RESULTADOS DE ENCUESTA APLICADA A ESTUDIANTES
ASÍ HAN EVOLUCIONADO LOS VAGONES DEL FUNICULAR
U SERS
The industrial sector is extremely diverse and includes a wide range of activities. This sector is particularly energy intensive, as it requires energy to extract natural resources, convert them into raw materials, and manufacture finished products. As will be seen, the energy intensive industry represents a large share of energy use in developing countries such as Kenya.
Industrial Energy Performance Indicators
Indicator E/tonne for each sub-sector (steel, paper, cement, Etc.)
Definition Physical energy intensity: energy per physical unit of output generally measured in tonne Units kWh/tonne
The service sector includes a wide range of different activities, from subsectors that require a great deal of electricity per unit of square meter (retail trade), those that use large quantities of fuel for water heating and cooking (restaurant, hotel), and those that by their nature consume little energy (warehousing, parking).
Hence, if available data is broken out by subsector, then a more detailed treatment is possible by analyzing energy use per subsector or building type within the service sector, such as retail, office, hotel, education, health care, and other.
Commercial & Institutional Energy Performance Indicators Indicator E/m2 per building type per end use
Definition energy per square meter for each building type and for each end use Units kWh/m2
The industrial sector can be broadly defined as consisting of energy-intensive industries (e.g., iron and steel, chemicals, petroleum refining, cement, aluminium, and pulp and paper) and light industries (e.g., food processing, textiles, wood products, printing and publishing, metal processing).
S
UB-
SECTORSI
NVOLVED IN THEB
ENCHMARKA
NALYSISThe table below shows the sub-sectors involved in the energy performance benchmarking process with the number of facilities analyzed in each.
Industry/Sector Sub Sectors No. Of Facilities
Analyzed
Hotels & Lodges Hotels 6 Health And Hospitals Hospitals 7
Learning Institutions Universities And Colleges 6
Table 3.0: Sub-Sectors Involved In The Energy Performance Benchmarking Process
R
EMOVINGO
UTLIERSThe underlying assumption of benchmarks is that energy use will increase approximately linearly with a proportional increase in any one of the comparison metrics (facility area, equipment installation and usage etc.
After the initial benchmarks were calculated, some of the benchmark ranges for many sub-sectors were larger than expected, which indicated that some facilities within a sub-sector may have used several thousand times more energy per metric than others. These phenomena could be explained by a number of possibilities:
• There are actually facilities within the same sub-sector that are many times more efficient than others.
• The metric used to calculate the benchmark is a poor indicator facility energy use.
• The metric data is inaccurate for some facilities.
• The sub-sectors include facilities with significant variations in manufacturing processes with varying degrees of energy intensity; despite efforts to group facilities that have similar operations requiring similar amounts of energy, facilities with unique operations or uses of energy likely exist within subsectors.
To standardize the benchmark values, a log transformation was performed on all benchmark data to make sure that the benchmarks within a sub-sector were normally distributed. The standardized values were calculated, and any benchmark that fell outside of the range of ±2 standard deviations from the average was removed from the analysis. Removing outliers in this way reduced the frequency of questionable benchmark data appearing within the analysis. Removing outliers also reduced suspicious data points that negatively influence the expected relationship between energy use and the metric.
To determine if the benchmarks are reliable and a good measure of expected energy use, the study compared and contrasted the rough linear relationship between energy use and the metrics for all subsectors. Benchmarks are expected to be reliable if they meet the following criteria:
• A sub-sector population size of at least five facilities was considered large enough to be representative of facilities throughout the state. Requiring a specific sub-sector population size helps ensure that a variety of facilities would represent each sub-sector. Setting the
requirement at five facilities allowed small sub-sectors to remain in the benchmark study.
• The ratio of the maximum and minimum values of facility benchmarks within the mid-range of the sub-sector should be less than 10. We imposed this criterion in an effort to eliminate benchmarks for sub-sectors that include facilities with wide variations in
manufacturing processes. It seemed reasonable that average performing facilities may exhibit energy intensity that 10 times more for one facility than another. For sub-sectors that were eliminated due to the wide variations in processes, perhaps they should be further subdivided before benchmarking results would prove accurate.
Once the reliability of the sub-sector benchmarks was determined, the benchmarks were ranked by their expected reliability.
The figure below approximates the distribution of expected facility benchmark results. We expect most facilities to have energy benchmarks in the mid-range. We divided the distribution into quartiles as a tool to provide guidance on the likelihood of energy conservation opportunities depending on where on the distribution a specific facility might fall. Q1, shown highlighted, represents the smallest values within a population. Facilities that use the least amount of energy-per-metric will fall into Q1, as they are more efficient than other facilities.
Figure 3.0: Example Of A Normal Distribution With Quartiles Indicated. The First Quartile (Q1) Is Highlighted.
Benchmark values that fall within the range of the lowest-value quartile (Q1) suggest that the facility is of above average efficiency, and can be expected to be more efficient than at least 75% of their sub-sector peers. Additional conservation opportunities probably exist, but they are probably less numerous, more expensive, and harder to accomplish. Low-hanging fruit opportunities are likely to have already been implemented. On the other hand, these facilities may be more motivated and more capable to accomplish further conservation, or have historically faced fewer barriers to implement conservation opportunities.
Benchmark values that fall within the range of the second quartile (Q2) suggest that the facility is slightly above average efficiency, and can be expected to be more efficient than at least 50% of their sub-sector peers. Additional conservation opportunities are more likely to exist than for Q1 facilities, but may be more difficult to identify or implement than for Q3 or Q4 facilities. These facilities are likely doing well with implementing low-hanging fruit conservation opportunities, but may face barriers to implementing capital-intensive conservation projects.
Benchmark values that fall within the range of the third quartile (Q3) suggest that the facility is slightly below average efficiency, and can be expected to be more efficient than at least 25% of their sub-sector peers. Additional conservation opportunities are likely to exist and may be easier to identify or implement.
These facilities are slightly behind their more-efficient peers. It may be that they are knowledgeable of conservation, but that resources have not been devoted to implementing many conservation opportunities.
Benchmark values that fall within the range of the highest-value quartile (Q4) suggest that the facility is well below average efficiency, and can be expected to be among the least efficient 25% of their sub-sector peers. Additional conservation opportunities are very likely to exist and may be among the easiest to identify or implement. It is likely that these facilities have little knowledge of conservation and the many opportunities that are available to them. Low-hanging fruit opportunities are very likely to exist, as well as more capital intensive conservation opportunities. Q4 facilities may face significant barriers to implementing conservation.
Sub-sectors with reliable benchmarks are shown in the table below in each benchmark category. The energy use per metric is given for each quartile, as explained above. Unreliable sub-sector benchmarks and quartile ranges were omitted from the report due to uncertainty in the quality of data, small sub-sector population sizes, a lack of linearity between energy and the benchmark metric, and/or unexpected anomalies in benchmark ranges.
Industry/
Table 3.1: Sub-Sector Kwh Benchmark Quartile Ranges.
The absolute magnitude of the benchmarks seen in the table below also provide a sense of the energy intensity of a sub-sector. The amount of spread in the benchmarks is also significant. Many sub-sectors have a Q4 benchmark that is about two times larger than the Q1 benchmark, this suggests that either this sub-sector has more variation in the operations, the metric is less reliable, or that this sub-sector has greater variation in the use of energy efficient technologies and procedures – some of these facilities may have very large opportunities.
3.1.1. A
NALYSIS FORS
ELECTEDI
NDUSTRIESThe table below shows energy intensities were computed for selected sectors where indicative data was available. Notably, much data regarding vaious fuel types is missing. Therefore, energy intensities shown herewith leave a lot in terms of representation of industry standards.
Industry Total Production (tons)
Electrical
(kWh) Fuel Oil
(kWh) IDO
(kWh) Furnace Oil
(kWh) HFO (kWh) LPG (kWh) Biomass
(KWh) Total Energy
(kWh) Sample Size
Energy Intensity (kWh/Ton)
Tea 32,380.70 15,420,120 282,704,320 298,124,440 5 1,841.37
Plastic
Making 15,466 29,699,755 43,259 630,958 43,259 30,417,231.00 8 245.84
Horticulture 275,522.58 14,462,125 1,500,181 3,997,960 313 79,948,960 99,909,538.50 10 36.26
Steel 55,548 4,694,798 4,694,798.00 2 42.26
Cement 13,710,080 109,994,306 493,511 110,487,817.40 4 2.01
Edible Oil 256,718 39,820,948 101,232,849 40,220,077 118,708,572 299,982,446.00 3 389.51
Food and
Beverage 53,031.37 6,725,567 316,269 5,850,595 5,157,360 18,049,791.00 6 56.73
Grain
Milling 159,510 23,386,336 2,173,879 25,560,215.00 4 40.06
Table 3.2: Energy Intensities for selected sectors in Kenya
Cement Industry
A set of four peers in the industry were selected for the study. No distinction was made between companies who import clinker and those who mine it locally. From the table below, a benchmark line shown describes the set annual production compared to the electrical energy consumption for the sector.
Figure 3.1. Electrical Energy Consumption vs. Annual Production in the Cement Industry
Tea Industry
A set of five peers were selected for the study. No distinction was made between different product outputs.
From the table below, a benchmark line shown describes the set annual production compared to the electrical energy consumption for the sector.
0 1000000 2000000 3000000 4000000 5000000 6000000 7000000 8000000
0 10000000 20000000 30000000 40000000 50000000 60000000 70000000
Annual Production
Annual Electricity Consumption kWh
Analysis of Annual Production by Annual Electricity Consumption kWh (R²=0.952)
Active Model
Figure 3.2. Electrical Energy Consumption vs. Annual Production in the Tea Industry
-2000000 0 2000000 4000000 6000000 8000000 10000000 12000000 14000000
30000000 40000000 50000000 60000000 70000000 80000000 90000000
Annual Production
Total Energy (kWh) consumed
Analysis of Annual Production of Tea by Total Energy (kWh) consumed (R²=0.122)
Active Model Conf. interval (Mean 95%) Conf. interval (Obs. 95%)
Electrical 46%
Diesel Oil 18%
Biomass 36%
Figure 3.1.3 : Typical Energy Balance for Tea Industry
Horticulture Industry
Food and Beverage Industry
Electrical Energy 48%
Diesel oil 29%
Fuel Oil 9%
HFO 5% Biomass 9%
Figure 3.1.4 : Typical Energy Balance for Horticulture Industry
Electrical 80%
Diesel (Furnace Oil) 10%
IDO 10%
Biomass
0% HFO
0%
Figure 3.1.5 : Typical Energy Balance for Beverage Industry
Edible Oil Industry
Plastics Industry
Electrical 34%
Fuel Oil HFO 11%
22%
IDO 11%
Biomass 22%
Figure 3.1.6 : Typical Energy Balance for Edible Oil Industry
Electrical 80%
Diesel oil 10%
10% IDO
Figure 3.1.7 : Typical Energy Balance for Plastics Industry
Learning Institutions
The study revealed that most educational institutions primarily relied on electrical power at 65% for lighting and appliances. Diesel oil at 11% was used for backup generators while LPG at 12% was mainly used for cooking and heating.
Hotels & Lodges
The study revealed that hotels and lodges drew almost a quarter of their energy needs from LPG at 23%.
This was mainly used for cooking and heating. Other forms of energy include industrial diesel oil at 15%
and heavy fuel oil at 8%. Diesel was mainly used for backup generators.
Electrical energy 65%
Diesel oil 11%
12% LPG
Biomass 12%
Figure 3.1.8 : Typical Energy Balance for Learning Institutions
Electrical Energy 46%
Diesel Oil 8%
HFO 8%
15% IDO 23% LPG
Figure 3.1.9 : Typical Energy Balance for Hotels and Lodges
3.1.2. E
NERGYI
NTENSITYV
ALUES FORS
ELECTEDI
NDUSTRIESW
ORLDWIDE Although environments and operating procedures are significantly different, processes remain essentially the same throughout industry. Benchmarks from the study can be compared with world benchmarks in similar industriesThe following table shows a summary of World Best Practice Primary Energy Intensity Values for Selected Industrial Sectors. These can be used as benchmarks for local industry.
Unit kWh/t kgce/t Iron and Steel
Blast Furnace – Basic Oxygen Furnace – Thin Slab t steel 4,527.7 555.1 Smelt Reduction – Basic Oxygen Furnace – Thin t steel 5,333.2 656.8 Direct Reduced Iron – Electric Arc Furnace – Thin t steel 5,166.5 635.8 Scrap - Electric Arc Furnace – Thin Slab Casting t steel 1,666.6 205.1 Aluminium
Primary Aluminium t aluminium 48,332.0 5940
Secondary Aluminium t aluminium 2,111.1 259
Cement
Wood Thermo-mechanical Pulp air dried t 6,277.6 770
Recovered Paper Pulp air dried t 1,083.3 133
Bleached Uncoated Fine air dried t 7,527.6 925
Krafliner (unbleached)/Bag Paper air dried t 6,916.5 850
Bleached Coated Fine air dried t 6,916.5 850
Bleached Uncoated Fine air dried t 9,277.5 1139
Newsprint air dried t 8,638.6 1061
Magazine Paper air dried t 6,305.4 775
Board air dried t 6,277.6 772
Recovered Paper Board air dried t 7,944.2 976
Recovered Paper Newsprint air dried t 4,944.3 608
Recovered Paper Tissue air dried t 4,138.8 509
Ammonia
Natural Gas Feedstock Steam Reforming t ammonia 7,777.6 956
Coal Feedstock t ammonia 9,666.4 1188
Ethylene
Ethane Cracking t high value
chemicals 4027.7 496
Naphtha Cracking t high value
chemicals 3611.0
478
Table 3.3: World Best Practice Primary Energy Intensity Values for Selected Industrial Sectors.
The following table shows a summary of typical Energy Intensity Values for Selected Commercial Buildings across the world.
Buildings Type/Use Kwh/sf-yr (Fuels) Kwh/sf-yr (Electricity) Kwh/-sf-yr Total
Schools 46,000 sf 9.6 10.5 20.01676
Colleges 650,000 sf 11.4 16.0 27.37284
Hospitals 500,000 sf 30.4 35.7 72.32995
Public Assembly 14,200 sf 10.5 9.7 20.1926
Restaurants 6,000 sf 41.9 48.4 90.2659
Large Office 90,000 sf 8.6 16.7 25.32134
Small Office 28,000 sf 10.7 16.7 27.37284
Warehouse 27,000 sf 5.6 4.5 10.11095
Refrig. Whouse 18,000 sf 6.8 28.8 35.60814
Lodging 35,800 sf 12.8 16.8 29.62949
Large Retail 32,200 10.5 16.0 26.58155
Small Retail 9,700 sf 10.2 16.4 26.61086
Health Care 24,000 sf 17.8 19.2 34.75823
Table 3.4: Typical Energy Intensity Values For Selected Commercial Facilities Worldwide.
It should be noted that these values can hardly be used for benchmarking purposes. They are only indicative. This is primarily because energy intensity for such facilities significantly varies with the geographical location, population and culture.