ABSTRACT
The Increase use of fossil fuels has resulted in higher greenhouse gas emissions and the rise in global warming. This study applied the post-combustion process to capture CO2 from engines powered on diesel, petrol and blended biodiesel. Exhaust emissions were measured using a gas analyzer model called IMR 1000-4 for the various engines. The engines include: diesel trucks, petrol light commercial buses, passenger cars, and petrol-biodiesel car. An amine-based adsorbent (MonoethanolAmine) through Temperature Swing Adsorption (TSA) was used to capture CO2 from the exhaust gases. The CO2 capture process was modeled using Aspen HYSYS V8.8. A source code was developed using Engineering Equation Solver (EES) to estimate the mass balance, energy, and exergy analysis of the system. The results shows that the average CO2 emission was highest for heavy-duty trucks, accounting for 17.53 volume percent followed by 15.1 volume percent for light commercial vehicle models from 1980 to 1999. In contrast, heavy commercial vehicle models from 2000 to 2014 exhibited the lowest CO2 emissions, ranging from 9.8 volume percent to 9.6 volume percent. The system achieved a CO2 capture rate of 1.9 kg per liter of fuel consumed by an internal combustion engine, with CO2 capture rates of 92%, 85%, and 75% observed for diesel trucks, petrol engines, and the biodiesel, respectively. The energetic efficiency (EE) and exergetic efficiency (ExE) ranged between 58.30% and 64.14%, and 47% and 53%, respectively. The exergy destruction (ED) gap between biodiesel and diesel was approximately 45%, while petrol exhibited a gap of about 32.22%. A sensitivity analysis using an Artificial Neural network (ANN) was used to determine the response and the factors influencing the level of CO2 captured from the exhaust fuels. The results indicated the importance of independent variables: MEAmine (0.96), temperature (0.84), mass flow (0.77), and pressure (0.38). The performance of the model was evaluated using the Relative Error (RE); the model achieved a minimal RE of 0.047 (4.7%). Furthermore, the predictability coefficient (R2) yielded a value of 0.958, indicating excellent model performance. In conclusion, CO2 can be captured from greenhouse gas emissions and as such could help to reduce the effect of climatic change in developing countries such as Nigeria.
TABLE
OF CONTENTS
Cover page i
Title page ii
Declaration iii
Dedication iv
Certification v
Acknowledgements vi
Table of Contents vii
List of Tables x
List of Figures xi
Nomenclature xiii
Abstract xv
CHAPTER 1: INTRODUCTION
1.1 Background of Study 1
1.2 Statement of Problem 3
1.3 Aim and Objectives 3
1.4 Scope of Study 4
1.5 Justification of the Study 4
CHAPTER 2: LITERATURE REVIEW
2.1 Climate Change and Greenhouse Gas Emission 5
2.2 Co2 Capture Technologies 6
2.2.1 Post-combustion capture for power plants 6
Membrane-based separation 9
Chemical absorption 11
2.3 Absorbent Selection for CO2
Capture from Exhaust Gases 14
2.4 Temperature
Swing Adsorption (TSA) Methodology 17
2.4.1 Vehicular emission carbon capture and TSA
application for internal
combustion engines 19
2.5 Biodiesel Production from Waste Cooking
Oil 20
2.6 Thermodynamic (Energy and Exergy)
Assessment of Carbon Capture 21
2.7 Knowledge Gap 23
CHAPTER 3: MATERIALS
AND METHODS
3.1 Materials
25
3.2 Methods 25
3.2.1 Data
collection 25
3.2.2 Experimental
biodiesel production, separation and purification 26
3.2.3 Equipment setup and exhaust gas measurement 27
3.2.4 TSA system
description for CO2 capture 28
3.2.5 Thermodynamic assessment of the CO2
capture system 30
Energy analysis of system 30
Exergy analysis 35
3.2.6 Optimization
of the system's energy/heat transfer rate using computational
fluid dynamics (CFD) simulation 38
CHAPTER 4: RESULT AND
DISCUSSION
4.1 Exhaust Emission Output 43
4.2 TSA Carbon Capture from Fuels 46
4.3 Thermodynamic Analysis Results 46
4.3.1 Thermodynamic state point pproperties 47
4.3.2 Energetic and exergetic performance of the system 48
4.3.3 Heat input impact on exergetic efficiency (ExE) and energy
efficiency (EE) 49
4.3.4 Impact of heat input on the exergy destruction (ED) 50
4.4 CFD Heat Exchanger Simulation Result 52
4.4.1 Pressure distribution in the heat exchanger 52
4.4.2 Velocity distribution in heat exchange 53
4.4.3 Temperature
distribution in heat exchanger 54
4.5 Sensitivity Analysis and Result Validation 55
4.5.1 Potential CO2 variable
contribution using machine learning
(Artificial neutral network) model 56
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion 59
5.1.1 Contributions
to knowledge 60
5.2 Recommendations 60
REFERENCES 61
APPENDIX 72
LIST OF TABLES
3.1 Categories
and details of the vehicle data sampled 26
3.2 Models for exergy balance, exergy of
product, exergy of fuel and
exergy efficiency. 37
3.3 Boundary Conditions 41
3.4 Properties of Stainless steel 41
3.5 Mesh Statistics 42
4.1 Material
and composition stream simulation result for CO2 capture system 46
4.2 State point results of thermodynamic
parameters 48
4.3 Result of performance parameter of the system 49
4.4 Comparison of the fluid temperature prediction at the outlets 55
LIST OF FIGURES
2.1 Schematic
representation of a power plant that captures CO2 after combustion. 7
2.2 Shows a process diagram for membrane
separation. 11
2.3 Schematic of a basic chemical absorption
mechanism for CO2 capture system 12
3.1 The
block diagram showing the flow process for the CO2 capture system. 29
3.2 The
block diagram showing the flow process for the CO2 capture
modelling
using Aspen hysys system. 29
3.3 Combusion
of fuel 31
3.4 Heat Exchanger Model Geometry 39
3.5 Model Mesh 42
4.1a Average
CO emission value of vehicle 45
4.1b Average
NOx emission value of vehicle 45
4.1c Average
CO2 emission value of vehicle 45
4.1d Average
O2 emission value of vehicle 45
4.2 Variation of exergy and energy efficiency
on heat input 50
4.3 Variation of heat input on exergy
destruction for diesel fuel 51
4.4 Variation of heat input on exergy
destruction for biodiesel and petrol fuel 51
4.5 Pressure magnitude and distribution plot: (a) Single channel,
parallel flow
(b) Multi-channel, parallel flow (c) Single channel,
counter flow
(d) multi-channel, counter flow 52
4.6 Velocity magnitude and distribution plot
(a) Single channel, parallel flow
(b) Multi- channel, parallel flow
(c) Single channel, counter flow
(d) Multi-channel, counter flow 53
4.7 Temperature magnitude and distribution
plot (a) Single channel, parallel flow
(b) Multi-channel, parallel flow (c) Single channel, counter flow
(d) Multi-channel, counter flow 54
4.8 Graphical
representation of the temperature drop along the flow path of the
refrigerant
system. 55
4.9 Network Architecture for the CO2 prediction model 57
4.10 Variation of predicted value with CO2 composition mole fraction 57
4.11 Predicted
normalized contribution importance for CO2 composition
mole fraction 58
NOMENCLATURE
E Exergy
(
)
Enthalpy
of formation (
)
Control
volume
m Mass
(
)
No of mols (mol)
Heat in control volume (
)
Compression
ratio
s Entropy
(
K)
T Temprerature
(
)
Work in control volume (
)
ABS Absorber
AC Air compressor
CCS Carbon capture and
storage
CND Condenser
EES Engineering
equation solver
EOR Enhance oil recovery
EU European union
FFA Free fatty acid
GHG Greenhouse gas
GT Gas turbine
HE Heat Exchanger
ICE
Internal
combustion engine
IGCC Integrated
gasification combined cycle
MEA Monoethanolamine
ORC Organic rankine
cycle
PCC Post-combustion CO2
capture
PSA Pressure swing adsorption
TSA Temperature swing
adsorption
VSA Vacuum swing
adsorption
WFG Waste fryer grease
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
OF STUDY
The
industrial revolution paved the way for modern manufacturing, transportation,
and fast economic expansion. This development helped fuel the rising energy
consumption need (Kumar et al., 2021).
2030 global energy consumption will increase by 50% (Rajendra et al., 2014).
The bulk of energy produced is fueled mainly through burning fossil fuels,
which releases pollutants like greenhouse gases (GHG) into the atmosphere. The Climate
is changing dramatically, resulting in some menaces such as draughts, floods
and hunger (Albuquerque et al., 2020).
One of the most harmful pollutants that enter the atmosphere is released while
burning fossil fuels, in which the Carbon dioxide (CO2) constitute
the chief GHG (Butt et al., 2012).
Since 1751, human activities through through industrial processes have caused
the atmosphere to lose around 1.5 trillion tons of CO2.
Transport-related CO2 emissions accounted for 24% of all
fuel-related emissions globally and expected to increase by 60% by 2050.
The
transport sector is vital to a country's economy, and the number of vehicles on
the road has increased over the past century. Approximately 10% of the world's
car owners live in developing nations, and just over 20% of the world's
transportation energy is consumed in these nations (Nepal, 2015).
The transportation industry, which accounts for about 25% of the CO2
emissions caused by human activity, meets the world's oil demand. About 75% of
the direct CO2 emissions in the sector are caused by motor vehicle
emissions (Kodjak, 2015).
Urban areas are under environmental stress due to the steady increase in
vehicles, which is notably responsible for poor air quality. Disorganized road
systems, inefficient cars, tampered gasoline, and traffic congestion contributes
to mobile source pollution (Assamoi et al., 2010).
The effects of traffic congestion, movement rate, maintenance condition, and vehicle
life duration are also used to determine the level of automotive pollution (Nasir et al., 2016).
The story of emission concentration has explicitly increased because of poor
vehicle maintenance culture and the immigration of old vehicles. These
activities result in an automotive fleet dominated by "super
emitters" vehicles with high emissions of dangerous pollutants. These
worrying circumstances are caused by developing nations' weak economic
conditions. However, due to the vast effect of climate change caused by the GHGs,
various studies have been channelled to cob these emissions from the
transportation sector. Some studies have recommended using electric cars, but
this technology faces several drawbacks. Some of the challenges of using
electric vehicles include the cost of the batteries, high energy density or
weight and the need for infrastructures to charge them (Kurien et al., 2020; Sharma and Maréchal,
2019).
One of the most promising technologies is Post-combustion CO2
Capture (PCC).
The
post-combustion CO2 capture technique (PCC) offers simplicity of
deployment in the present system. This method requires more applications in vehicular
sources to harness CO2 from gaseous emissions, especially in
developing nations like Nigeria. Based on the European Automobile Manufacturers
Association, the European Union manufactured 2.7 million commercial automobiles
in 2016 (European Commission, 2019).
This figure demonstrates the great opportunities for CO2 capture
technologies for vehicles. However, some challenges, such as the mobility nature
of cars, the interrupted emissions, and the lack of available space for CO2
storage, could be hindrances to optimum CO2 capturing from vehicle
sources. Hence, there is a need for critical understanding and assessment of CO2
capture for vehicles. The European Environmental Agency estimates that the road
transportation industry produced roughly 0.746 giga tonnes of Carbon dioxide
emission in 2015, dominated mainly by diesel, petrol and biodiesel vehicles (Sharma and Maréchal, 2019).
Although some studies have established that using biodiesel increases CO2
emissions., most studies contend that this does not affect global warming since
plants utilize the CO2 produced by this process in their
photosynthetic process (Agarwal and Das, 2001; Alleman et al., 2016;
Çelebi and Aydın, 2018; Körbitz, 1999; ÖRS and BAKIRCIOĞLU, 2016; SUGÖZÜ et
al., 2010; Yesilyurt, 2019).
1.2 STATEMENT
OF PROBLEM
The
practices of fuel adulteration, poor road conditions, and importation of
substandard fuels and vehicles contribute to increased atmospheric emissions.
These problems are faced particularly in developing countries like Nigeria's
transport sector. Most sources of these menaces include vehicle engines fueled
by diesel, petrol and some potential biodiesels. The transportation sector's
high emissions levels necessitate further studies to mitigate them. However,
these circumstances present significant potential for CO2 capture to
cob the emission problem through absorbent compounds. Therefore, there is a need
to harness some promising methods to capture CO2 from these sources,
which will thus constitute this research's novelty.
1.3 AIM
AND OBJECTIVES
The
aim of this study is to apply post-combustion process analysis of fuels for CO2
capture from tail pipe emission of vehicle engines.
Specific
objectives are:
1. To
measure and compare the post-combustion of fuels from tail pipe exhaust engines.
2. To
apply Temperature Swing Adsorption (TSA) to capture CO2.
3. To evaluate the thermodynamics performance of the
system - Energetic and Exergetic analysis of the CO2 capture system.
4. To
optimize the system's energy/heat transfer rate using Computational fluid
dynamics (CFD) simulation.
1.4 SCOPE
OF STUDY
The
scope of the study is limited to:
1. Post-combustion processes involved in CO2
capture from tail pipe emission of exhaust engines mainly from petrol, diesel,
and their blends to form bio-diesel.
2. The
study only extends to the ORC system to exhaust waste and heat recovery system.
3. The
study also examines the pressure, temperature and velocity gradient across
different heat exchanger configurations, precisely parallel and counter flow
types.
1.5 JUSTIFICATION
OF THE STUDY
Carbon
capture and storage (CCS) is the most emphasized technology to decrease CO2
emissions from fuel sources to the atmosphere. Also, CO2 separated
from flue gases can enhance oil recovery (EOR) operations, where CO2
is injected into oil reservoirs to increase the mobility of oil and reservoir
recovery. Based
on economical and environmental considerations, applying efficient and suitable
technology to capture CO2 is necessary. Pure CO2 has many
applications in food/beverage and chemical industries, such as urea and
fertilizer production, foam blowing, carbonation of beverages and dry ice
production, or even in the supercritical state as a supercritical solvent.
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