reported in the Lebanese National GHG Emissions Inventory. Forty-two percent of the gas oil/diesel used by the industry was for power generation. Fuel oil and LPG are mainly used for combustion in boilers and furnaces. The coal is used mainly in the cement industry.

The baseline scenarios for projecting energy use in industry and the corresponding GHG emissions for Lebanon reflect technologies, activities and practices that are likely to evolve. The scenarios are directly linked to the economic conditions in the country. In most industries, the industrial $ value growth rate is assumed to be the same as the economic growth of the gross domestic product (GDP). To minimize uncertainty in the development of the baseline scenario, more than one set of values of economic indices is used to place minimum and maximum bounds on projected energy use in industry. Table 2 lists all the different economic indices that are used in this sector to develop the baseline scenario and for assessment of the mitigation options. Two baseline scenarios are created using a low economic growth index (case BA) and a high economic growth index (case CA), while the other economic indices (inflation and discount rates) are used later for the mitigation options.

The industrial sector is divided into five sub-sectors. The division is done on the basis of physical data availability for specific industries. Industries whose physical data on production and energy use are not available are lumped into one sub-sector for which the US$ value of output is used for the activity level, and the overall energy intensities are calculated based on reference quantities of fuel and electricity use in the country. The emission factors of GHG emissions for the different types of industrial activities and fuels are calculated from Intergovernmental Panel on Climate Change (IPCC) methodology and from the environmental database provided through the LEAP software. The projections for energy use for short and long term planning are based on the growth rate of the economy with the exception of two sub-sectors, namely the cement industry and bakeries. The bakeries’ energy use is projected using the population growth rate while the cement industry follows projections based on demand and future government planning for new cement plants. All the industrial sub-sectors’ branches and energy intensity data are shown in Table 3a and b for the baseline scenarios BA and CA, respectively.

The energy demand represented by fuel type for the industrial sector is presented for both scenarios BA and CA at low and high economic growth rates. Electricity use in the sector is also presented directly and not in terms of fuel used to produce electricity, which is either fuel oil or diesel, as this is considered in the supply side. Table 4a shows the energy demand for Lebanon in millions of gigajoules for scenario BA (economic growth is 3%). Table 4b shows the energy demand for Lebanon in millions of gigajoules for scenario CA (economic growth is 6%).

The energy demand shows that residual fuel oil is the largest energy source used by industry, followed by diesel oil, then electricity. The fuel oil has a high sulfur content and is mainly used in boilers that are not so efficient. The high usage of gas oil/diesel is mainly due to bakeries. The low growth rate scenario shows that energy consumption will increase by the year 2005 by 28% over the current level, and will triple by the year 2040. This implies an average annual growth of 2.5% for the whole period in scenario BA. The high growth rate scenario shows that the energy consumption will increase by year 2005 by 70% over the current level and by 950% by year 2040. This implies an average annual growth of 5.94% for the whole period in scenario CA.

The electricity supply comes from combustion of two fuel sources to the industry, diesel oil and fuel oil. If the overall efficiency of the diesel plants is assumed to be 25% and that for the fuel oil plants assumed to be 42%, then the amounts of fuel required by industry for electricity production can be easily calculated from the demand. Table 5 shows the fuel demand for electric power generation in both scenarios BA and CA.

There is concerted international effort to reduce green-house gas (GHG) emissions to the atmosphere. Anthropogenic GHG emissions are due mainly to fossil energy utilization. The most abundant GHG is CO₂, which is the product of combustion of carbonaceous fuels. Of the combustion of the three main fossil-fuels: natural gas, liquid petroleum fuel and coal, coal emits the largest amount of CO₂ per unit of heat produced because the C/H ratio in its chemical composition is the highest.

The most effective way of reducing CO₂ emissions is by energy conservation both at the user end, and also by more efficient production of power and/or heat. This will reduce also the emissions of the pollutants such as SO_x, NO_x and particulates. In large power plants, the efficiency of steam generation is quite high and fuel can be saved mainly by improving the thermodynamic cycle of power generation. In contrast with the large power plants, small industrial boilers which produce steam for the manufacturing of textiles, paper, ceramics, etc., and for the heating of households and offices, have relatively low efficiencies, leaving plenty of opportunity for fuel savings and emission reductions by improved fuel treatments and boiler-house practices.

The study presented in this paper focuses on the industrial boiler sector in China. The large coal consumption of this sector, about 400 million tons (Mt)/year, makes it the target of our investigation. However, the problem of a large number of small boilers dispersed spatially at sites nation-wide constitutes challenges to remedial action. The UN Global Environmental Facility (GEF) published data for 1991 as part of a cooperative project entitled “China: Issues and Options in GHG Emissions Control—Pre-feasibility Study on High Efficiency Industrial Boilers”. The report contains recommendations to transfer foreign, advanced technology to Chinese boiler makers for the manufacture of more efficient and cleaner industrial boilers. It would take, however, a long time to replace a significant number of the existing industrial boilers and it is recommended that action is taken in the mean time to improve their performances. These recommendations, which could be seen to be complementary to those of the GEF report, are based on our field studies and the statistical evaluation of the data.

The statistics for 1991 have shown that the number of industrial boilers in China reached 432,000 (983,000 ton-steam/h) with a total annual consumption then of 350 Mt of coal, i.e. around one third of the total coal production in China. The industrial boiler sector is the main contributor to pollution: 6.2 Mt particulate emissions accounting for 36.6% of the total; 5.2 Mt SO₂ accounting for 38.8%; and more than 500 Mt of CO₂. With the fast growth of the economy, the environmental emissions are getting worse. The average operating efficiency of the industrial boilers is in the range of 60–70%. Accordingly, more than 60 Mt coal is wasted and over 100 Mt of excess CO₂ is emitted to the atmosphere annually. It can be estimated that, by now, the boiler population has risen to more than 500,000 with a total coal consumption of over 400 Mt per annum.

Two hundred and fifty certificates of boiler thermal-balance tests from three Provinces: Shanxi, Henan and Jiangsu, were collected for this study. These tests were conducted by specialized teams, licensed by the provincial governments during the past decade. Among the 250 boilers, the medium size boilers (4, 6 and 10 t/h) accounted for 74% of the total number, while few boilers of 1 or 35 t/h were tested. The average capacity of the 250 boilers is some 6.5 t/h, i.e. much larger than the 2.3 t/h, the average size of the total national industrial boiler population. The coal-burning equipment of the 250 boilers is dominated by the type of traveling grate stoker accounting for 96.4%: chain-stoker 72.8% and reciprocating stoker 23.6%, respectively. The higher grade bituminous coal [Lower Heating Value (LHV) 5016 kcal/kg and volatile matter (VM) in dry ash-free coal >20%] is the major fuel in these boilers, though a lean coal (VM=1 20%) is used in the majority of boilers in the Shanxi province. The authors visited the three teams and six boiler houses to confirm the correctness of measurements and test conditions. Table 1 contains the statistical average data of the boiler tests carried out in the three provinces.

The test data are illustrated by histograms of boiler efficiency, excess-air factor, waste-gas temperature, CO concentration and carbon content in slag and fly ash (combined), in Fig. 1, Fig. 2, Fig. 3, Fig. 4 and Fig. 5 respectively. Fig. 6 and Fig. 7 show the distributions of carbon content in slag and fly ash, respectively.

In order to show the relationship between input and dependent variables, it was necessary to better specify the coal type, boiler size, and firing system in the samples. For this purpose, 69 bituminous coal-fir#ed chain-stoker boilers of 4 t/h were chosen from the 250 tests. Fig. 8, Fig. 9, Fig. 10, Fig. 11 and Fig. 12 show the boiler efficiency, the waste-gas temperature, and carbon contents in the slag and fly ash as functions of the excess air, and the efficiency vs the thermal load for this smaller sample of 69 boilers.

The following observations are made:

1. The average boiler efficiency is 65% for the average capacity of 6.5 t/h and for the use of high-quality bituminous coal. This efficiency is lower than that given in the Technical Guidebook for Industrial Boilers which require an efficiency of 76% for the same boiler type, capacity and for using the same type of coal. The average efficiency in Shanxi province is lower than in the others by 5%. One reason for this lower efficiency is the coal type, a lean coal used mainly in Shanxi, which is relatively difficult to burn completely.

2. The highest boiler efficiency is 82% while the lowest is 40%. Only 7.6% of the boilers reach the recommended level of 76%. According to the efficiency range, the boilers can be divided into three groups: (1) less than 65% accounting for 47.1%; (2) 6 70% making up 28%; and (3) higher than 70% taking up 24.8%.

3. One of the main reasons for the low boiler-efficiency and high CO₂ emission is the very high excess-air factor (the excess air factor is the multiple of the stoichiometrically required combustion air): the average is 2.8 compared with 1.5 to 1.8 specified in the standard. The highest value reached is 6.9. Only 20% of boilers are operated in the recommended range of the excess air.

4. The average waste-gas temperature is 188°C, i.e. slightly higher than the recommended 180°C. This may be due to economizers installed in most boilers to recover the heat from the exiting flue-gas by preheating the feed water. However, the high excess-air would be another reason for the low exit-gas temperature.

5. In Shanxi, the average CO is two times higher than normal, so causing low efficiency and more CO₂ emission. The highest CO is 2.8%, resulting in a boiler efficiencies loss of 10.2%, i.e. 10 times higher than normal. The average CO concentration in Henan and Jiangsu seems to be in the reasonable range of 0.05–0.07%. It is not an optimum, however, because a high value of excess-air is used to reduce the CO concentration.

Steam reheating is an important feature in steam-power plants. The main objective of reheat is to increase the power output and, under certain conditions, the thermal efficiency of the plant, thus improving plant performance. There is a wide range over which reheat pressures can be varied. Hence, for every set of steam conditions, an optimum value of reheat pressure exists that will yield an optimum steam turbine-boiler reheat-cycle. Second-law analysis was considered for optimizing conventional plants. [5 and 6] provided a second-law analysis for the optimization of cogeneration steam plants. Some constraints exist and present a limiting factor for the range of choice of reheat pressures. [7] analyzed the influence of reheat temperature and pressure on regeneration-cycle performance. Their results indicated that the most economical gain would occur when the reheat temperature increases no more than 30 K from the saturation temperature corresponding to the steam pressure from a high-pressure turbine-exhaust. Silvestri et al. concluded that both first- and second-reheat pressures can be varied over an appreciable range only with a limited effect on the heat rate. Equipment designs and operating concerns that place limits on reheat-pressure selection were also noted in both studies.

Recent studies indicate the importance of reheat temperature control and numerical modeling of reheat regenerative furnaces. The most recent analyses indicate the possibility of attaining high plant-efficiencies, over 45%, as a result of using efficient steam turbines, even reaching 67% with multiple Rankine topping cycles. However, improving the performance of existing plant configurations through optimization of reheat pressures remains a desirable objective for the next decade or so.

The present paper is an extension of a previous paper by Habib et al. which applied first-and second-law analysis for optimizing the reheat pressures of non-regenerative power plants. The analysis in the present paper is extended to include feedwater heating in the plant.

The layout of the thermal-power plant considered in the present work is shown in Fig. 1. The plant utilizes the reheat regeneration-cycle and comprises a boiler with two reheats, multistage turbines, a condenser and open-type feed-water heaters.

The thermal power plant can be divided into two main units, the steam generator which consists of the furnace and the heat exchanger unit and the turbine cycle which consists of the high-pressure turbine, intermediate-pressure turbine, low-pressure turbine, condenser and feed-water heaters.

The exergy destruction in the steam generator can be calculated as described in the following paragraphs.

The exergy destruction in the furnace occurs as exergy losses from the boiler in the exhaust gases, thermo-mechanical loss and chemical loss.

The contours of the first-law thermal efficiency are shown in Fig. 2. The figure indicates a maximum efficiency close to high reheat-pressure P₁ of 25% of the boiler pressure and at low reheat-pressure P₂ of 4.4% of the boiler pressure. The diagonal of the figure with P₂=P₁ presents the single reheat case. For this case, the maximum efficiency is almost 0.357. Thus the improvement in ?_I due to incorporating the second reheat is approximately 0.02 or 5.6%. The contours provide a spectrum for the selection of optimum thermodynamic reheat-level for a specific application. For the same improvement in efficiency, a selection of reheat pressures is available to suit different temperatures at the inlet to the boiler. These temperatures are usually limited by the boiler design. It is anticipated that low temperatures at the inlet to the reheaters at the second reheat-pressure will result in a high temperature at the exit of the low-pressure turbine.

The second-law efficiencies of the plant, steam generator and turbine cycle are shown in Fig. 3, Fig. 3 and Fig. 3 respectively. Fig. 3(a) indicates an improvement of.0261 or 6.3% in the second-law efficiency due to incorporating the second reheat pressure. Compared with Fig. 2, Fig. 3(a) indicates that the maximum second-law efficiency occurs at the same location as the maximum of the first-law efficiency. Comparison of the three Fig. 3, Fig. 3 and Fig. 3 indicates different optimum conditions of reheat pressures for the plant, the steam generator and the cycle. The optimum steam generator efficiency occurs at P₂=56% and P₁=20% of the boiler pressure. Corresponding values for the cycle are 12.6 and 2.8%. The figure also indicates that the steam generator efficiency is more sensitive to changes in P₂ than in P₁. Cycle efficiency is more sensitive to P₁ than P₂.

At high values of reheat pressures, the temperature difference through which heat is transferred from hot gases to the steam is lower and therefore irreversibilities are expected to be lower. The irreversibility rates in the steam generator are shown in Fig. 4 and Fig. 5. It is indicated by Fig. 4(a) that the minimum irreversibility rates occur at P₂=56.2% and P₁=20% of the boiler pressure. The irreversibility in the steam generator is more sensitive to changes in P₂ than in P₁. The irreversibility losses in the steam generator are due to availability destruction in the furnace and boiler heat-exchanger sections. The irreversibilities in these two sections are shown in Fig. 4 and Fig. 4. These figures indicate that the heat-transfer irreversibility losses are more than 3 times greater than the furnace irreversibility losses. The furnace irreversibility losses are due to availability destruction as thermo-mechaincal, chemical and exergy destruction in stack gases. These are presented in Fig. 5 and Fig. 5.

The irreversibilities in the cycle and its components are shown in Fig. 6, Fig. 7, Fig. 8 and Fig. 9. The cycle irreversibility losses are given in Fig. 6 and account for about 12% of the exergy input to the power plant. The cycle irreversibility losses are more sensitive to the low reheat-pressure P₁ than the high reheat-pressure P₂. The irreversibility losses occur at P₂=10% and P₁=2.5% of the boiler pressure. The irreversibility losses due to the cycle components, the condenser, the three turbines and the two feedwater heaters are given in Fig. 7, Fig. 8 and Fig. 9. The losses in these components at the optimum reheat pressures are 49, 7, 18, 11, 3 and 12%, respectively.

The choice of reheat pressures which may be available to the plant designer to achieve optimum efficiencies is limited by some specific constraints. These are the quality of the steam at exit of each turbine. A typical presentation of these constraints is given in Fig. 10 where the quality values at the exit of the high pressure turbine, x₁, and the intermediate pressure turbine, x₂, were kept above 1.0. In the case of the turbine preceding the condenser, the quality value x₃ was maintained above 0.9. Considering the constraints on quality at exit of each turbine, the choice of the two reheat pressures will be limited to the region bounded by x₁=1.0, x₂=1.0 and x₃=0.9 as indicated by Fig. 10.

A thermodynamic optimization procedure of reheat pressures for the reheat regeneration thermal-plant is presented. The results provide the different optimum low- and high-reheat pressures for each of the steam generator, turbine-cycle unit and the overall plant. It is concluded that the first and second law efficiency are more strongly influenced by the high reheat-pressure than the low reheat-pressure.

There is enough scientific evidence to support the contention that gradual changes in global climate are being caused by excessive accumulation of certain gases in the Earth’s atmosphere, due mainly to anthropogenic activities. One major consequence of the excessive accumulation of these gases in the atmosphere is the greenhouse effect. Climate change is a global issue, and hence the need for intergovernmental action to combat it. The recognition of the threat posed by the continued emission of greenhouse gases into the atmosphere led to the Rio Summit on Climate Change and the United Nations’ Framework Convention on Climate Change (UNFCCC) in 1992. Nigeria ratified the UNFCCC in August 1994. Carbon dioxide (CO₂) is by far the most important greenhouse gas (GHG). Most of the anthropogenic CO₂ emissions are due to the combustion of fossil fuels in various sectors of the economy—i.e. the energy supply system and the end-use sector. Hence, it is always possible to link GHG emissions to energy production and utilisation.

On the basis of official results of the 1991 national census, which gave the population of Nigeria as 88.5 million people, and the assumption of an average growth rate of 2.85% per annum, it is estimated that there were 108 million inhabitants in Nigeria in 1998. Nigeria has an abundant supply of natural energy-reserves, amongst which are oil, natural gas, coal, bitumen, and renewable energy resources. Over the years, however, the energy sector of Nigeria has been primarily dominated by petroleum, and this has been the prime mover of economic and social development. As a result of increasing earnings from oil exports, domestic energy consumption has been rising roughly in unison with developments in the economic and social frontiers within the last two decades.

As at the end of 1998, Nigeria’s proven oil-reserves were 21,000 million barrels and efforts are underway to increase this to 25,000 million barrels. Despite the proven reserves and a large quantity of probable reserves, oil production has been below 650 million barrels per annum, on average, between 1980 and 1995. Crude-oil production, export and consumption trends for this period are shown in Fig. 2. From this, we observe that domestic requirements for crude oil have been steadly increasing for most of the period, at an average of 7% per annum. There are four petroleum refineries in Nigeria, with a total design capacity for 450 thousand barrels of oil per day. However, for the last 2 years, the refineries have been faced with various production problems. This has led to an acute shortage of petroleum products all over the country and consequently massive importations of fuel in a bid to support domestic demand, especially in the transport sector.

Nigerian gas reserves were estimated at about 3.36×10⁶ million m³ in 1998. The gas-to-oil ratio of the Nigerian crude is quite high, with the resultant effect that about one half of the estimated gas reserves appears as associated gas, i.e. gas produced along with crude oil; the other half exists as non-associated gas in isolated gas-deposits.

Natural gas consumption in Nigeria is low compared with the available resource base. In 1995, for example, of the 32,000 million m³ of gas produced, only 15% was consumed domestically; the remaining 85%, i.e. mainly associated gas, was flared. A good proportion of the associated gas lifted with crude oil is flared because there are no adequate infrastructures to supply the gas to possible end-users, if collected, and also because of the fact that the recovery cost of associated gas is much higher than for non-associated gas. Consequently, since the available non-associated gas can meet domestic demand for many years to come, there is no motivation to recover the associated gas that is currently being flared, and this has been the trend for some time. As at 1990, Nigeria contributed 27% of the total gas flared globally, i.e. the largest percentage in the world. However, in recent years, there has been some increase in the demand for natural gas. For example, about 3.4% of the total commercial energy consumption was consumed as gas in 1970, reaching about 38.6% in 1990. This is due to the increase in demand for gas in the fertiliser and petrochemical industries, as well as an increase in the number of gas-fired power plants.

Although measures are being taken to recover some of the associated gases currently being flared, gas-flaring in the oil industry will continue to be a major source of environmental pollution and an important component of Nigeria’s total CO₂ emissions, for some time to come. Some of the efforts being made to find economic uses for the flared gas include the West African Gas Pipeline Project, under which Nigeria is to supply gas to some West African countries (notably Ghana, Togo and Benin), the Oso Natural-Gas Liquids project, and the Escravos Gas-Flare Reduction Plant.

As with oil and gas, Nigeria has abundant coal resources. Data from the Nigerian Coal Corporation revealed that there is an estimated reserve of 2700 million tonnes. This consists of proven reserves of about 640 million tonnes and probable reserves of 2060 million tonnes. Of the total amount of coal resources, less than about 50 million tonnes are coking coal. Table 1 shows the trend and quantity of coal extracted between 1980 and 1992.

Because of the out-dated mining practices and ageing equipment, coal production at the mines is low, with a correspondingly high production cost. Before 1990, the Nigerian Railway Corporation and the National Electric Power Authority were the major consumers of coal in the country. However, following the replacement of coal locomotives with diesel engines and the retirement of the last coal-fired power plant, there has been a decline in the demand for coal consumption in Nigeria. Presently the major consumer of coal is the cement factory at Nkalagu, which accounts for over 90% of total domestic demand from 1985 until the present. Small quantities are also used for cooking in households close to the mines and by small-scale industries.

Electricity generation in Nigeria is primarily by three hydro-power plants and six gas-/oil-fired thermal plants. Nigeria’s hydro-potential is estimated to be of the order of 8000 MW, of which only 1900 MW had been exploited by 1990. Hydro-power generation has been an important part of the electricity-generation mix of the national grid, with its share ranging between 22% in 1983 to 46% in 1995. Electricity consumption rose from 4.6 TWh in 1980 to about twice this value in 1995. Despite this steady rise, at an average of 4.6% per annum, electricity consumption per capita in Nigeria as of 1994 was a mere 0.136 MWh annually, one of the lowest in Africa. These estimates do not include autogeneration, however.

As a result of incessant power outages, autogeneration is an important feature of power generation in Nigeria, especially in the industrial and residential sectors. Although data on private power generators are scarce, it is believed that the installed capacity of these generators was about 1760 MW in 1990, amounting to about 30% of grid capacity of the nation’s only electricity utility, the National Electric Power Authority (NEPA). Major factories in the industrial city of Lagos now run on diesel generating-sets for their production process, and only use the NEPA supply when off-production. If autogeneration is taken into consideration therefore, Fig. 4 does not present the whole picture of electricity production and consumption in the country.

Wood is probably the most important non-commercial fuel in Nigeria. It is used mainly for cooking in rural and some urban households. It is also the last resort during periods of acute scarcity of kerosene and gas, which are not unusual in urban centres. Wood in some cases is converted into charcoal and used either as a supplement to fuelwood or mainly by itself for cooking.

Fuelwood and charcoal are also used in small industries, such as food-processing factories, bakeries, brick manufacturing and beer brewing. The consumption data for fuelwood and charcoal are subject to controversy because data sources are limited: it is, however, estimated that consumption of the products would account for about two-thirds of the total final energy-consumption in the country. Annual fuelwood consumption has been estimated to be 80 million m³, or 43.4 million tonnes.

In several countries, the energy supply has been discussed in terms of feasible options for the medium and long terms. Hydroelectric power-stations cannot be considered in some countries because there are insufficient river falls or flows to warrant either being financially attractive or providing an economic return on the investment. Also nuclear generation is being rejected by many environmentalists.

Natural gas has been claimed to be the best solution for power generation because of its low pollutant emissions and its relatively large availability for the next few decades. Its use in industry as a substitute for fuel oil in heating processes and for electricity generation in cogeneration systems is becoming increasingly popular.

Solid wastes burning in municipal power-stations is being proposed as a possible solution, not only for solving the problem of disposing of the wastes but also for producing electricity for export to the grid: this paper discusses this question in the Brazilian context and proposes some solutions to be considered in a real case-study according to an international experience review.

The economic attractiveness of an enterprise is dependent on parameters such as the interest rate considered and the loan period; however, when different and/or competing technologies are under analysis, the differential cost should be evaluated to determine the economic gap between them. Fig. 1 presents an illustration of competing technologies with their corresponding investment costs; capacity is the available quantity of each technology at a certain cost.

Technology C is marginal because it has the highest cost among the possibilities considered; the least cost technology A will be recommended until its capacity Q_a is fully reached. When it occurs, technology B is the next possibility to be explored until its capacity Q_b is reached; differential costs A and B are the capacities Q_a and (Q_b?Q_a) multiplied by the difference between the costs of marginal technology and the corresponding one.

Nowadays, the solid-waste power-stations have higher costs if compared with the natural gas or the hydroelectric power-stations: the same steam thermal-cycle has an investment cost of 850–1000 US$/kW if burning fuel oil, 1300–1800 US$/kW for coal and around 4500 US$/kW for urban solid-wastes. Hence, at present, the last one will only be considered in the absence of the others or if there are sufficient incentives for its use. Also the environmental impacts of burning municipal solid-wastes are not comparable with the adverse impacts of burning natural gas. However, in the long term, because of the urgency of introducing new power-stations to satisfy the continuously growing electricity demand, it is desirable to analyse the feasibility of burning municipal (and some other) wastes, which are largely generated by human activities. The useful disposal of such wastes in several countries is a problem that demands urgent decisions.

More electricity is required in the developed and developing countries because of its convenience and the intensive use of electronic systems in the industrial, residential and commercial sectors. However, only part of the energy of the input fuel is converted into useful energy (e.g. as electricity or mechanical work) and the remaining part is disposed of as heat and losses.

Cogeneration is defined as the combined production of more than one form of energy as a result of the combustion of a fuel. The major difference between a conventional power-station and a cogeneration system is that the latter is designed to produce the electricity as well as to recover the exhaust heat for district heating or for a refrigeration cycle, whereas, in the former, the aim is only to produce electricity. In a steam cycle, for example, a cogeneration system would have a back-pressure steam-turbine and a power-station would have a condensing one: Fig. 2 illustrates the differences.

Latin America has only recently become concerned with the economic exploitation of refuse. In the United States, waste recycling and the associated energy generation are well established. Pollutant emissions as a result of solid-waste power generation have been studied extensively. Typical USA municipal solid-waste plant characteristics are: combustion capacity from 13 to 4000 ton/day (mean: 786 ton/day); electricity produced from 0.5 to 935 MW (mean: 176 MW); steam production from 1.25 to 1159 ton/h (mean: 115 ton/h).

In Europe, there is an agreement that MSW should be burned as a desirable option — see Fig. 3.

In Japan, it is desirable to burn MSW because of the lack of space; estimates are that 82% of the 50×10⁶ tons/year of MSW generated are related to the energy production; 253×10⁶ tons/year of industrial wastes are also discarded in which 39% are organic wastes and 6% are some different wastes of considerable heating value (as oils, plastics, etc.). District-heating systems receive the hot water produced by the MSW incineration and the steam also obtained is used to produce electricity in multiple units; 108 incineration units in 1991 were responsible for generating 320 MW(e).

The population of more than one billion inhabitants of China generates 200×10⁶ tons/year, that is equivalent to 0.55 kg/person/day; from this quantity, only a little part is incinerated. In India more than 90% of the MSW is disposed of in embankments.

Brazil’s most important pertinent development was undertaken by São Paulo State Energetic Company — CESP, in 1989 to construct two MSW power plants for São Paulo — Brazil’s biggest city: 12000 tons/day of wastes were intended to be burned to generate electricity to supply about 400 000 residences. São Paulo City burned at that time no more than 500 tons/day.

Electricity generated by burning municipal solid-wastes incurs an investment cost higher than those of other competing technologies, although it can be an important contribution to environmental and social improvements.

Guaratinguetá Region (GR), a group of eight cities in the Southeast region of São Paulo State, Brazil, consists of the following cities: Guaratinguetá, Cachoeira Paulista, Aparecida, Cunha, Lorena, Piquete, Roseira and Potim. It is located in the most industrialised region of São Paulo State: important chemical industries exist in Guaratinguetá and Piquete; some heavy industries (paper and allied products; fabricated metals industries) in Cruzeiro, and some new food industries are now being built in Lorena. In 1993, German researchers analysed the structure of wastes generated in the region: they concluded that 66500 tons/year were produced in the GR as a result of commercial, residential and public contributions, including the wastes from hospitals.

The MSW heating value and moisture content for the Guaratinguetá Region ranged from 4000 to 5000 kJ/kg and from 40 to 50%, respectively.

In the same report it was also concluded prematurely that incineration is not appropriate for the Guaratinguetá Region, because of the composition and low heating value of the local MSW. Nevertheless, it is important to investigate more thoroughly the feasibility of producing electricity and recovering heat in a cogeneration system that can burn, total or partially, the solid wastes generated locally.

An interesting possibility is the use of a combined system to generate energy; the incineration equipment, that burns the MSW, produces steam and is connected to a gas-turbine system burning a gaseous fuel such as natural gas. International experience suggests incineration (associated with the conventional steam cycle) and gasification (in a gas or combined cycle) as two possibilities for converting the solid wastes energy content into useful energy.

The objective of this report is to present technical and economic feasibility analyses for two cogeneration schemes to be located in an industrial district at Guaratinguetá City.

Energy conservation is an important part of any national energy strategy. Energy conservation in a country like Palestine without energy resources of its own, is even more important. Energy conservation in buildings, by using proper insulation material, will reduce imported energy and reduce fossil fuel combustion and its polluting products.

Building insulation will reduce the running cost of space heating at the expense of an increase in the initial investment by the added insulation material. The high cost of fuel and electricity in Palestine, as shown in Table 1, is expected to favor the economics of buildings insulation. All monetary values mentioned in the paper are in US dollars.

Energy consumption for space heating forms more than 60% of the household energy consumption in Palestine. Houses are heated using kerosene or gas stoves, or central heating systems. Boilers in the central heating systems burn diesel, light fuel oil, or LPG.

The Palestinian territories extend from coastal Gaza Strip at the Mediterranean Sea to the mountain ranges in the West Bank with elevations reaching 1000 m above sea level. In contrast the Jordan valley, in the east, drops to 300 m below sea level.

The Palestinian territories can be divided into three climatic zones for space heating purposes. Table 2 shows the characteristics of each climate zone.

Almost no space heating is required in the Jordan valley, while little heating is needed in the coastal region. The heating season in the mountain region of West Bank, with 1354 degree days, extends from October to April.

Building materials employed in newly constructed houses are stones, concrete, bricks and the required iron bars for reinforcement. Wall structures, vary from one region to another. In general, stone and concrete walls are used in the West Bank mountain regions, while brick is more commonely used in the coastal region and the Jordan valley.

Table 3 lists some typical wall structures for buildings in Palestine and their thermal characteristics, including the conductance U-value, and thermal resistance.

Wall I, which is the most common structure in the West Bank, consists of a 7 cm-thick stone layer followed by 20 cm-thick concrete layer and a 3 cm internal plaster layer. In order to improve the thermal resistance of the structure and reduce heating loads, some new houses employ wall IV or wall V. Wall III is the most common type in the Gaza Strip and Jordan Valley. Wall II is used in many rural areas in the West Bank to reduce the cost of construction by eliminating the outer stone layer.

Heat losses from a building at steady-state are computed as losses through walls and ceiling, plus ventilation and air infiltration.

Air ventilation and infiltration are not affected by wall insulation, while heat losses through walls decrease with increasing resistance or decreasing conductance. Hence, only wall losses will be considered in the insulation thickness optimization analysis that will follow.

The annual energy requirement for space heating, E_h, can be determined using the degree days, DD, the wall conductance, U, and the efficiency of the heating system, ? as given by the following equation

(1)

E_h=86400UDD/?

The life cycle cost analysis employed in this paper computes the heating cost over the lifetime of the building.

The total heating cost over a lifetime of N years is evaluated in present value dollars using the present worth factor PWF. The PWF, which depends upon the inflation rate, g, and the interest rate, I, is adjusted for inflation as shown below.

As insulation thickness increases the heating load decreases, and hence the cost of fuel and total cost of heating. On the other hand, the insulation cost increases as its thickness increases. The total cost of fuel and insulation material will show a minimum when plotted versus the insulation thickness as shown in Fig. 1 for wall I. The insulation thickness at the minimum total cost is taken as the optimum insulation thickness.

The optimum insulation thicknesses for the various wall types specified in Table 3 were computed using Eq. (13) and the values of the parameters in Table 4. Table 5 presents the results for the various walls and for two types of insulation, rock wool and polystyrene.

In order to account for different values of the interest and inflation rates, the optimum insulation thickness for wall I is plotted versus the PWF, as shown in Fig. 2, for the two types of insulation. For example, an interest rate of 8% and inflation rate of 10% the PWF is 9.05 and the optimum insulation thickness is 0.067 m for polystyrene.

Life cycle savings per meter square of wall area are computed as the difference between the cost of heating the uninsulated building and the cost of insulating the building. Table 6 presents the total saving over 10 years for insulated walls in the West Bank. Such savings are proportional to the fuel cost and to the PWF; any increase in the fuel cost will increase the savings. The payback period is calculated as the insulation cost divided by the annual savings per square meter of wall, also given in Table 6.

The effect of degree days and wall thermal resistance on the optimum insulation thickness are illustrated in Fig. 3. Colder climates having higher degree days require larger layers of insulation, as shown in the figure. At a given number of degree days, buildings having higher thermal resistance require less insulation.

Comparing our results with a similar analysis for Jordan at a similar number of degree days shows that in our case the optimum insulation thickness is much larger than that in Jordan. This results from the much higher fuel prices in Palestine, which are about twice as high as in Jordan, and from the lower insulation cost in Palestine, which is about one third that of Jordan.

The optimum insulation thickness which minimizes the life cycle cost was computed for different wall structures. Savings over a lifetime of 10 years were computed for different wall structures and number of degree days. Even for climates with as few as 500 degree days, savings will be realized by adding insulation. The savings in cold climates as in mountain ranges of the West Bank can be as much as 22 $/m² of wall area over lifetime of 10 years.

Payback periods are 1–1.7 years for rock wool insulation, and 1.3–2.3 years for polystyrene insulation, depending on the type of wall.

Generalized charts for obtaining the optimum insulation thickness were prepared as a function of wall thermal resistance and number of degree days.

Insulation of buildings in Palestine is shown to be economically feasible and should be implemented, as it will save money and reduce imported energy.

Bioenergy is expected to become one of the key energy-resources for global sustainable development. Biomass maintained adequately is renewable and free from net CO₂ emissions. However, the annual amount of available bioenergy cannot be infinite, since the land area available for biomass production is limited and a certain amount of biomass production must be reserved for the required food and materials. Bioenergy production will be limited more strongly when the growths of the population and economy in the world cause the growth of biomass demand for food and materials in the future. On the other hand, bioenergy can be produced not only from bioenergy plantations, which occupy land but also from biomass residues (such as straw, animal dung and wood scrap) which do not occupy land directly. These biomass residues are discharged from various processes in the biomass flow from harvest to consumption.

The purpose of this study is to evaluate the global bioenergy potential comprehensively, considering energy potentials of both bioenergy plantations and biomass residues. For this purpose, we have developed a global land-use and energy model (GLUE) including the mechanisms of land-use competition and overall biomass flow.

The details of the model are explained in Ref. [1].

1. Modeling technique: The model is described by the SD (system dynamics) technique, which is adequate to describe the stock and flow of biomass explicitly.

2. Simulation period: The time scope of GLUE is 125 years from 1975 to 2100, with 1-year time steps.

3. Regions in the model: The world is divided into two regions: a developed region and a developing region. The developed region comprises OECD countries (excluding Turkey, Mexico, and Korea), former USSR, Eastern Europe, Israel, and South Africa; the developing region includes all the other countries.

4. Structure of the model The model (GLUE) consists of a land-use sub-model and an energy sub-model.

5. Land-use sub-model: This, modified from the sub-model reported in Ref. [8], considers a food sector and a wood sector [1, 2]. The sub-model includes land use competition among various uses of biomass applications such as paper, timber, food, feed, and energy. The sub-model covers a wide range of land uses and biomass flow including food chains from feed to meat, paper recycling, and discharge of biomass residues.

6. Energy sub-model: This was developed following the basic structure of the Edmonds–Reilly model. The sub-model includes a module chemical flow, in order to evaluate the energy potential of chemical-products scrap. The latter is considered to have a worthwhile energy potential in municipal wastes.

7. Relationship between these two sub-models: The supply of modern bioenergy calculated in the land-use sub-model is substituted for demand of coal in the energy sub-model.

8. Non-commercial energy: The energy sub-model handles only commercial energy. The land use sub-model handles not only commercial energy including modern bioenergy, but also non-commercial energy including traditional bioenergy.

We determined a reference case for the GLUE based on data of the FAO (Food and Agriculture Organization of the United Nations) and base scenarios of the World Bank, IPCC, and Ref. [15]. The area of arable land in the developing region will double during the period from 1990 to 2100, which is based on the RIGES (Renewable-Intensive Global Energy Scenario) in Ref. [16]. The data set of the reference case is shown in Table 1, and discharge rates of biomass residues and practical energy-usable rates are shown in Table 2.

In this study, we use a low heat value (LHV) for the biomass. The weight of the biomass means the air-dry biomass weight (20% water content) unless specified otherwise; heat value of biomass is 15 GJ/air-dry ton of biomass; and carbon (C) content of biomass is 0.50 ton per ‘bone’-dry ton of biomass.

We consider the simulation results of the reference case-study as calculated via GLUE.

1. Biomass balance-table: We arranged the simulation results in the form of a biomass-balance table (BBT). The BBT is a unified framework of various biomass statistics showing the biomass flows quantitatively and analyzing bioenergy potential.

Because of the limitations of space, we show only two BBTs (namely for wood biomass and food biomass) for the developing region in 2100. In the BBT, horizontal items mean kinds of biomass. Vertical items mean biomass-utilization processes, and the two vertical items from the bottom mean ‘ultimate bioenergy potential’ and ‘practical bioenergy potential’ respectively Positive values mean the production or import of biomass; negative values mean the consumption or export of biomass; and hatched values are subtotals in the Table.

As shown in Table 3 and Table 4, the developing region in A.D. 2100 will harvest 407 EJ/yr of primary biomass (148 EJ/yr of primary wood and 259 EJ/yr of primary food) and consume 133 EJ/yr of secondary biomass (37 EJ/yr of secondary wood, 44 EJ/yr of secondary food, and 52 EJ/yr of traditional bioenergy) The amount of total primary biomass supply (407 EJ/yr) exceeds the total primary energy supply (330 EJ/yr) in the world in 1995. In addition, the primary biomass demand for wood, food, and the total amount will increase five times, twice, and twice respectively between 1990 and 2100 in the developing region, because both the biomass demand per capita and the population increase.

2. Ultimate bioenergy-potential: We show ‘ultimate bioenergy potential’ in the developed region and the developing region in Fig. 4 and Fig. 5 respectively; the information being summarised in Table 5.

Fig. 4 shows that, in the developed region, the potential of energy crops produced on surplus arable land will be large (100 EJ/yr) in 2100. This is because it is assumed that the food demand will be stable and the productivity of arable land will increase. The potential for energy crops in the world will reach 154 EJ/yr in 2100. The potential is sensitive to parameters concerning food supply and demand. For example, the potential in 2100 will reduce by half if animal food demand per capita in the developing regions is 25% larger than that in the reference case.

On the other hand, there will be a large energy potential for biomass residues in the developing regions. The ultimate energy potential of biomass residues (223 EJ/yr) will account for 80% of all the bioenergy potential (277 EJ/yr) in the developing regions in 2100.

The peak load in Riyadh occurs during the hot season, and in general between noon and 4 pm. The power plant and the transmisson and distribution lines are then under maximum load. Also, the peak load on the utility grid in Riyadh coincides with the peak solar-insolation, which results in peak power-generation in the PVGC system. Thus, the PVGC system provides an excellent means of reducing the peak load imposed on the grid. The PVGC system does not need electrical batteries for the storage of electrical energy because the utility grid acts as the store for the PVGC system. Normally, the size of storage batteries of the PV stand-alone system should be sufficient for more than 3 days supply in Riyadh. Therefore, the cost of the PVGC system is 40% less then the PV stand-alone system, of equivalent power supply as the PVGC system. The 40% decrease in the initial cost is due to the cost of storage batteries (capacity, type and quality).

The power of the PVGC system is 6 kWp under standard conditions, i.e. 1000 W/m², 25°C and 1.5 AM. The PV generator is divided into 6 arrays, each of 1 kWp. The PV arrays are wired to supply dual voltages to the inverter, i.e. positive, zero and negative. The inverter has a built-in automatic controller, which is suitable for different methods of operation and protection of the PV arrays, inverter and the parallel operation with the utility grid. Several parameters are recorded by the data-logger in order to obtain a performance analysis of the PVGC system. These parameters include solar radiation incident on horizontal and tilted planes, ambient and PV module temperatures, voltages and currents for the positive and negative arrays, inverter input and output voltage, current and power of the inverter, and current supplied to the load and the utility grid, i.e. value and direction of flow of the current between PV generator, utility grid and load. To simulate various possible operating conditions, the load current has been varied.

During the period of the peak load imposed on the utility grid, all the electrical equipment, i.e. power generator, transformer, and transmission and distribution lines, is fully loaded, and the rate of energy loss is a maximum in the grid equipment. Therefore producing the electrical power at a location near to the load, i.e. via the PVGC system, will reduce the percentage loading upon the equipment and hence the amount of energy lost in the equipment of the utility grid is less.

The PVGC system does not need electricity-storage batteries, because the electrical power generated by the PV array is, automatically, either consumed by a local load or fed to the utility grid or shared by the load and the grid.

The results obtained from the simulation of the behaviour of the PV array,under various conditions, have been studied and compared with experimental data. Many software packages are available for the simulations of PV array behaviour but the predictions from various packages differ.

Also the degree of agreement between the predictions from the simulation and the experimental data depend on the accuracy of the values of the input parameters for the software, e.g. for the dust accumulation on the collectors surfaces: the latter is a function of the tilt angle, the wind speed and direction, and the frequency of cleaning of the PV arrays. Others input parameters to the simulation software have important effects on the output results of the simulation of PV array systems behaviour, namely solar radiation (and hence the tilt of the array, and the solar spectrum variation – hourly, daily and monthly), ambient temperature, operating temperature of the PV array and its tilt angle (fixed or tracked on a single axis). The values of the measured data for the PV array depend on the accuracy of the equipment used, i.e. sensors and transducers, which are used to measure various values of voltage, current, solar radiation and temperature. So the accuracy of the measuring equipment used should be checked periodically in order to reduce the errors. The accumulation of dust depends on the tilt angle and the climatic condition of the location of the PV array. Dust accumulation can reduce the output power from the array by 5% to more than 25% for low and high accumulations of dust, respectively. The assumed 5% loss of power due to dust accumulation on the PV array surface can be reduced to less than 1% at the Solar Village by frequent cleaning of the PV array. The reference conditions for the comparison of various cases is the case numbered 1 in Table 1, i.e. a fixed tilt angle of 25° (namely the latitude of the Solar Village), and 5% loss of power due to dust accumulation on the PV arrays. The other parameters are kept constant, i.e. albedo =20%, mismatch losses between various arrays =3% and wiring losses of the PVGC system =2%.

From Table 1, the following conclusions can be drawn relative to the reference conditions (i.e. case 1):

• PV arrays, with a fixed tilt angle, which is equal to site latitude (case 1), give a higher output per year when compared with arrays having other values of the fixed tilt-angle, either smaller or larger in magnitude.

• PV arrays with a single-axis tracker along with fixed tilt angle (case 5) give about 22.2% more output than achieved with the reference systems (case 1).

• PV arrays with single tracker along with monthly best tilt angle (case 8), give about 25.8% more output power than achieved with the reference system (case 1).

• When the PV array is left without cleaning for a long time, the dust accumulation can reduce the output of the reference system (case 1) by 21.05%. Therefore, because the cost of the PV system is relatively high as compared with other sources of power, it is advisable to clean the PV array each time it is required. Cleaning can be accomplished quickly and is not a tedious task.

• Normally the single-axis tracker system (case gives a high output-power, but with 25% loss of power due to dust accumulation, i.e. case number 16. This will result in less output power than the reference condition (case 1) by 0.9%. Therefore the cleaning of the PV array, whenever required, is an economic and efficient way to get the maximum possible outputs from the PV system.

This was studied under various conditions, such as with respect to variation of the tilt angle, load power and the cleaning of the PV array surface. The tilt angle was varied according to the season. The power and the duration of operation of the load were also varied. The weekly profile of the load is ON for 5 days (i.e. during the day) and is OFF for 2 days.

Using Eq. (1), several cases are considered as follows:

• When the load is OFF, therefore, P_load=0, and P_inv=?Pgrid: this means that the output power of the inverter is given to by the grid.

• When the load is ON but P_inv=0, i.e. very low incident solar radiation is received on the surface of the PV array, then P_load=P_grid, which means that the load is supplied by the grid.

• When the load is ON but P_inv is less than P_load, e.g. P_load=4 kW and P_inv=2 kW, after substitution in Eq. (1), it can be seen that P_grid=2 kW, which means that load power is supplied from the inverter and the grid. Fig. 2 shows the conditions which are discussed above: as an example at 7:00 am, the load is supplied only from the grid, while after 7:30 am, the load is supplied from the PV generator, which is also supplying the energy to the grid. Also, it is clear from Fig. 2 that starting from noon, the PVGC is supplying current to satisfy the load and is feeding the grid. Therefore, the PVGC system is reducing the peak load imposed on the grid. Fig. 3 shows the performance of the PVGC system. It is clear from the figure that, during the month of September 1996, all the energy generated by the PV-inverter is supplied to the grid, which means that the load is OFF. But in September 1997, the load was increased, so that it consumed all the energy generated by the PV-inverter: therefore, P_grid=0, which means that the grid neither supplies nor receives energy. When P_grid=0, the inverter of the PVGC system must be internally protected against the case of islanding. The performance of the inverter was excellent during every islanding test.

Bioenergy is expected to become one of the key energy resources in order to reduce the excessive global warming and the rate of exhaustion of fossil-fuel resources. Biomass is renewable and free from net CO₂ emissions as long as it is maintained sustainably. But the land area available for biomass production is limited and a certain amount of biomass must be reserved for material production (e.g. wood and food). Therefore bioenergy has not yet been competitive with exhaustible energy sources such as oil and coal, and there is no clear prospect for its supply in the future.

In the 21st century, biomass which is used for food, material, and energy will be more indispensable. Hence, it is required to know the biomass flow accurately for sustainable development.

In this study, we propose a “Biomass-Balance Table” which shows harvest, conversion, and consumption of biomass systematically. The scheme of the Biomass-Balance Table is similar to that of the energy-balance table.

Biomass resources (such as raw-wood materials, cereals, pasture harvested from the land, and seafood produced in the sea, rivers and lakes) are converted into biomass products (such as fuelwood, charcoal, paper, timber and food) by the biomass-processing industries (such as for paper and pulp, food and livestock industry). They are consumed by human beings. Some of them are recycled. In these processes, biomass residues are discharged and can be used for energy production.

This is divided into plantation bioenergy produced on the land and bioenergy residues discharged during the processes of harvesting, conversion and consumption for food, timber and paper.

A “Biomass-Balance Table” scheme is analogous to that of an energy-balance table: it shows the harvesting, conversion and consumption of biomass systematically.

In this study, we made tables for the world, a developed region, a developing region, 10 regions in the world (corresponding to New Earth 21 model) in 1990, expressed in Joules, but due to limitations of space, tables for only the world and Japan are shown.

Biomass processes as a result of (harvesting, conversion and consumption) are expressed in the columns, and biomass forms (primary, secondary and scrapped biomass) are expressed in the row. “Bioenergy consumption” in the column means the biomass used for energy production, i.e. the input to energy-conversion equipment. A positive value indicates harvesting, production or an import, whereas a negative value means an input, a consumption or an export; – indicates that there is no process.

For example, “industrial roundwood” in the row in Table 2A has an annual indigenous production of 0.39 EJ plus 0.5 EJ is imported and nothing exported. There is a supply of roundwood equivalent to 0.89 EJ in Japan. For woodpulp production, 0.39 EJ of roundwood is used and for timber production 0.50 EJ is used. Also 0.39 EJ of industrial roundwood used for woodpulp production becomes 0.17 EJ of woodpulp, 0.18 EJ of black liquor, and the rest, i.e. 0.04 EJ, is counted as a loss. For “Black liquor”, all the ultimate bioenergy potential of 0.18 EJ is used for energy production as shown in Table 2A.

Most data are derived from FAO (Food and Agriculture Organization) statistics.

1. Heating values: 15 GJ/t for general wood (of 20% moisture content), 28 GJ/t for charcoal, 12.5 GJ/t for black liquor are used.

2. Charcoal, fuelwood and other fiber: 1 J-charcoal is produced from 2.5 J-fuelwood. We assumed a conversion rate from other fiber crops (such as straw, bagasse, and bamboo) into other fiber pulp as 40%.

3. Industrial roundwood residue and fuelwood residue: It is assumed that 61% of forest biomass above ground is harvested and the rest is left in the forest. When 1 unit of industrial roundwood is produced, 0.64 ( ) units of industrial roundwood residues are discharged. On the other hand, when fuelwood is harvested, small branches are also harvested, so the discharge rate of fuelwood residues are set at 19.5%, i.e. half that of industrial roundwood residue. Therefore in producing 1 unit of fuelwood, 0.24 ( ) units of fuelwood residues are discharged.

4. Paper and pulp production: According to the data from the paper and pulp industries in Japan, process losses except that for producing black liquor are very little [a loss at the pulp production process is 1.4% (0.36 Mt) of input value, a loss at paper production process is 1.6% (0.46 Mt) of input value]. So we did not assume any loss for the pulp and paper production process.

5. Paper recycle and scrap: Some scrapped paper is recycled to make pulp. We assumed that the amount of the pulp made from scrapped paper recycling is set to cover the pulp shortage, which occurs when only woodpulp and other fiber pulp are used for paper production.

6. Timber recycling and scrap: Some small wood chips (6.57 Mt) and scrapped wood which are discharged during construction work (7.5 Mt) are recycled (the former being 3.09 Mt and the latter 2.3 Mt) in Japan in 1990. We assumed all of the above recycled timber is used as fuel. There is no global data available about this, so we did not put any figure into tables for regions other than Japan.

1. Heat value: Each heating value for food biomass is calculated on the basis of the data in “supply per capita (cal/cap)”*“population (cap)”/“food supply (ton)” which are shown in FAO Agrostat PC.

2. Energy crops: In Brazil 0.24 EJ of liquid fuel was produced from sugarcane in 1990.

3. Crop residue, sugarcane residue: 1.3 t (i.e. 15.6 GJ) crop residues are discharged from 1 t crop. From 1 t sugar cane, 0.15 t (0% moisture) (i.e. 2.30 GJ) of sugarcane residues are discharged.

4. Bagasse: 0.283 t (50% moisture) (i.e. 2.13 GJ) of bagasse is discharged from 1 t sugar cane. In Brazil, 0.48 EJ of bagasse was used for energy in 1990. In Okinawa, Japan, 0.25 Mt (2.6 PJ) bagasse was used in the same year.

5. Animal food production: Feed put into livestock is mainly converted into meat (such as dressed meat, milk, eggs and animal fat), dung and energy for respiration. For the conversion rate of the livestock digestion process, we used the livestock data for Japan in 1990.

6. Kitchen refuse: Food energy supply per capita was 2633 kcal/day, caloric intake was 2061 kcal/day, and the rate of loss was 21.8% in Japan in 1990. Therefore, we assume 21.8% of food supply is scrapped as kitchen refuse.

7. Human growth, respiration and human faeces: Using the characteristics of the digestion process of pigs, we assumed that 30% of caloric intake becomes human faeces. This is available for energy use. but at present it hardly leads to any net useful energy. So we did not assume any useful energy production here.