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Table 3.3 Comparison of payback periods with previous studies
3.2 ARS-TEG vs. ORC-VCC; Thermoeconomic Comparison
ORC based WHR systems have been a topic of great interest in recent years. According
the publication data from Scopus[16], of the nearly 1700 journal and conference articles discussing
WHR using ORC published in between the period of 1973 to 2020, over 90% were published during
the period between 2010 to 2020. A number of recent publications [63]–[66] have studied the
integration potential of ORCs with Vapor Compression Cycles (VCC) as an integrated WHR mode.
In this section, a comparative thermoeconomic analysis for the application case of
refrigerated transport truck [15] between ORC-VCC and the novel ARS-TEG system, will be
presented. The purpose of this section is to investigate if the proposed ARS-TEG system can
provide the required cooling and how its economic outcomes differ from the more conventional
ORC-VCC based WHR system.
First, a description of the ORC-VCC model is presented. Next, a discussion of the exhaust
temperature and flowrate is presented. Then the closure parameters, device isentropic assumptions,
and working fluid selection criterion is discussed, following which the cycle model for the ORC-
VCC system is explained. This discussion is followed by the presentation of the results from the
cycles of the respective WHR systems. Finally, an economic analysis of the cost of the respective
39
systems will be presented along with discussion of the costing guides in relevant published
literature.
3.2.1 ORC-VCC System Description
ORC-VCC based systems can have a number of different configurations. Several recent
studies have employed a varied number of configurations for the ORC-VCC systems, such as,
multiple regenerators [67] in the ORC subsystem or employed a single working fluid with
combined condenser [68]–[70], shaft connected expander and compressor [66], [71], have been
studied.
For the purposes of the present study, a basic ORC cycle is combined with a VCC by means
of an electricity generator. Figure 3.5 shows the schematic for the ORC-VCC based WHR system.
The pressure of the ORC working fluid in a subcooled state at 1 is increased by a pump
located between 1→2. Between 2→3, the fluid’s temperature rises at it receives heat form the
recuperator. The boiler receives heat from the vehicle exhaust which is transferred to the ORC fluid
between 3→4. The fluid leaves the boiler as a superheated vapor at point 4. Between 4→5 the ORC
fluid expands to a lower pressure as the Expander/Turbine produces mechanical power which is
converted to electricity by an electricity generator. Between 5→6 the ORC fluid is cooled down in
the Recuperator. A condenser between 6→1 cools down the ORC fluid to a subcooled state.
40
1
2
3 4
5 G
6
7
8
9
10
Tcool
Ec
ORCVCC
Boiler
Expander
Recuperator
Condenser
Pump
Evaporator
CompressorThrottling
Valve
Condenser
Figure 3.5 Schematic for ORC-VCC based WHR system
All of the portion of the electricity generated by the ORC subsystem is supplied to the
pump of the ORC subsystem and the remainder is delivered to the compressor of the VCC
subsystem.
In the VCC subsystem, a high pressure, subcooled, refrigerant expands through an
isenthalpic throttling valve between 7→8, to lower temperature and pressure. Between 8→9 the
refrigerant receives heat from a cooled space in the Evaporator as it gets superheated at 9. The
refrigerant is compressed form 9→10 to the subsystem high pressure. The refrigerant is subcooled
in a condenser from 10→7 as heat is rejected to the ambient, completing the cycle.
3.2.2 Exhaust Temperature
The vehicle exhaust temperature is a function of the fuel requirements during different
cycles of operation [15] (plain, city traffic or mountains). Within each cycle, the initial flowrate
and temperature are typically (in case of cold-start) not sufficient to operate the WHR system.
41
Moreover, in the case of city traffic operation cycle, the vehicle is mostly in an idling mode,
therefore, the exhaust temperature is not high enough for WHR operation.
(a) (b)
Figure 3.6 (a) Cumulative frequency of exhaust temperatures (b) Cumulative frequency of available exhaust heat to operate a 5kW absorption refrigeration system for different driving
cycles [15]
Figure 3.6 shows two graphs from the work of Koehler et al. [15] on WHR in refrigerated
transport vehicle using absorption refrigeration. Figure 3.6(a) shows a qualitative distribution of
cumulative frequency of exhaust temperature ranges. From this graph it can be seen that the higher
temperature frequency of plain cycle is the highest. The horizontal axis of Figure 3.6(b) shows
normalized generator/desorber heat inputs, where a value of unity represents a generator/desorber
heat input sufficient, to provide cooling of 5 kW. Negative values occur when the temperature is
lower than the desorption temperature of the refrigerant in the absorption system.
Figure 3.7 shows real-world velocity of a heavy-duty diesel vehicle and the selective
catalytic reduction (SCR) device inlet temperature data , with respect to time, from a cold-start
stage [51]. From the graph, clear fluctuations in exhaust temperature can be seen. To model the
ORC-VCC and ARS subsystems, an average temperature may be selected to conduct a comparison
42
Figure 3.7 Real-world velocity and SCR temperature data for a heavy-duty diesel vehicle [51]
Additionally, Figure 3.8 shows a frequency distribution graph for the exhaust temperature
of a heavy-duty refrigerated transport truck (~450hp) [51]. From the data presented in the graph,
an average exhaust temperature of ~250°C is chosen as the heat input temperature for ORC-VCC
and ARS-TEG systems for the purposes of thermoeconomic comparison.
Figure 3.8 Frequenc distribution of exhasut temperatures for a heavy-duty refrigeated transport
truck [51]
43
3.2.3 Closure Parameters for Heat Exchangers
Heat exchanger sizing is significant in establishing basis for thermoeconomic analysis and
comparison of any thermal cycles. For refrigeration systems, like the ARS subsystem discussed in
this study, AHRI [72], [73] guidelines and standards can be used to set key parameters like degree
of subcooling (condensers) and superheating (evaporators or boilers) and closest approach
temperatures (recuperators).
With respect to ORC, closure parameters were chosen by reviewing previous studies of
similar/proportionate source temperatures, sink temperatures, and input waste heat rate and power
generation. Majority of literature reviewed [66], [68], [74] refer to ‘Pinch Point Temperature’
(ΔTpp) for the ORC subsystem (typically 5-10 K). Other published works assumed no superheat
(evaporators/boilers) or subcooling (condensers) [69]. According to Park et al [75], ORC systems
with a power output of 3-50 kW are typically superheated types. To maintain consistency of
comparison between the two WHR systems, and based upon the preceding discussion, AHRI
guidelines were adopted for both ORC-VCC and ARS-TEG systems.
Table 3.4 lists the closure parameters for heat exchangers in the ORC-VCC system:
Subsystem Component Parameter
ORC
Boiler CAT 10 K
DoSH 2 K
Recuperator CAT 10 K
εHX 0.75
Condenser CAT 10 K
DoSC 2 K
VCC
Evaporator CAT 5 K
DoSH 2 K
Condenser CAT 10 K
DoSC 2 K
Table 3.4 Heat Exchanger Closure Parameters for ORC-VCC
44
In the above table CAT, DoSH and DoSC stand for ‘Closest Approach Temperature’,
‘Degree of Superheat’ and ‘Degree of Subcooling”, respectively. Moreover, the heat source
temperature, cooling delivery and the ambient temperature (250°C, -15°C and 30°C respectively)
are assumed to be the same for both the ORC-VCC and ARS-TEG systems.
3.2.4 Isentropic Efficiency Assumptions and Working Fluids
To estimate the performance of an ORC-VCC WHR, representative isentropic efficiencies
are assumed for the compressors, expanders, and pumps. Actual values of isentropic efficiencies of
these components vary with the size of the component and cycle pressure ratios, among other
parameters.
For example, according Park et al’s review [75] of experimental studies of ORC systems,
ORC based WHR systems that output 1-5kW of power, typically have an expander efficiency in
the range of 0.5-0.75. Table 3.5 lists a summary of isentropic efficiencies of different components
along with the waste heat input, net electric power out and cooling capacities for several ORC-
Table 6.1 Control Matrix for Experimental Facility
6.2 Instrumentation
Table 6.2 lists the different types of instruments used to measure temperatures, flowrates
and pressures for the four fluid loops in the WHR system. Also included in the table are the
measurement uncertainties associated with these measurements. Almost all of the temperature
measurement devices were calibrated against a high accuracy reference thermometer, yielding
estimated uncertainties of ±0.25°C. The high temperature thermocouples for the simulated exhaust
gas stream were not calibrated, and therefore are assumed to have ±1°C uncertainty.
For data collection, a data acquisition unit (DAQ) is interfaced with a PC running LabView
[98], and a LabView code was setup to save the collected measurements to a spreadsheet file.
The TEG power output is recorded manually. The readings from the DC resistive load
monitor are recorded twice; once at the start of the experimental run when the system achieves a
relative steady state, and again just before the experimental run is complete. The final reading ,
which is the average of the two readings, has an uncertainty of ±0.2% [99].
93
Temp (°C) Flowrate Pressure (Pa) Type Unc Type Unc Type Unc
Exhaust K ±1 Diff Pressure ±0.15 L m-1 Diff Pressure ±0.31
Coupling Fluid T ±0.25 Oval Gear ±0.04 L m-1 Transducer ±6 kPa
NH3-LiNO3 T ±0.25 Ultrasonic ±0.02 L m-1 Transducer ±6 kPa
NH3 T ±0.25 Ultrasonic ±0.006 L m-1 Transducer ±6 kPa
Ethelene Glycol T ±0.25 Oval Gear ±0.04 L m-1 - -
Table 6.2 Measurement types and uncertainties for ARS-TEG experimental facility
6.3 Exergetic Efficiency
The theoretical maximum heat available for recovery from an exhaust stream is the amount
of energy the stream must reject to achieve thermal equilibrium with the ambient. The ratio between
this theoretical maximum and the actual waste heat recovered by a WHR system is known as the
exergetic efficiency of a WHR system. For HAU-2, the exergetic efficiency is defined as:
𝜼𝒆𝒙 = (∆𝑬��𝒐𝒊𝒍 + 𝑷𝑻𝑬𝑮) 𝑬��𝒂𝒊𝒓,𝒊𝒏⁄ 6.1
Where 𝜼𝒆𝒙 is the Exergetic Efficiency of the HAU, ∆𝑬��𝒐𝒊𝒍 is the rate of change of
exergy of the oil as it passes through the HAU, in W. PTEG is the total electric power generated by
the TEG array in W and 𝑬��𝒂𝒊𝒓,𝒊𝒏 is the exergy rate of the air at inlet.
Exergetic efficiency, defined this way for a WHR application, indicates the ratio of useful
heat and electrical power recovered from an input hot stream of specific exergy content. In Figure
6.2, the exergetic efficiencies are graphically presented for different input temperatures and mass
flowrates of the hot and cold streams. It can be seen form the graphs that the exergetic efficiencies
are higher for high thermal oil inlet temperatures. This is expected as the smaller the difference
between delivery and source temperature for a WHR system, the higher is its exergetic efficiency.
94
0.005 0.01 0.005 0.01
0.01 0.02 ṁoil
ṁair
0.0
0.1
0.2
0.3
0.4
0.5
ηex [
-]
Toil,in = 50°C
[kg·s-1]
[kg·s-1]
(a)
0.005 0.01 0.005 0.01
0.01 0.02
0.0
0.1
0.2
0.3
0.4
0.5
ηex [
-]
250[°C]
313[°C]
400[°C]
Toil,in = 75°C
(b)
Exergetic Efficiency
0.005 0.01 0.005 0.01
0.01 0.02 ṁoil
ṁair
0.0
0.1
0.2
0.3
0.4
0.5Toil,in = 100°C
ηex [
-]
[kg·s-1]
[kg·s-1]
(c)
Tair,in
Figure 6.2 Exergetic Efficiency of HAU-2 at different inlet conditions
Another aspect to consider is the mass flowrates of the thermal oil stream and simulated
exhaust (air) stream. From the graphs, it can be observed that, within the margin of error, the
exergetic efficiency is strongly sensitive to the air mass flowrate, with greater exergetic efficiencies
recorded at higher air flowrate when other parameters remain unchanged. Since, air has a much
lower thermal capacity compared to the thermal oil, a higher flowrate of air results in a better
source-use match in terms of heat capacity rates, resulting in higher exergetic efficiencies.
Thus, a thermal oil inlet temperature closer to the hot exhaust stream inlet temperature, and
a higher air mass flow rate result in higher exergetic efficiencies.
95
6.4 Thermoelectric Power
Owing to the relatively small amount of waste heat recovered in the form of TEG electrical
power generation, exergetic efficiency, as defined previously, does not inform greatly as regards to
TEG performance. For this, the total TEG power generated for each experimental run is analyzed
in this section.
0.005 0.01 0.005 0.01
0.01 0.02 ṁoil
ṁair
0
5
10
15
20
25
PT
EG
,to
t [W
]
[kg·s-1]
[kg·s-1]
Toil,in = 50°C
(a)
0.005 0.01 0.005 0.01
0.01 0.02
0
5
10
15
20
25
PT
EG
,to
t [W
]
250[°C]
313[°C]
400[°C]
Toil,in = 75°C
(b)
Total TEG Power Tair,in
0.005 0.01 0.005 0.01
0.01 0.02 ṁoil
ṁair
0
5
10
15
20
25
PT
EG
,to
t [W
]
[kg·s-1]
[kg·s-1]
Toil,in = 100°C
(c)
Figure 6.3 TEG Power produced by HAU-2 at different inlet conditions (uncertainty of ±0.2%)
.
96
Graphs in Figure 6.3, for different inlet temperature and mass flowrate conditions, show
the TEG power produced for all 30 experimental runs. It can be observed that the thermoelectric
power output is greatly sensitive to the temperature difference between the hot and cold stream at
the inlet of the HAU-2.
For given inlet temperatures and air mass flowrate, the oil mass flowrate has no significant
impact on the total TEG power generated. But for the cases where the air mass flowrate is reduced,
there is a significant drop in TEG power output. This sensitivity to air mass flowrate is caused by
the much higher thermal capacity of the thermal oil stream relative to the thermal capacity of the
air stream.
Thus, TEG power is greater when the temperature difference between the fluid streams and
the flowrate of air are relatively high. Additionally, for the same hot and cold side temperatures,
the increase in the thermal fluid side flowrate only minimally affects the TEG power production,
instead, TEG power is more sensitive to the air flowrate.
6.5 Comparison of HAU Model Predictions and Experimental Results
The model developed for HAU-2 (Section 4.3) takes as inputs: the time averaged flowrates
of thermal oil stream and simulated exhaust (air) stream, the inlet temperatures of those streams,
and the average TEG power produced for each experimental run. The model calculates the outlet
temperatures, air side pressure drop, heat loss rate, heat transfer rates in each stage, and overall heat
transfers for each stream.
Figure 6.4 and Figure 6.5 present the comparison between the model’s predicted and the
experimentally measured Qoil and Qair, respectively, for all the experimental cases.
97
The average relative error, and the average absolute relative error in the predicted values
of Qoil is ~0.3, respectively. The average relative error, and the average absolute relative error in
the predicted values of Qair is ~0.2.
The model output of Qoil depends on the Tair,out , as shown by equations 6.2 and 6.3.
However, there is an overestimate error associated with the Tair,out measurement (discussed in the
Appendix).
𝑸𝒐𝒊𝒍 = 𝑸𝑎𝑖𝑟 − 𝑸𝒍𝒐𝒔𝒕 − 𝑷𝑻𝑬𝑮 6.2
𝑸𝒂𝒊𝒓 = ��𝒂𝒊𝒓𝒄𝒑,𝒂𝒊𝒓(𝑻𝒂𝒊𝒓,𝒊𝒏 − 𝑻𝒂𝒊𝒓,𝒐𝒖𝒕) 6.3
Meanwhile, the experimental measurement of Qoil is dependent on the Toil measurements
which have low uncertainties associated to them. In general, the model prediction for both Qoil and
Qair are consistent with the trend of experimental observation of greater heat transfer rates for larger
temperature differences between the hot and cold stream.
Moreover, due to the lower heat capacity rate of air compared to the thermal oil, the heat
transfer rate increase with an increase in air flowrate when other parameters are kept constant.
98
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02ṁoil
ṁair
Tair,in
0
200
400
600
800
1000
1200
1400
1600
1800Q
oil
,mo
d [
W],
Qo
il,e
xp [
W]
[kg·s-1]
[kg·s-1]
[°C]
Toil,in = 50°C
(a)
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02
0
200
400
600
800
1000
1200
1400
1600
1800
Qoil,mod Qoil,exp
Toil,in = 75°C
(b)
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02
0
200
400
600
800
1000
1200
1400
1600
1800Toil,in = 100°C
(c)Qoil Comparison
Figure 6.4 Qoil Model vs Experiment Comparison
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02ṁoil
ṁair
Tair,in
0
200
400
600
800
1000
1200
1400
1600
1800
Qai
r,m
od [
W],
Qai
r,ex
p [
W]
[°C]
[kg·s-1]
[kg·s-1]
Toil,in = 50°C
(a)
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02
0
200
400
600
800
1000
1200
1400
1600
1800
Qair,mod Qair,exp
Toil,in = 75°C
(b)
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02
0
200
400
600
800
1000
1200
1400
1600
1800Toil,in = 100°C
(c)Qair Comparison
Figure 6.5 Qair Model vs Experiment Comparison
99
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02ṁoil
ṁair
Tair,in
0
20
40
60
80
100
120
140T
oil
,ou
t,m
od [
°C],
To
il,o
ut,
exp [
°C]
[kg·s-1]
[kg·s-1]
[°C]
Toil,in = 50°C(a)
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02
0
20
40
60
80
100
120
140
Toil,out,mod Toil,out,exp
Toil,in = 75°C
(b) Toil,out Comparison
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02
0
20
40
60
80
100
120
140 Toil,in = 100°C(c)
Figure 6.6 Toil Model vs Experiment Comparison
0.02
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01ṁoil
ṁair
Tair,in
0
20
40
60
80
100
120
140
160
180
200
Tai
r,o
ut,
mod [
°C],
Tai
r,out,
exp [
°C] Toil,in = 50°C
[°C]
[kg·s-1]
[kg·s-1]
(a)
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02
0
20
40
60
80
100
120
140
160
180
200
Tair,out,mod Tair,out,exp
Toil,in = 75°C
(b)
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02
0
20
40
60
80
100
120
140
160
180
200Toil,in = 100°C
(c)Tair,out Comparison
Figure 6.7 Tair Model vs Experiment Comparison
100
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02 ṁoil
ṁair
Tair,in
0
20
40
60
ΔP
mo
d [
Pa]
,ΔP
exp [
Pa]
[°C]
[kg·s-1]
[kg·s-1]
Toil,in = 50°C
(a)
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02
0
20
40
60
ΔP
mo
d [
Pa]
,ΔP
exp [
Pa]
ΔPmod
ΔPexp
Toil,in = 75°C
(b)
ΔP Comparison
0.005 0.01 0.005 0.01
250 313 400 250 313 250 313 400 250 313
0.01 0.02 ṁoil
ṁair
Tair,in
0
20
40
60
ΔP
mo
d [
Pa]
,ΔP
exp [
Pa]
[°C]
[kg·s-1]
[kg·s-1]
Toil,in = 100°C(c)
Figure 6.8 ΔPexp vs ΔPmodel comparison
In Figures 6.6 and 6.7, a comparison between the engineering model’s predicted Toil,out and
Tair,out with their corresponding experimental measurement is presented.
The absolute relative and relative errors in the predicted values of Toil,out are ~0.06 and -
0.06, respectively. Meanwhile, the absolute relative and relative errors in the predicted values of
Tair,out are both ~0.25 in value.
101
It can be seen that, for all cases, the predicted Toil,out has a much greater degree of qualitative
similarity (within the margin of error) with the measured Toil,out, while the error in the model
predicted Tair,out is greater than for Toil,out.
Another comparison to consider is the pressure drop in the air stream as it goes through the
HAU. From the graphs presented in Figure 6.8, it can be seen that the pressure drop is sensitive to
the air mass flowrate. The model’s prediction of pressure drop in the HAU is consistent with the
experimentally measured value (within the margin of error). The absolute relative and relative
errors in the predicted values of ΔP are ~0.14 and ~-0.13, respectively
6.6 Discussion
Two different designs for an integrated Thermoelectric Generator and Absorption
Refrigeration based WHR heat acquisition units (HAU) were developed. Their performance was
evaluated at different temperatures and flowrates. Through the analysis of the experimental results
from the first design of the HAU (HAU-1), important design factors were identified:
• Spreading Resistance is an increased thermal resistance when there exists an
area mismatch between the TEGs and the air-side heat exchange surface. This
leads a lower TEG hot junction temperature and less efficient TEG operation.
• An undersized oil-block results in higher TEG cold junction temperatures and
a reduction in TEG efficiency. An undersized oil-block also results in lower
thermal oil outlet temperatures.
102
• Optimal TEG array sizing is important in designing an HAU as an increase in
the number of TEGs results in a larger heat exchange area between the hot and
cold stream, thereby reducing the overall ΔT across the TEG junctions.
Based on these parameters, an improved HAU (HAU-2) was developed and experimentally
tested. The following changes were made to the TEG design:
• To increase thermal energy transfer to the thermal oil, HAU-2 was divided into
two-stages, with the TEGs present in the first stage. The first stage allows the
TEGs to operate at higher ΔT while the second stage recovers more heat
downstream of the TEG which increases the thermal oil outlet temperature
compared to the one-stage design of HAU-1.
• To minimized the effect of spreading resistance, a copper heat spreader was
utilized in the first stage.
• Based on Rattner’s [43] work, the optimum number of TEGs (2) were used in
HAU-2 as opposed to 6 TEGs in HAU-1.
These changes resulted in a HAU system with 4 times more TEG power output than HAU-
1. Moreover, the exergetic efficiencies for the HAU-2 (~40%) were found to be comparable to
ORC based WHR systems [100], [101]. The experimental results also indicate a great degree of
sensitivity of key system variables to the exhaust/air-side flowrate.
An engineering model for HAU-2 was developed and system performance parameters were
compared to the experimental observations. A general congruence of trend between the model’s
predictions and experimental measurements was observed after air-side flowrate corrective
calibration (see Appendix), though some error still exists due air-side temperature outlet
measurements.
103
Therefore, current study establishes that a cascaded approach to WHR based on TEG and
ARS is a viable pathway for WHR especially for application where cooling and electric power are
the desired modes of WHR.
104
Conclusions and Recommendations for Future
Research
105
This chapter provides a discussion of the major findings of this Ph.D. dissertation with
regards to ARS-TEG based integrated cascaded waste heat recovery systems. Thereafter, some
recommended pathways for future research on this topic are also suggested.
7.1 ARS-TEG thermoeconomic studies
Chapter 3 discusses the thermoeconomic studies conducted for the vehicle and carburizing
furnace applications of the proposed ARS-TEG based WHR system. Cycle models were developed
based on AHRI guidelines for closure parameters of heat exchangers and reasonable component
efficiencies were assumed. Another complementary study compared the performance of ORC-
VCC and ARS-TEG based WHR systems for vehicle application and compared the capital
investment cost of the two systems. The key findings for the thermoeconomic studies are:
1. The payback periods for ARS-TEG based cascaded WHR systems are competitive
with conventional ORC based WHR systems.
2. The temperature drop across the TEGs is a significant design parameter, as it affects
the efficiency of both pathways (ARS and TEGs). If the ΔT across the TEG junctions
is too large, the desorber heat delivery temperature can drop below the required
temperature.
3. In light of the real-world data [51] for a refrigerated transport vehicle’s exhaust
temperaure fluctuations, strategies must be employed to ensure a steady operation of
the WHR system. It was proposed in this disseration that electric heaters may be used
to deliver heat to the desorber when the exhaust tempeatures are too low. At higher
than design temperatures, the exhaust can be re-routed to bypass the desorber.
106
4. By comparing the captial cost of the ARS-TEG system to an ORC-VCC system, it was
surmized that the cost of the ARS-TEG sysytem ($10,464.05), without the TEGs was
significanly lower than the cost of the ORC-VCC system ($15,390.7). But the TEGs
cost another $10,269 for the ARS-TEG system, making it slightly more expensive.
Since, the electrical power requirement of a refrigerated transport vehicle is not the
primary requirement, a reduction in the number of TEGs can result in a more
competitive ARS-TEG based WHR system.
7.2 HAU design
In Chapter 4 of this Ph.D. dissertation a detailed study of the iterative design and
development of HAU for the ARS-TEG based WHR system was presented. Two HAUs were
constructed during the course of this study. Findings from the performance of the first HAU
informed the design of HAU-2. Some of the key findings form this section of study are:
1. Spreading resistance due to the mismatch in contact area of the TEGs and fins on the
air-side results in a large spreading resistance (Rsp = 0.023 K W-1). This results in a
lower TEG hot side temperature and lower TEG efficiency. Using a copper heat
spreader can minimize the spreading resistance (Rsp = 0.013 K W-1).
2. The number of TEGs in a WHR system must be optimized as increasing the number
of TEGs beyond an optimal number can lead to poorer WHR performance as the
average ΔT across the TEGs drops. For HAU-1, using 6 TEGs the maximum power
produced for a specific set of inlet conditions was ~4 W, whereas, for the same inlet
conditions, HAU-2 generated a maximum of ~21 W.
107
3. Heat exchange surface sizing on both the hot and cold sides of an HAU requires
optimization. Backpressure, especially on the hot exhaust side of the HAU, is a
significant design parameter as an increase in backpressure required additional work.
If the heat exchange surfaces on the thermal oil side are undersized, the TEG cold side
temperature and hot side temperature ‘pinch’ and the efficiency of the TEGs drops.
The thermal fluid’s outlet temperature is also lower than required if the oil side heat
exchange surfaces are undersized.
7.3 HAU Model prediction and experimental results
In Chapter 4 and Chapter 5 a detailed description of the HAU and cycle models is
presented. In Chapter 6 of this dissertation a detailed comparison of the model predication and
experimentally observed values is presented. Some of the key findings are:
1. Due to the smaller heat capacity (cp) of the exhaust side flow, parameters like Qoil,
PTEG and Toil are more sensitive to the inlet conditions on the air side.
2. The exergetic efficiency of the HAU is comparable to that of a typical ORC based
WHR systems (~40%).
7.4 Recommendations for future research
More studies are needed to improve upon the ARS-TEG based WHR system proposed in
this Ph.D. dissertation. My recommendations for future works include:
108
7.4.1 Advanced HAU design
The HAU developed during this study can be improved upon by eliminated the thermal
fluid coupling fluid between the HAU and desorber. This can be achieved by designing and
developing an integrated desorber where the heat from the exhaust cascades through the TEGs and
is delivered to the ARS working fluid solution directly. This has the potential to significantly
increase the performance of the ARS-TEG waste heat recovery system by:
• Delivering higher-grade waste to the ARS sub-system
• Eliminating parasitic heat losses due to the thermal oil flow circuit
• Reduction in cost by eliminated additional heat exchange surfaces and thermal oil
as working fluid
7.4.2 Experimental Investigations
In addition to the HAU specific experimental work reported in this Ph.D. dissertation
(Chapter 5 and Chapter 6), additional full system (ARS+TEG) experimental data is needed to gain
further insights into the operation of the WHR system at different conditions.
109
Appendex A: Velocity Measurement
Calibration
Preliminary analysis of the experimental data indicated the presence of an over-estimate
error in measurements related to the air-side flow. It was suspected that the temperature and
flowrate measurement locations at the exit of the HAU were possibly within the Vena Contracta
effect as shown in Figure A.1.
Vena Contracta Effect
Pitot TubeThermocouple
Figure A.1 Stage-2 Airflow, immediately at the exit of the HAU, is contracted due to the shape
of the HAU. This contraction leads to an increase in the velocity at the location of temperature
and velocity measurement
To minimize the effect of this error, a post-experiment, flowrate calibration was performed
as a correction for the measure data by taking flow measurements downstream using a hotwire
anemometer (Figure A.2). However, some error due to Tair,out measurements still persists.
110
Figure B.2 Stage-2 Vcorr (Velocity measurement downstream of original measurement) vs Vmsrd
(original measurement)
0
0.5
1
1.5
2
2.5
3
3.5
0 0.5 1 1.5 2 2.5 3 3.5 4
Vco
rr[m
/s]
Vmsrd [m/s]
Velocity Correction – Vmsrd vs. Vcorr
111
Bibliography
[1] P. A. Owusu and S. Asumadu-Sarkodie, “A review of renewable energy sources, sustainability issues and climate change mitigation,” Cogent Eng., vol. 3, no. 1, Apr. 2016,
doi: 10.1080/23311916.2016.1167990.
[2] EPA, “Sources of Greenhouse Gas Emissions,” Clim. Chang., pp. 1–2, 2015.
[3] E. Worrell, L. Bernstein, J. Roy, L. Price, and J. Harnisch, “Industrial energy efficiency
and climate change mitigation,” Energy Effic., vol. 2, no. 2, pp. 109–123, 2009, doi:
10.1007/s12053-008-9032-8.
[4] A. S. Rattner and S. Garimella, “Energy harvesting, reuse and upgrade to reduce primary energy usage in the USA,” Energy, vol. 36, no. 10, pp. 6172–6183, 2011, doi:
10.1016/j.energy.2011.07.047.
[5] H. Jouhara, N. Khordehgah, S. Almahmoud, B. Delpech, A. Chauhan, and S. A. Tassou,
“Waste heat recovery technologies and applications,” Thermal Science and Engineering
Progress, vol. 6. Elsevier Ltd, pp. 268–289, 01-Jun-2018, doi: 10.1016/j.tsep.2018.04.017.
[6] J. Ilona, C. W. T, and D. Amber, “Waste Heat Recovery. Technology and Opportunities in U.S. Industry; Sponsor Org.: USDOE Office of Energy Efficiency and Renewable Energy
[7] R. Shen, X. Gou, H. Xu, and K. Qiu, “Dynamic performance analysis of a cascaded thermoelectric generator,” Appl. Energy, vol. 203, pp. 808–815, Oct. 2017, doi:
[10] M. Liu, W. Saman, and F. Bruno, “Development of a novel refrigeration system for refrigerated trucks incorporating phase change material,” Appl. Energy, vol. 92, pp. 336–
342, 2012, doi: 10.1016/j.apenergy.2011.10.015.
[11] S. Brückner, S. Liu, L. Miró, M. Radspieler, L. F. Cabeza, and E. Lävemann, “Industrial
waste heat recovery technologies: An economic analysis of heat transformation technologies,” Appl. Energy, vol. 151, pp. 157–167, 2015, doi:
10.1016/j.apenergy.2015.01.147.
[12] D. Lu et al., “Modeling and analysis of an ammonia–water absorption refrigeration system
utilizing waste heat with large temperature span,” Int. J. Refrig., vol. 103, pp. 180–190,
Jul. 2019, doi: 10.1016/j.ijrefrig.2019.04.008.
112
[13] K. E. Herold, R. Radermacher, and S. A. Klein, Absorption Chillers and Heat Pumps.
2016.
[14] H. Kaibe, T. Kajihara, and S. Fujimoto, “Recovery of Plant Waste Heat by a
[15] J. Koehler, W. J. Tegethoff, D. Westphalen, and M. Sonnekalb, “Absorption refrigeration system for mobile applications utilizing exhaust gases,” Int. Commun. Heat Mass Transf.,
[18] J. Shon, H. Kim, and K. Lee, “Improved heat storage rate for an automobile coolant waste
heat recovery system using phase-change material in a fin-tube heat exchanger,” Appl.
Energy, vol. 113, pp. 680–689, 2014, doi: 10.1016/j.apenergy.2013.07.049.
[19] A. Mahmoudi, M. Fazli, and M. R. Morad, “A recent review of waste heat recovery by Organic Rankine Cycle,” Appl. Therm. Eng., vol. 143, no. January, pp. 660–675, 2018,
doi: 10.1016/j.applthermaleng.2018.07.136.
[20] G. Bisio, “Energy recovery from molten slag and exploitation of the recovered energy,”
Energy, vol. 22, no. 5, pp. 501–509, 1997, doi: 10.1016/S0360-5442(96)00149-1.
[21] S. Lan, Z. Yang, R. Chen, and R. Stobart, “A dynamic model for thermoelectric generator
applied to vehicle waste heat recovery,” Appl. Energy, vol. 210, pp. 327–338, Jan. 2018,
doi: 10.1016/j.apenergy.2017.11.004.
[22] O. Badr, S. D. Probert, and P. W. O’Callaghan, “Selecting a working fluid for a Rankine-
[23] H. Tian, G. Shu, H. Wei, X. Liang, and L. Liu, “Fluids and parameters optimization for
the organic Rankine cycles (ORCs) used in exhaust heat recovery of Internal Combustion Engine (ICE),” Energy, vol. 47, no. 1, pp. 125–136, 2012, doi:
10.1016/j.energy.2012.09.021.
[24] J. Deng, R. Z. Wang, and G. Y. Han, “A review of thermally activated cooling
technologies for combined cooling, heating and power systems,” Prog. Energy Combust.
Sci., vol. 37, no. 2, pp. 172–203, 2011, doi: 10.1016/j.pecs.2010.05.003.
[25] X. Zhang, L. Wu, X. Wang, and G. Ju, “Comparative study of waste heat steam SRC,
ORC and S-ORC power generation systems in medium-low temperature,” Appl. Therm.
Eng., vol. 106, pp. 1427–1439, Aug. 2016, doi: 10.1016/j.applthermaleng.2016.06.108.
[26] T. C. Hung, T. Y. Shai, and S. K. Wang, “A review of organic rankine cycles (ORCs) for
the recovery of low-grade waste heat,” Energy, vol. 22, no. 7, pp. 661–667, Jul. 1997, doi:
10.1016/S0360-5442(96)00165-X.
113
[27] B. F. Tchanche, G. Lambrinos, A. Frangoudakis, and G. Papadakis, “Low-grade heat
conversion into power using organic Rankine cycles - A review of various applications,”
Renewable and Sustainable Energy Reviews, vol. 15, no. 8. Elsevier Ltd, pp. 3963–3979,
2011, doi: 10.1016/j.rser.2011.07.024.
[28] N. Galanis, E. Cayer, P. Roy, E. S. Denis, and M. Désilets, “Electricity generation from
low temperature sources,” J. Appl. Fluid Mech., vol. 2, no. 2, pp. 55–67, 2009.
[29] T. C. Hung, T. Y. Shai, and S. K. Wang, “A review of organic rankine cycles (ORCs) for
the recovery of low-grade waste heat,” Energy, vol. 22, no. 7, pp. 661–667, 1997, doi:
10.1016/S0360-5442(96)00165-X.
[30] G. Yu, G. Shu, H. Tian, Y. Huo, and W. Zhu, “Experimental investigations on a cascaded
steam-/organic-Rankine-cycle (RC/ORC) system for waste heat recovery (WHR) from diesel engine,” Energy Convers. Manag., vol. 129, pp. 43–51, Dec. 2016, doi:
10.1016/j.enconman.2016.10.010.
[31] H. Wang et al., “Performance of a combined organic Rankine cycle and vapor
compression cycle for heat activated cooling,” Energy, vol. 36, no. 1, pp. 447–458, 2011,
doi: 10.1016/j.energy.2010.10.020.
[32] W. Han, L. Sun, D. Zheng, H. Jin, S. Ma, and X. Jing, “New hybrid absorption-
compression refrigeration system based on cascade use of mid-temperature waste heat,”
Appl. Energy, vol. 106, pp. 383–390, 2013, doi: 10.1016/j.apenergy.2013.01.067.
[33] R. Funahashi, “Waste heat recovery using thermoelectric oxide materials,” Sci. Adv.
Mater., vol. 3, no. 4, pp. 682–686, Aug. 2011, doi: 10.1166/sam.2011.1200.
[34] X. Shi and D. Che, “A combined power cycle utilizing low-temperature waste heat and LNG cold energy,” Energy Convers. Manag., vol. 50, no. 3, pp. 567–575, Mar. 2009, doi:
10.1016/j.enconman.2008.10.015.
[35] M. He, X. Zhang, K. Zeng, and K. Gao, “A combined thermodynamic cycle used for
waste heat recovery of internal combustion engine,” Energy, vol. 36, no. 12, pp. 6821–
6829, 2011, doi: 10.1016/j.energy.2011.10.014.
[36] A. Khaliq, R. Kumar, and I. Dincer, “Exergy analysis of an industrial waste heat recovery
based cogeneration cycle for combined production of power and refrigeration,” J. Energy
[37] I. Mikulic, R. Zhan, and S. Eakle, “Dependence of fuel consumption on engine backpressure generated by a DPF,” in SAE Technical Papers, 2010, doi: 10.4271/2010-01-
0535.
[38] J. Li, S. Xu, and S. Kong, “Investigation of the absorption-compression hybrid
refrigeration applied to the coach air conditioning,” Heat Transf. Res., vol. 46, no. 3, pp.
[39] A. Sonthalia, S. Reddy, C. R. Kumar, and K. Kamani, “Theoretical Investigation of Waste Heat Recovery from an IC Engine Using Vapor Absorption Refrigeration System and
Thermoelectric Converter,” Heat Transf. - Asian Res., vol. 44, no. 6, pp. 499–514, 2015,
doi: 10.1002/htj.21133.
114
[40] J. H. Meng, X. D. Wang, and W. H. Chen, “Performance investigation and design
optimization of a thermoelectric generator applied in automobile exhaust waste heat
recovery,” Energy Convers. Manag., vol. 120, pp. 71–80, 2016, doi:
10.1016/j.enconman.2016.04.080.
[41] X. Liu, Y. D. Deng, Z. Li, and C. Q. Su, “Performance analysis of a waste heat recovery thermoelectric generation system for automotive application,” Energy Convers. Manag.,
vol. 90, pp. 121–127, 2015, doi: 10.1016/j.enconman.2014.11.015.
[42] W. He, S. Wang, C. Lu, X. Zhang, and Y. Li, “Influence of different cooling methods on
thermoelectric performance of an engine exhaust gas waste heat recovery system,” Appl.
Energy, vol. 162, pp. 1251–1258, 2016, doi: 10.1016/j.apenergy.2015.03.036.
[43] A. S. Rattner and T. J. Meehan, “Simple analytic model for optimally sizing thermoelectric generator module arrays for waste heat recovery,” Appl. Therm. Eng., vol.
146, pp. 795–804, 2019, doi: 10.1016/j.applthermaleng.2018.10.003.
[44] S. Vasta, V. Palomba, D. La Rosa, and W. Mittelbach, “Adsorption-compression cascade
cycles: An experimental study,” Energy Convers. Manag., vol. 156, pp. 365–375, Jan.
2018, doi: 10.1016/j.enconman.2017.11.061.
[45] K. C. A. Alam, M. Z. I. Khan, A. S. Uyun, Y. Hamamoto, A. Akisawa, and T. Kashiwagi,
“Experimental study of a low temperature heat driven re-heat two-stage adsorption
[46] F. H. Dubberke et al., “Experimental setup of a cascaded two-stage organic Rankine cycle,” Appl. Therm. Eng., vol. 131, pp. 958–964, Feb. 2018, doi:
10.1016/j.applthermaleng.2017.11.137.
[47] X. Wang, A. Christ, K. Regenauer-Lieb, K. Hooman, and H. T. Chua, “Low grade heat
driven multi-effect distillation technology,” Int. J. Heat Mass Transf., vol. 54, no. 25–26,
pp. 5497–5503, Dec. 2011, doi: 10.1016/j.ijheatmasstransfer.2011.07.041.
[48] G. F. Hewitt and S. J. Pugh, “Approximate design and costing methods for heat
[53] B. Elliston and M. Dennis, “Feasibility of Solar-Assisted Refrigerated Transport in
115
Australia,” 2009.
[54] D. Bergeron, “SANDIA REPORT Solar Powered Refrigeration for Transport Application-
A Feasibility Study,” 2001.
[55] J. L. S. A. Tassou, G. De-Lille, “Food Transport Refrigeration,” no. 853, pp. 1–25, 2014.
[56] G. F. Nemet, “Beyond the learning curve: factors influencing cost reductions in
photovoltaics,” Energy Policy, vol. 34, no. 17, pp. 3218–3232, Nov. 2006, doi:
10.1016/j.enpol.2005.06.020.
[57] C. F. Yu, W. G. J. H. M. Van Sark, and E. A. Alsema, “Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects,”
Renew. Sustain. Energy Rev., vol. 15, no. 1, pp. 324–337, Jan. 2011, doi:
10.1016/j.rser.2010.09.001.
[58] “Electric Power Monthly - U.S. Energy Information Administration (EIA).” [Online].
[63] F. Mol es, J. Navarro-Esbrí, B. Peris, A. an Mota-Babiloni, and K. Kontomaris, “Thermodynamic analysis of a combined organic Rankine cycle and vapor compression
cycle system activated with low temperature heat sources using low GWP fluids,” 2015,
doi: 10.1016/j.applthermaleng.2015.04.083.
[64] K. H. Kim and H. Perez-Blanco, “Performance analysis of a combined organic Rankine
cycle and vapor compression cycle for power and refrigeration cogeneration,” Appl. Therm. Eng., vol. 91, no. 140, pp. 964–974, 2015, doi:
10.1016/j.applthermaleng.2015.04.062.
[65] X. Bu, L. Wang, and H. Li, “Performance analysis and working fluid selection for
geothermal energy-powered organic Rankine-vapor compression air conditioning,” 2013.
116
[66] M. Tauseef Nasir, M. A. Ali, T. S. Khan, E. Al-Hajri, M. B. Kadri, and K. C. Kim,
“Performance assessment and multi objective optimization of an Organic Rankine Cycle
driven cooling air conditioning system,” Energy Build., vol. 191, pp. 13–30, 2019, doi:
10.1016/j.enbuild.2019.03.012.
[67] N. Toujeni, N. Bouaziz, and L. Kairaouani, “Energetic investigation of a new combined ORC-VCC system for cogeneration,” in Energy Procedia, 2017, vol. 139, pp. 670–675,
doi: 10.1016/j.egypro.2017.11.270.
[68] N. Javanshir, S. M. Seyed Mahmoudi, and M. A. Rosen, “Thermodynamic and
Exergoeconomic Analyses of a Novel Combined Cycle Comprised of Vapor-Compression Refrigeration and Organic Rankine Cycles,” Sustainability, vol. 11, no. 12, p. 3374, 2019,
doi: 10.3390/su11123374.
[69] B. Saleh, “Energy and exergy analysis of an integrated organic Rankine cycle-vapor
[70] B. Saleh, “Parametric and working fluid analysis of a combined organic Rankine-vapor
compression refrigeration system activated by low-grade thermal energy The effect of boiler temperature on the COP S for different candidates in the basic ORC-VCR system.
Production and hosting by Elsevier A R T I C L E I N F O,” J. Adv. Res., vol. 7, pp. 651–
660, 2016, doi: 10.1016/j.jare.2016.06.006.
[71] W. Zhi-qi, Z. Qi-yu, X. Xiao-xia, L. Bin, and Z. Xin, “Performance comparison and
analysis of a combined power and cooling system based on organic Rankine cycle,” J.
Cent. South Univ, vol. 24, pp. 353–359, 2017, doi: 10.1007/s11771-017-3437-5.
[72] AHRI, “AHRI Standard 560-2000 Standard for absorption water chilling and water
heating packages,” 2000.
[73] A. Standard, “Performance Rating of Commercial and Industrial Unitary Air-Conditioning
Condensing Units,” 2011.
[74] J. Bao, L. Zhang, C. Song, N. Zhang, X. Zhang, and G. He, “Comparative study of combined organic Rankine cycle and vapor compression cycle for refrigeration: Single
fluid or dual fluid?,” Sustain. Energy Technol. Assessments, vol. 37, Feb. 2020, doi:
10.1016/j.seta.2019.100595.
[75] B.-S. Park, M. Usman, M. Imran, and A. Pesyridis, “Review of Organic Rankine Cycle
experimental data trends,” 2018, doi: 10.1016/j.enconman.2018.07.097.
[76] S. Karellas and K. Braimakis, “Energy-exergy analysis and economic investigation of a cogeneration and trigeneration ORC-VCC hybrid system utilizing biomass fuel and solar
[82] S. F. Mussati, S. Cignitti, S. S. Mansouri, K. V. Gernaey, T. Morosuk, and M. C. Mussati,
“Configuration optimization of series flow double-effect water-lithium bromide
absorption refrigeration systems by cost minimization,” Energy Convers. Manag., vol.
158, pp. 359–372, Feb. 2018, doi: 10.1016/j.enconman.2017.12.079.
[83] S. Sanaye, M. Emadi, and A. Refahi, “Thermal and economic modeling and optimization
of a novel combined ejector refrigeration cycle,” Int. J. Refrig., vol. 98, pp. 480–493,
2019, doi: 10.1016/j.ijrefrig.2018.11.007.
[84] S. Quoilin, S. Declaye, B. F. Tchanche, and V. Lemort, “Thermo-economic optimization of waste heat recovery Organic Rankine Cycles,” Appl. Therm. Eng., vol. 31, no. 14–15,
pp. 2885–2893, 2011, doi: 10.1016/j.applthermaleng.2011.05.014.
[85] C. Zhang, C. Liu, S. Wang, X. Xu, and Q. Li, “Thermo-economic comparison of
subcritical organic Rankine cycle based on different heat exchanger configurations,”
Energy, vol. 123, pp. 728–741, Mar. 2017, doi: 10.1016/j.energy.2017.01.132.
[86] M. P. Bailey, “Chemical engineering plant cost index (cepci),” Chem. Eng., vol. 121, no.
2, pp. 68–69, Feb. 2014.
[87] “2019 Chemical Engineering Plant Cost Index Annual Average - Chemical Engineering |
[100] Y. Dai, J. Wang, and L. Gao, “Parametric optimization and comparative study of organic
Rankine cycle (ORC) for low grade waste heat recovery,” Energy Convers. Manag., vol.
50, no. 3, pp. 576–582, Mar. 2009, doi: 10.1016/j.enconman.2008.10.018.
[101] D. Wang, X. Ling, H. Peng, L. Liu, and L. L. Tao, “Efficiency and optimal performance
evaluation of organic Rankine cycle for low grade waste heat power generation,” Energy,
vol. 50, no. 1, pp. 343–352, Feb. 2013, doi: 10.1016/j.energy.2012.11.010.
Vita
Shahzaib B. Abbasi
Education
Ph.D., Mechanical Engineering, The Pennsylvania State University December 2020
M.S., Pakistan Institute of Engineering and Applied Sciences November 2013 B.E., N.E.D University of Engineering and Technology April 2010
Work Experience
Graduate Researcher – University of Twente, Enschede, The Netherlands Feb. 2020 – Present Doctoral Researcher – The Pennsylvania State University, State College, PA Jan. 2017 – Dec. 2020
Graduate Researcher – Mississippi State University, Starkville, MS Aug. 2015 – Dec. 2016
Junior Engineer – National Center for Non-destructive Testing, Islamabad, Pakistan Nov. 2013 – Jun. 2015 Fellow – P.I.E.A.S, Islamabad, Pakistan Nov. 2011 – Nov. 2013