Solution Manual Energy Systems Engineering Evaluation and Implementation Vanek Albright Angenent 3rd Edition – Updated 2024
Complete Solution Manual With Answers
Sample Chapter Is Below
Chapter 1 Introduction
Short answers:
1.1 Open-ended discussion question; see below.
1.2 Open-ended discussion question; see below.
1.3 HDI = 0.736; not in line with other countries
1.4 (a) see figure, (b) discussion question, (c) R2 = 0.457, (d) R2 = 0.0241, (e) and (f)
discussion questions
1.5 33.9, 29.5, 18.9, 23.0 EJ
1.6 98.6, 22.5, 38.8, 13.6 quads
1.7 Rank order: India, China, United States, Japan
1.8 140.4, 248, 22.3, 30.8, 81.9 mtoe
1.9 See explanation below.
Detailed answers:
1.1. Use the Internet or other resources to chart the development of an energy technology, from
its earliest beginnings to the present day. Did the roots of this technology first emerge prior to
the start of the industrial revolution? If so, how? If not, when did the technology first emerge?
In what ways did the industrial revolution accelerate the growth of the technology? More
recently, what has been the impact of the information age (e.g., computers, software,
electronically controlled operation, the Internet, etc.) on the technology?
Answer: A variety of answers are possible. For example, for wind energy, the
roots of the technology predate the industrial revolution, dating back to the
middle ages in Europe and earlier to use of wind for grinding grain in China
and the Middle East. The industrial revolution made possible metallurgical
techniques, which in turn enabled the precision fabrication of turbine blades,
electrical components, etc., used in wind electric conversion devices. Since the
1970s, information technology has been used computationally to improve the
shape of the turbine blades or operationally to integrate electricity from the
turbine into the grid.
Alternatively, for solar energy: solar energy predates the industrial revolution,
in that the use of passive solar design to heat buildings or keep them cool goes
back to the Ancient Greeks, the Chinese, and the Native Americans of the
southwest. Also, solar drying of clothes and food has been practiced since
antiquity. The industrial revolution accelerated the growth of solar energy by
making possible metallurgical techniques, which in turn enabled the precision
fabrication of experimental solar-powered, steam-driven devices starting in the
late 1800s. They also made possible home-sized solar water heating systems for
domestic hot water. Since the 1970s, information technology has been used
computationally to control the manufacture of solar panels, or to operate
tracking systems that optimize the position of solar panels relative to the sun.1.2. Note to instructors: it may be preferable to provide the students with the raw data
for the three countries used in this exercise, if you wish to save them time on the data
gathering and focus on the calculations and analysis.
Solution: From studying the accompanying graphs, it is clear that the trend in the United
Kingdom more closely resembles that of the United States than that of China.
On the GDP side, both the United States and the United Kingdom have gradually been
decreasing energy and CO2 emissions per unit of GDP produced, although the United
Kingdom is somewhat more efficient than the United States in producing a unit of GDP.
This is different from China, which saw dramatic reductions in energy and CO2
emissions per unit of GDP between 1980 and 2000, although these are perhaps slowing
after 2000.
On the population side, energy consumption per capita is slightly up for the United
Kingdom and the United States and CO2 emissions per capita are slightly down for the
period in question. This suggests that both countries are reducing the amount of CO2 per
unit of energy consumed. China is much lower in per capita measures than the other two
countries, but is seeing an upturn in both since 2000, so that it appears that in the most
recent years, China is moving in a different direction than the other two countries. Since
1995 or so, China is growing a much larger middle class, so it is not surprising that
energy and CO2 might turn upward in this way.
In general, the shape of the figures varies little whether one compares the three
countries in terms of per unit of energy or per unit of CO2 emitted. The most profound
change has been the reduction in energy and CO2 per unit of economic activity in
China. Compared to this trend, all other measures have not changed as much.
Figures to accompany Problem 1.2:
GJ/$GDP
120.0
100.0
80.0
60.0
40.0
20.0
0.0
China
UK
USA
1980 1985 1990 1995 2000 2004kgCO2/$GDP
GJ/capita
tonneCO2/cap
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
400.0
350.0
300.0
250.0
200.0
150.0
100.0
50.0
0.0
25.0
20.0
15.0
10.0
5.0
0.0
1980 1985 1990 1995 2000 2004
1980 1985 1990 1995 2000 2004
1980 1985 1990 1995 2000 2004
China
UK
USA
China
UK
USA
China
UK
USA1.3. The country of Fictionland has 31 million populations and consumes on average
12.09 exajoule (EJ), or 11.46 quads, of energy per year. The life expectancy of
Fictionland is 63 years, and the GDP per capita, on a PPP basis, is $13,800. The adult
literacy rate is 75%. The eligible and actual student enrollments for primary, secondary,
and college/university levels of education are given in the table below:
Eligible Enrolled
Primary 2,500,000 2,375,000
Secondary 2,100,000 1,953,000
University 1,470,000 558,600
Questions:
a. Calculate the HDI for Fictionland.
b. How does Fictionland’s HDI to energy intensity ratio compare to that of the
countries in the scatter charts in the chapter? Is it above, below, or on a par with
these other countries?
Solution: Part (a): Calculate components of HDI as follows:
Life expectancy:
63
25
633 . 0
85
25
CGER calculation:
Primary: 2.375M / 2.5M = 0.95
Secondary: 1.953M / 2.1M = 0.93
University: 558.6K / 1.47M = 0.38
CGER: (0.95 + 0.93 + 0.38) / 3 = 0.751
Educational index:
2
75 . 0
1
753 . 0
751 . 0
3
3
GDP calculation:
GDPFactor
Log
13800
Log
100
10
10
Log
10
822 . 0
40000
Log
100
10
The HDI is then the average of the three factors, or 0.736.
Part (b): Fictionland lies below the curve for the other countries.
1.4. Regression analysis of population, economic, and environmental data for countries
of the world. For this exercise, download from the Internet or other data source values
for the population, GDP in either unadjusted or PPP form, energy consumption, and
land surface area of as many countries as you can find. (Note to instructors: a possibledata set is available in the spreadsheet workbook that accompanies this instructor’s
manual suite.) Then answer the following questions:
a. From the raw data you have gathered, create a table of the countries along
with their GDP per capita, energy use per capita, and population density in
persons per square kilometer or square mile.
b. In part (a), did your data source allow you to include figures for all three
measures for all the major countries of all the continents of the world? If not,
what types of countries was it not possible to include, and why do you suppose
this might be the case?
c. Using a spreadsheet or some other appropriate software, carry out a linear
regression analysis of energy consumption per capita as a function of GDP per
capita. Produce a scatter chart of the values and report the R2 value for the
analysis.
d. One could also speculate that population density will influence energy
consumption, since a densely populated country will require less energy to move
people and goods to where they are needed. Carry out a second regression
analysis of energy consumption per capita as a function of population density.
Produce a scatter chart of the values and report the R2 value for the analysis.
e. Discussion: Based on the R2 value from parts (c) and (d), how well do GDPpc
and population density predict energy consumption? What other independent
variables might improve the model? Briefly explain.
f. Given the global nature of the world economy, what are some possible flaws
in using energy consumption figures broken down by country to make
statements about the relative energy consumption per capita of different
countries?
Solution:
a. Preprocess: from the raw data given, create a table of the countries to be
included in the model and the dependent and independent variable values for each
countries. Note: for simplicity and ease of grading, please do not augment the data
set with figures that you find in other sources.
ANSWER: Align the data so there is corresponding energy/cap, GDP/cap,
and population/area data for each country. Calculate these variables by
dividing the total energy consumption by the country population, total GDP
by population, etc. Table not shown for brevity.
b. Note that in part (a) not all countries are included. What can you say about the
countries which are typically left out of this list? A one- to two-sentence answer is
fine.
ANSWER: The data contain an estimated 96.6% of the total world energy
consumption, and 85.8% of the total world population. Thus, the countries
left out have lower energy consumption/capita than the countries evaluated.
We also may infer that these are third-world countries where energy
consumption, GDP, and population estimates may not be as readily
available. Other answers also accepted.c. Solve for the parameters a and b for the correlation between energy/capita and
GDP/capita using Excel or some other package. Also, give the R2 value and plot a
scatter chart with curve fit.
ANSWER: a = 61.3 (million Btu/capita), b = 0.0065 (million Btu/GDP)
Note: Answers may vary slightly due to students not including one of the
countries in the analysis, if the country is an “outlier” (e.g., Hong Kong,
Singapore).
(Removed outliers: Hong Kong, Singapore)
Energy vs GDP y = 0.0064x + 60.896
R2 = 0.4609
Energy [mBTU/cap]
1000.0
900.0
800.0
700.0
600.0
500.0
400.0
300.0
200.0
100.0
0.0
0 10000 20000 30000 40000 50000 60000 70000
GDP [$/cap]d. Now solve for the parameters a and b for the correlation between energy/capita
and population density, and also solve for the R2 value for the model and plot a
scatter chart with curve fit.
ANSWER: a = 155.41 (million Btu/capita), b = 0.00235 (million Btu/pop.
density)
Energy vs Population density y = 0.0235x + 155.41
R2 = 0.0241
Energy [mBTU/cap]
1000.0
900.0
800.0
700.0
600.0
500.0
400.0
300.0
200.0
100.0
0.0
0.0 1000.0 2000.0 3000.0 4000.0 5000.0 6000.0 7000.0
Pop density [persons/km2]
Energy vs Population density
y = 0.1086x + 144.5
R2 = 0.0181
Energy [mBTU/cap]
1000.0
900.0
800.0
700.0
600.0
500.0
400.0
300.0
200.0
100.0
0.0
0.0 200.0 400.0 600.0 800.0 1000.0 1200.0
Pop density [persons/km2]
e. Discussion: Based on the R2 value from part (d), how well do GDP and
population density predict energy consumption? What other independent variables
might improve the model? You can describe these variables and explain in words
how they might help, but you do not need to carry out any calculations (1 page
maximum).
ANSWER: GDP is shown to be a rather effective predictor of energy
consumption, with a visually linear positive trend and an R2 of 0.46.
Population density is not an effective predictor with a very low R2 (0.02),
with the slope largely influenced by countries with large population densities(Hong Kong and Singapore). There are potentially many variables that could
improve the fit of the model; a few examples could be:
Climate
Policy metrics (e.g., Kyoto protocol membership, democratic
government)
Metrics of transportation network (e.g., miles of paved road, miles of
railroad, etc.)
Metrics of energy resources (e.g., oil production from country,
nuclear technology)
f. Discussion: Given the global nature of the world economy, what is a possible flaw
in using energy consumption broken down by country to make statements about
energy consumption per capita? One paragraph maximum.
ANSWER: Energy consumption of a country does not necessarily mean that
all the energy was used to make goods or provide services by the people of
that country. For example, China produces many manufactured goods that
are subsequently consumed by citizens in the United States. So, this may
make Chinese citizens appear to be consuming more energy, when it should
in reality be attributed to American citizens. Similar arguments could be
made for other products, including oil, which can require an energy intensive
process to extract and refine before exporting to other countries.
1.5 According to the U.S. Department of Energy, in 2005 the United States’ industrial,
transportation, commercial, and residential sectors consumed 32.1, 28.0, 17.9, and 21.8
quads of energy, respectively. What are the equivalent amounts in EJ?
Solution: Multiply each value by 1.055 EJ/quad. Thus, the values are for the
four sectors, respectively: 33.9, 29.5, 18.9, and 23.0 EJ.
1.6 From Fig. 1-8, the energy consumption values in 2000 for the United States, Japan,
China, and India are 104, 23.7, 40.9, and 14.3 EJ, respectively. What are these same
values converted to quads?
Solution: Multiply each value by 0.948 quad/EJ. Thus, the values are for the
four countries, respectively: 98.6, 22.5, 38.8, and 13.6 quads.
1.7 Also for 2000, the estimated total CO2 emissions for the four countries given in
Problem 1.6 were 5970 MMT, 1190 MMT, 2986 MMT, and 1048 MMT, respectively.
Create a list of the four countries, ranked in order of decreasing carbon intensity per unit
of energy consumed. Give the units in either tonnes CO2 per terajoule (TJ) or tons per
billion Btu consumed.
Solution: The rank order is India, China, United States, and Japan. Solved here in are
metric units. For each country, divide total carbon by total energy. In rank order: 1048
MMT/14.3 EJ = 73.3 tonnes/TJ; 2986 MMT/40.9 EJ = 73.0 tonnes/TJ; 5970
MMT/2014 EJ = 57.4 tonnes/TJ; 1190 MMT/23.7 EJ = 50.2 tonnes/TJ. In standard
units, the values are 85.0 tons/bil.Btu, 84.7 tons/bil.Btu, 66.6 tons/bil.Btu, 58.3
tons/bil.Btu.1.8 Convert the energy consumption values for the countries of Australia, Brazil, Israel,
Portugal, and Thailand from Table 1-1 into units of million toe.
Solution: From the table, the starting values are 5.98, 10.56, 0.95, 1.31, and 3.49
EJ, respectively. Multiplying by 23.47 mtoe per EJ gives, respectively: 140.4,
248.0, 22.3, 30.8, and 81.9 mtoe.
1.9 Use the description of the derivation of the horsepower unit by James Watt and
others to show that 1 hp = 746 W, approximately.
Solution: Based on the paper: Smith, H. (1936). “The Origin of the Horsepower Unit.”
American Journal of Physics, Vol. 4, No. 3, pp. 120–122. According to Watt’s text, the
horsepower is based on a horse moving 2.5 mph and drawing 150 lb of weight up a
shaft (i.e., over a pulley, and ignoring frictional losses in the pulley). This is equivalent
to 13,200 ft/h, or, taking into account the weight being lifted, 1.98 million ft-lb/h, or
33,000 ft-lb/min. From www.onlineconversions.com, 1 ft-lb/min is equal to 22.6
milliwatts. Multiplying out gives 33,000 × 0.0226 = 746 watts.
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