Post on 11-Jun-2020
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Parameter setting on catalytic controller
PAPER WITHIN Product Development and Materials engineering
AUTHOR: Dharani Shanmugavel & Unnikrishnan Asan Janardhanan Pillai
TUTOR: Lars Walfridsson & Lennart Mähler
JÖNKÖPING June 2017
Using Design of Experiments and Scanning Electron Microscope Analysis
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This exam work has been carried out at the School of Engineering in Jönköping in the subject area Product development and Materials engineering. The work is a part of the Master of Science program. The authors take full responsibility for opinions, conclusions and findings presented. Examiner: Peter Hansbo Supervisors: Lars Walfridsson, Husqvarna Group Lennart Mähler, Jönköping University Scope: 30 credits Date:
Abstract
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Abstract
This thesis work has been conducted in the Handheld Laboratory at Husqvarna AB with the purpose of finding the parameters responsible for the performance of the catalytic converters used in the test rig. The catalytic converters are used in the test rig during the long term testing of the chain saws to reduce the hydrocarbon content from the exhaust before it enters into the
environment.
To perform this research two approaches were carried out. One with Design of Experiment (DOE) and another using Scanning Electron Microscope (SEM) analysis. In Design of Experiments parameters that are suspected to be influencing the performance of the catalytic converter were refined. Using these parameters a test plan is made with the help of statistical analysis application Minitab and the tests were carried out in the test rig. Using SEM the effects of aging and its effect on microstructure and chemical composition on the catalyst surface was analyzed.
The results from the DoE shows that the exhaust flow, collector diameter and distance to the muffler are responsible for the collection of exhaust. Distance to the muffler and collector length are the factors affecting the conversion of the exhaust. In addition to that exhaust flow is also responsible for the duration of heating coil running time.
The results from the SEM analysis shows that the operating temperature is high due to which there is thermal degradation of catalyst and there is also deactivation due to fouling. Another finding is that the flow on to the catalyst is not uniformly distributed which is leading to the reduction in efficiency of catalyst and accelerated aging of catalyst.
Keywords
Catalytic converters, Two stroke engines, Design of Experiments, Exhaust emission control, SEM, catalytic deactivation, Minitab
Acknowledgement
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Acknowledgement
The authors of this thesis would like to take the opportunity to acknowledge the people that
with their help and support made this thesis project possible
Lars Walfridsson
Test Method Engineer
For being our thesis supervisor who guided us through this
entire thesis work with his constant support, suggestions
and motivation
Lennart Mähler
Senior Lecturer
Mechanical Engineering
For being our supervisor who has been encouraging and
positive with our work
Staffan Ek
Laboratory Engineer
Who has been a great support from the starting of the thesis
up till the end and supporting us whenever we needed help
during the test.
Uno Sjölander
Laboratory Technician
For being very supportive during our DoE test in the rig and
also with the sample preparation during SEM analysis
Lennart Waden
System Development,
Measurement Technology
Who helped us with the Measurement system
Hans Åke Sundberg
Senior Technical Expert
&
Albin Hagberg
Mechanical Design Engineer
We are thankful to these engineers who have helped us in
understanding the concept of DoE and the Minitab
application
Kaj Torbjörner
Material Laboratory
For his assistant while performing the SEM analysis on the
catalytic converter
Jimmy Ek
Chain Department
For his instructions on how to assemble and disassemble
the product and use them in the test rig
Henrik C Henningsson
Chainsaw Department
For his guidance in tuning the product while performing the
DoE test
Kenth Malmqvist
Service Technician
For being a guidance during our DoE measurement in the
test rig
We would like to express our gratitude to the Research & Development team of the Handheld
Products, Husqvarna AB for giving us this opportunity to do our master thesis.
Contents
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Contents
1. Introduction ........................................................................... 8
1.1 BACKGROUND ......................................................................................................................... 8
1.2 PURPOSE AND RESEARCH QUESTIONS....................................................................................... 8
1.3 DELIMITATIONS ....................................................................................................................... 8
1.4 OUTLINE .................................................................................................................................. 8
2. Theoretical background ...................................................... 10
2.1 TWO STROKE ENGINE: ........................................................................................................... 10
2.1.1 Working principle: .................................................................................................... 10
2.1.2 Emission and causes for it: ........................................................................................ 11
2.2 CATALYTIC CONVERTER: ...................................................................................................... 11
2.2.1 Construction: ................................................................................................................. 12
2.2.3 Efficiency: .................................................................................................................. 13
2.2.4 Catalyst Deactivation: .................................................................................................. 14
2.3 STATISTICAL ANALYSIS OF DATA: .......................................................................................... 16
2.3.1 Estimation of the error in the measured quantities: ............................................... 16
2.3.2 Moving/Floating average: ........................................................................................ 17
2.4 DESIGN OF EXPERIMENTS (DOE): .......................................................................................... 18
2.4.1 DOE Types: ................................................................................................................... 18
2.4.2 Stages of DOE:........................................................................................................... 19
2.4.3 Screening Experiment: ............................................................................................. 19
2.4.4 Fractional Factorial Design: ..................................................................................... 24
2.4.5 Design Resolution: ................................................................................................... 28
2.5 PARETO CHART: .................................................................................................................... 29
2.6 MINITAB: ............................................................................................................................... 29
3. Method and implementation .............................................. 31
3.1 EXPERIMENTAL APPROACH: .................................................................................................. 31
3.2 MEASUREMENT RELATIONS: ................................................................................................. 32
3.2.1 Degree of Collection: ................................................................................................. 32
3.2.2 Conversion Level: ...................................................................................................... 32
3.2.3 Heating Coil ON/OFF: .............................................................................................. 32
3.3 TEST SETUP ............................................................................................................................ 33
Contents
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3.3.1 Test Rig: ..................................................................................................................... 33
3.3.2 Measurement Instrument (FID): ............................................................................. 36
3.3.3 Flow in the general ventilation: ............................................................................... 36
3.3.4 Optimal position of the collector: ............................................................................ 37
3.4 INITIAL TEST RUNS: ............................................................................................................... 37
3.4.1 Determining the duration of experiment for data collection: ............................... 38
3.4.2 Initial observations: .................................................................................................. 39
3.5 DOE TEST ............................................................................................................................. 40
3.5.1 Factors which has to be considered for DOE: ........................................................ 40
3.5.2 DOE Design: .............................................................................................................. 43
3.6 SEM ANALYSIS: .................................................................................................................... 44
3.6.1 Sample preparation: ................................................................................................. 44
3.6.2 Tests Performed: ....................................................................................................... 45
4. Findings and analysis ........................................................... 46
4.1 DOE TEST RESULTS: ............................................................................................................. 46
4.1.1 Response for Degree of Collection: .......................................................................... 46
4.1.2 Conversion level: ...................................................................................................... 48
4.1.3 Coil ON/OFF: ............................................................................................................50
4.2 RESULTS FROM SEM ANALYSIS: ............................................................................................ 52
4.2.1 Chemical composition: .................................................................................................. 52
4.2.2 Microstructure analysis on wash coat: .................................................................... 54
5. Discussion and conclusions ................................................. 56
5.1 DISCUSSION OF METHOD ........................................................................................................ 56
5.2 DISCUSSION OF FINDINGS ....................................................................................................... 57
5.2.1 DOE test results ........................................................................................................ 57
5.2.2 SEM test results: ....................................................................................................... 57
5.3 CONCLUSIONS ........................................................................................................................ 58
5.4 FUTURE WORK ....................................................................................................................... 59
6. References ............................................................................ 60
7. Appendices ........................................................................... 62
7.1 APPENDIX 1: ............................................................................................................................ 2
7.2 APPENDIX 2: ............................................................................................................................ 3
7.3 APPENDIX 3: ............................................................................................................................ 4
7.4 APPENDIX 4: ............................................................................................................................ 6
7.5 APPENDIX 5: .......................................................................................................................... 10
Contents
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7.6 APPENDIX 6: .......................................................................................................................... 11
Introduction
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1. Introduction
The master’s thesis investigates the role of various parameters affecting the performance of
catalytic converters which are used in the test rigs at the testing facility for hand held 2-
stroke petrol engine devices. The thesis work is a part of the development of new test cells
at the facility.
1.1 Background
Within Husqvarna’s development for handheld petrol products, testing and verification
were conducted to determine the strength, performance and drivability in a long-term runs.
Products such as chainsaws and brush cutters, mainly driven by two-stroke gasoline
engines, tested for longer time in their respective test rigs.
To avoid large emissions of two-stroke exhaust, products exhaust is purified by the catalyst
system before they’re released into the environment. As there are around 52 test rigs
running for long hours, the emission during these test cycles have a huge impact on the
environment. By setting the suitable parameters to the catalysts in different test rigs
according to the engine capacity we can improve the catalyst conversion thereby reducing
the HC content released into the environment.
By means of Design of Experiments (DOE) the parameters related to the efficiency of the
catalytic converter and their effect of interaction towards the catalyst performance is tested.
The causes for the deactivation of catalyst in the test rigs are also studied by analyzing the
wash coat using SEM.
1.2 Purpose and research questions
The purpose of this thesis work is to find the factors that affect the performance of the
catalytic converter used in the test rig in Husqvarna AB. To understand the process an
initial screening experiment is conducted with the help of design of experiments and SEM
analysis on the catalyst to study on the reasons for the catalyst deactivation.
What are the parameters affecting the collection and conversion rate of a catalytic
converter in 2 stroke handheld equipment in the test rig?
How are these parameters influencing the catalyst performance in the test rig?
1.3 Delimitations
The entire thesis work is conducted in the Research and Testing Department in Husqvarna
AB. The parameters responsible for the performance of the catalytic converter found during
this thesis for the Design of experiments is respective to the test rig used in Husqvarna AB.
The aim of the thesis is not to optimize the test rig, it is performed to identify the major
factors responsible for the performance. Due to time limitations the experiment is
conducted on a specific product (H560 XP). The DOE test plan wasn’t repeated due to the
maintenance of FID. The SEM analysis is done on catalysts which are run under varying
situations in the test rig instead of using catalyst which are run under controlled situation.
1.4 Outline
This thesis work is divided into eight chapters
Introduction
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1. In chapter one, the background of this work, purpose and research questions and
delimitations of the thesis work were discussed.
2. In chapter two the theories related to this work like Two-stroke engine, catalytic
converter, DoE (Design of Experiments) were presented.
3. In chapter three “ Method and Implementation “ How the DoE is planned and
performed as well as the SEM analysis were discussed
4. In chapter four the results from the DoE and SEM analysis are presented
5. In chapter five “Discussions and Conclusions” the results from the DoE and SEM
from the chapter four are discussed and further work that can be done were
mentioned
6. In this chapter there references used in this work are shown.
7. Appendices chapter includes the Matlab codes, Test Data(Plan, Result, SEM result)
Theoretical background
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2. Theoretical background
In this chapter, a detailed theoretical explanation about the product, test rig and the
measurement method is described.
2.1 Two stroke Engine
2.1.1 Working principle
The various operations performed inside the two stroke engine are illustrated in the
Figure 1.Unlike four stroke engines the two stroke engine has no valves but they have inlet
and exhaust port. In Figure 1 : Two stroke engine working cyclesFigure 1(a) the air/fuel
mixture above the piston gets compressed and ignited by the spark plug resulting in rapid
rise in pressure and temperature which will push the piston down. In Figure 1(b) the
exhaust port opens as the piston moves down which leads to release of high pressure hot
exhaust gas from the combustion process. In Figure 1(c) the transfer port opens which
connects the cylinder with the crankcase via transfer duct and fresh charge enters the
cylinder from the crankcase if the pressure in the crankcase is greater than the pressure in
the cylinder. In Figure 1(d) after the completion of scavenging process now the cylinder is
filled with fresh air/fuel mixture and it gets compressed as the piston rises. (Blair, 1996)
Figure 1 : Two stroke engine working cycles (Blair, 1996)
The handheld device being tested in the rig are run by two stroke engine. Due to the design
simplicity, weight, multi-directional operation without flooding, less number of moving
parts, power and torque characteristics with easy maintenance these type of engines are
most preferred for handheld applications. Another important feature of this engine is that
it can produce 1.4 times more power as a four stroke engine. But with all the above
Theoretical background
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advantages it has some disadvantages as well like the poor utilization of fuel during the
scavenging process, high hydrocarbon and particulate matter emission rates. (Emission
Control of SmallSpark-Ignited Off-Road Engines and Equipment, January 2009)
2.1.2 Emission and causes for it
In a two stroke engine HCs, CO and particulate matter in the form of white smoke are the
primary emissions whereas NOx emissions are considered less significant. Two stroke
gasoline engine has hydrocarbon emission rate approximately 6 times higher than that of a
four stroke engine.
A two stroke engine relies on the pressurized flow of the compressed intake charge to force
combustion products out of the cylinder. Because intake and exhaust gases are entering and
leaving the cylinder simultaneously, this results in a portion of the intake escaping through
the exhaust port without being combusted. The simplest two-stroke engine rely on
carburetor air/fuel intake charge and therefore 15 to 40 % of the escaping charge is
unburned fuel. These so called scavenging emissions result in high emissions of HC and
increased consumption of fuel compared to four stroke engines. (Emission Control of
SmallSpark-Ignited Off-Road Engines and Equipment, January 2009)
2.2 Catalytic Converter
The catalytic converter is a device which uses the technology of catalyst to enhance the
reaction of harmful gasses in the engine’s exhaust to harmless gasses. The catalyst itself will
not be a part or get consumed during the chemical reaction. When installed into an exhaust
system the reaction of the HC and CO in the exhaust with oxygen to form the carbon dioxide
and water is promoted. Another reduction happening by means of a catalytic converter is
the reduction of NOx to nitrogen. The chemical reactions happening in the catalytic
converter are shown below,
1. Reduction of NOx to nitrogen and oxygen.
𝑁𝑂𝑥 → 𝑁𝑥 + 𝑂𝑥
2. Oxidation of CO to carbon dioxide
𝐶𝑂 + 𝑂2 → 𝐶𝑂2
3. Oxidation of HC to carbon dioxide and hydrogen.
𝐶𝑥𝐻4𝑥 + 2𝑥𝐶𝑂2 → 𝑥𝐶𝑂2 + 2𝑥𝐻2
For a reaction to occur, a particular energy barrier has to be crossed, this energy barrier is called activation energy (Ea). When is catalytic converter is introduced into the system the activation
energy is reduced which can be seen in the graph shown in Figure 2. The graph in the left side
is the situation without catalyst and the graph in the right is the situation with catalyst. It can be observed from the graph that the activation energy needed to start the reaction is less while using a catalyst compared to the situation without a catalyst. (Avneet Kahlon, 2015)
Theoretical background
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Figure 2 : Activation of chemical reaction without catalyst (left) and with catalyst (right). (Avneet Kahlon, 2015)
Mainly two types of technologies of catalysts are used in the industry now a days to treat exhaust from spark ignition engines which are two-way and three-way catalyst technology. In two way
catalyst technology only HC and CO are oxidized which can be seen in Figure 3, while in three
way catalyst technology NOx is also reduced. In two way catalysts platinum or palladium are used to increase the oxidation of the unburnt HC and CO. Three way catalysts add up a third precious metal, rhodium for the reduction of NOx.
Figure 3 : Reaction in two way catalytic converter (Emission Control of SmallSpark-Ignited Off-Road Engines and Equipment, January 2009)
2.2.1 Construction
Catalysts are generally constructed with a thin layer of precious metal over a composite inorganic materials which are mainly oxides which is applied to a surface of chemically inactive metallic or ceramic support which are called substrate. The thin layer of inorganic materials on the catalytic converter is called wash coat. Alumina is an example of wash coat component in two way catalyst. The substrate determines the reaction area as the thin catalytic layer is applied over it. For catalytic converts installed in small engines the design varies from just a wire mesh or screens to a more complex honey comb design. The reaction happens when the exhaust flows through the open channels in the substrate where the catalytic layer is present. (Emission Control of SmallSpark-Ignited Off-Road Engines and Equipment, January 2009)
Theoretical background
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Figure 4 : Examples of catalysts used in small engine products. (Emission Control of SmallSpark-Ignited Off-Road Engines and Equipment, January 2009)
2.2.3 Efficiency
The efficiency of the catalytic converter depends on various factors like substrate form, substrate cell size, catalyst formulation, operating temperature environment and exhaust gas composition.
2.2.3.1 Effect of oxygen
From the chemical reactions happening in the catalytic converter it can be seen that oxygen is the major contributor for the conversion of HC and CO. So if the volume of the oxygen in the exhaust is low, it will reduce the efficiency of the catalyst. Assuming the HC to be propylene in the exhaust, the volume of oxygen needed for the complete oxidation of HC is 3.4 g/km per gram of HC and 0.6 g/km per g of CO. In some systems a secondary pump will be installed to obtain ample oxygen in the exhaust. But for a more efficient reduction of NOx the volume of oxygen should be less. (McCartney, 2003)
2.2.3.2 Effects of oil on the catalyst
Oil used in the engine, fuel etc. contribute a lot to the aging of the catalytic convertor. Lube oils in the engine can enter into the exhaust system by leaking through worn out piston rings, faulty valve seals, failed gaskets and/or warped engine components which leads to fouling of the catalytic converter for which the chemical composition in the oil is the major factor. The compounds yielding P, S, K, Ca &Zn originated from oil during the engine operation poison the catalyst by surface reaction, thereby reducing or eliminating portions of the catalyst. P-free synthetic engine oil and commercial synthetic oil has almost similar wear characteristics on the catalyst. Phosphorus alone will lead to a glassy surface on the catalyst which contributes to the catalyst temperature increase. Zinc from lubricative additives like Zinc thiophosphate (ZDDP), or zinc and phosphorus from ZDDP, deposits on the catalyst surface which yields a surface inactive to gas phase reactions. Usually phosphorus gets accumulated more easily than zinc and the volume of phosphorus will be larger. Sulphur also creates a poisoning layer over the catalyst, but if the temperature of the exhaust was to reach an excess of 5000 C the sulphur will burn off. (Hakan Kaleli, 2001)
Theoretical background
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2.2.3.3 Catalytic converts in 2 stroke engines
It’s really challenging to install a catalytic converter into the exhaust system for a two stroke engine system. Due to the low volume of NOx in the emission from 2-Stroke engines two way catalysts are mainly installed so to reduce the volume of HC and CO and also to reduce white smoke or particular matter (PM). The estimated rate of conversion efficiencies of a 2 way catalytic converter are in the order of 50%-80% for HC, 50%-75% for CO and 45%-70% for PM.
2.2.4 Catalyst Deactivation
Catalyst deactivation plays a major role in the determination of degree of collection and degree of conversion. It is the loss of catalytic activity and/or selectivity over time (Bartholomew, 2001). It contributes a lot to the cost of operation in the form of replacing of catalyst and shutdown of process. Optimization of the process and designing a stable catalyst can slow down or sometimes even prevent the process of catalyst deactivation. The catalyst deactivation can
occur due to three main reasons: chemical, mechanical and thermal. The Table 1 below shows
the classification of catalyst deactivation mechanism.
Table 1 : Catalyst Deactivation Mechanism (Hussain, 2014)
Type Mechanism Description
Chemical Poisoning Blocking of the reaction active site on the catalyst surface due to strong chemisorption
Vapor formation Production of volatile compounds due to the reaction between catalytic phase and the gas
Reactions: Vapor-solid and solid-solid
Formation of inactive phase as a result of reactions between vapor, support or promoter with catalytic site
Mechanical Fouling Physical deposition of species from gas or fluid into the catalyst surface or pores
Attrition/crushing Depletion of catalytic material or internal surface area due to abrasion or mechanically-induced crushing
Thermal Thermal degradation Loss of catalytic surface, support surface area and active phase-support reactions due to thermal degradation
Theoretical background
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2.2.4.1 Poisoning
Poisoning is the strong chemisorption of species on catalytic sites, thereby blocking sites of catalytic reaction making the catalyst less active. The operational meaning of poisoning is whether a species act as a poison depending on its adsorption strength relative to the other species competing for the catalytic site (Bartholomew, 2001). In automotive catalysts the poisoning contaminates the support material and precious material by blocking the active sites (Hussain, 2014). It will also physically block the adsorption site and induce changes in the electronic or geometric structure of the catalyst surface (Bartholomew, 2001).
The impurities which are more common in a vehicle-aged catalyst are S, P, Zn, Ca and Mg. S and P are the principal poisons and for phosphor the main source is the lubrication oil. Iron (Fe) is responsible for poisoning in platinum (Pt) group metals and they are assumed to be derived from the corrosion and wear of the engine components. Other impurities include chromium (Cr), Nickel (Ni) and copper (Cu) (Hussain, 2014).
2.2.4.2 Vapor-solid or solid-solid reaction
The catalyst deactivation can happen due to several other chemical reactions other than poisoning which are,
•Reaction of the vapor phase with the catalyst surface.
•Catalyst solid-support or catalytic solid-promotor reactions.
•Solid-state transformations of catalytic phases during reactions.
2.2.4.3 Fouling, coking or carbon deposition
Fouling is the physical deposition of species from fluid phase on the catalyst surface which results in inhibiting the reaction by blocking of sites or pores in the catalyst. In its advance stages, fouling leads to the disintegration of catalyst and plugging of the voids. In most cases fouling caused by the deposition of carbon and coke in porous catalyst. Carbon is a product of CO disproportionation while coke is a product of decomposition of hydrocarbons on catalyst surfaces. The rate of deactivation greatly depends on the temperature and the reactant composition. Fouling can also lead to the reduction in air flow which will increase the back pressure from the exhaust system which will lead to the reduction of degree of collection. (Bartholomew, 2001)
Figure 5 : Effect of fouling caused by coke/carbon on catalyst. (Bartholomew, 2001)
2.2.4.4 Attrition or crushing of catalysts
Various forms of mechanical failures formed in catalytic converters are
•Crushing of granular, pellet or monolithic catalyst forms due to a load.
Theoretical background
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•Size reduction and/or breakup of catalyst granules or pellets.
•Erosion of catalyst particles or monolith coating due to high fluid velocities.
The two principle mechanisms involved in the mechanical failures are fracturing of agglomerates into smaller agglomerates and erosion of aggregates of primary particles. Mechanical failure can be observed under an optical or electron microscope. Where we can observe the loss of wash coat from the wall of the honeycomb channel.
2.2.4.5 Thermal degradation and sintering
Thermal degradation occurs for three major reasons (Bartholomew, 2001),
•Loss of catalytic surface area due to crystallite growth of the catalytic phase.
•Loss of support area due to collapse of support and catalytic surface.
•Chemical transformation of catalytic phases to non-catalytic phases.
The first two reasons are referred to as “sintering”. Sintering is a result of high reaction temperature (500⁰ C) and accelerated in the presence of water vapor (Bartholomew, 2001). The temperature can go up when there is considerable volume of fuel in the exhaust gas. This effect will result in reduction of surface area of both the support material and precious metal (Hussain, 2014)
2.3 Statistical analysis of data
Statistics can be used as a mathematical tool for the qualitative analysis of experimental data. During experiments the data will be gathered for the same setup multiple times which are replicated measurements and the probability to obtain errors in these measurements are more. Statistical analysis can be used to obtain the true mean, another important application is to determine the uncertainty of the data gathered by means of variance and hence know the reliability of the data obtained. (Peters, 2001)
2.3.1 Estimation of the error in the measured quantities
2.3.1.1 Standard deviation
Standard deviation is the most common method to measure the variation of data from the average value. (O’Regan, 2016) The standard deviation of a sample can be calculated by using the relation given below:
𝑠 = √∑(𝑥𝑖 − �̅�)2
(𝑛 − 1)
(1)
Where S is the standard deviation, Xi is the individual value, x ̅ is the average, and n is the number of data. The standard deviation of the population can be calculated by using the relation given below:
Theoretical background
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𝜎 = √∑(𝑥𝑖 − 𝜇)2
𝑁
(2)
Where σ is the standard deviation, µ is the average, Xi is the individual value and N is the number of data.
2.3.1.2 Variance
Variance is also method to measure the variation in of the data from the average value. In short variance can be explained as the square of standard deviation. The variance of sample and population can be obtained by taking the square of S and σ respectively.
2.3.2 Moving/Floating average Moving average is a method used to study the behavior of a set of data collected when there is a frequent variation in the values. The moving average of a set of date X1, X2, X3……..etc. of order n can be calculated by,
Figure 6 : Red line depicts the graph of data after taking moving average (Walker, 2017)
Figure 6, shows an example for the calculation for floating average, It can be observed that the from the unsteady data which are obtained a behavior cannot be analyzed but the red graph which is calculated using moving average the data is steady and the behavior of the data can be analyzed more clearly. (Nicholson, 2014)
Theoretical background
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2.4 Design of Experiments (DoE)
To understand the products and processes of an existing system or to develop a new product or process, experiments are performed. Investigating the processes which include numerous factors, resources and time the experimentation cost eventually increases. Then to develop an experiment setup in order to increasing the learning about the system using minimum resources Design of Experiments (DoE) is the best tool available. (Experimental Design & Analysis Reference, 2015) Design of Experiment (DoE) is a statistical method used to define the correlations between the input and the output of a system or a process (Heikkinen Tim & Müller, 2015). To understand the effects of different variables on another variables, design and analysis of experiment technique is used. The objective of this method is to establish a cause and effect relationship between the number of dependent and independent variables. (C.Runger, 2002) According to the DoE the dependent variables are called the response and the independent variables are called factors and the experiments according to the need are run at different factor values, called levels. These runs involves the combination of number of investigated factors and these combinations are referred as treatment. Based on the number of factors to be investigated, the number of treatments required for an experiment are determined (Experimental Design & Analysis Reference, 2015). In industries, to identify the variables of product or its process that are affecting the quality of the product, design of experiment is used. (C.Runger, 2002)
Figure 7 : General model of a process or a system (C.Runger, 2002)
2.4.1 DOE Types
1. One factor design
2. General full factorial design
3. Two level full factorial design
4. Two level fractional factorial design
5. Plackett-Burman design
6. Taguchi’s orthogonal arrays
Theoretical background
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2.4.2 Stages of DOE
2.4.2.1 Planning
It is very important to plan the course of experiments before starting on the process of testing and data collection. The need for the experiment, availability of time and resources, good knowledge on the experiment procedure are a few things that has to be considered during the planning stage. It’s better to form a team comprising have individuals from different departments who are related to the product or process in order to identify the factors which they think are most appropriate or has to be measured. (Experimental Design & Analysis Reference, 2015)
2.4.2.2 Screening
In most of the DOE application cases the number of potential variables are huge. In order to identify the most important factors from a larger set of factors screening designs are used (Minitab, 2009) . These experiments are performed in the early stages of the project when many factors considered have little or no effect on the response (C.Runger, 2002). The most often used designs for screening are,
1. 2 level full and fractional factorial designs
2. Plackett-Burman designs
3. General full factorial design
2.4.2.3 Optimization
The most important factors affecting the process will be narrowed down from the screening experiment. In the optimization the objective is to determine the optimum setting of these important factors by which we can either increase, decrease or achieve a set value as a response from the process (Experimental Design & Analysis Reference, 2015)
2.4.2.4 Robustness Testing
After the determination of optimal settings the next stage is to improve the product or process to be insensitive to any variations with respect to the change in environmental conditions such as humidity, ambient temperature (Experimental Design & Analysis Reference, 2015).
2.4.2.5 Verification
Final validation of the test and the data collected.
2.4.3 Screening Experiment
Using DoE we are conducting this experiment to identify the factors that are have large effect on the performance of the catalytic converter, this type of experiment is called screening experiment. Where screening experiments are performed in the early stages of a project in which many or few factors are considered to be having an effect on the response and those factors with large effect are identified and more investigation is done in later experiments. Since we have 7 factors and the runs required for this experiment will be 2^7=128. We prefer fractional factorial design for the following reasons,
1. As mentioned earlier there will be little interest in the higher order interactions as we
are starting to study the system where certain higher order interactions are negligible
thereby consuming less time when compared to full factorial setup
2. Reduces the cost
Theoretical background
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2.4.3.1 Factorial Design
In this type of experiment we can study a response that was caused by several factors. Here the levels of all the factors are varied at the same time instead of one at a time and their interaction between the factors can be studied. According to (C.Runger, 2002)definition of factorial design is given as “By a factorial experiment we mean that in each complete trial or replicate of the experiment all possible combinations of the levels of the factors are investigated”. The type of factorial designs available are discussed in the following sections,
2.4.3.2 2K Factorial design
In this design the number of factors are denoted by ‘k’ each at only two levels and it may be either quantitative (temperature, pressure, time, speed) or qualitative (two machines, two operators). The observation of this design is attained by 2*2*….*2=2k and therefore it is called as 2k factorial design. During the early stages of the experiment this design is used (C.Runger, 2002). Here the “2” means that each factor is represented at either high or low level. (Rushing, Karl, & Wisnowski, 2013) The number of runs for the experiment depends on the number of factors.
Table 2 : Number of Runs for a 2k Factorial (Engineering Statistics Handbook, 2013)
Number of Factors(k) Number of Runs
2 4
3 8
4 16
5 32
6 64
7 128
2.4.3.3 Basic two factorial design
Let us consider A and B as the two factors therefore k=2 and the design for this factorial experiment is 2^2=4. The figure shows the 2^2 design. The low and high levels of the factors A and B are represented by ´–´ and ´+´ signs. This customary design representation is called as the geometric notation for the design (C.Runger, 2002).
Figure 8 : 2^2 factorial design (C.Runger, 2002)
Theoretical background
21
The lowercase letters given in the figure are the treatment combinations, ´ (1) ´ represents where both the factors are at low level and also they represent the total number of observations taken in this factorial experiment.
To estimate the effect of factor A, the average of the observations from the right side of the square in the Figure 8(where A is at high level) is subtracted by the average of the observations on the left side of the square in Figure 8(where A is at low level).
𝐴 = 𝑎𝑣𝑔𝐴+ − 𝑎𝑣𝑔𝐴−
=𝑎+𝑎𝑏
2𝑛−
𝑏+(1)
2𝑛
𝐴 =1
2𝑛[𝑎 + 𝑎𝑏 − 𝑏 − (1)]
(3)
Similarly, the effect of B can be found by,
𝐵 = 𝑎𝑣𝑔𝐵+ − 𝑎𝑣𝑔𝐵−
=𝑏+𝑎𝑏
2𝑛−
𝑎+(1)
2𝑛
𝐵 =1
2𝑛[𝑏 + 𝑎𝑏 − 𝑎 − (1)]
(4)
Now the AB interaction can estimated by subtracting the diagonal averages in the Figure 8
𝐴𝐵 =𝑎𝑏+(1)
2𝑛−
𝑎+𝑏
2𝑛
𝐴𝐵 =1
2𝑛[𝑎𝑏 + (1) − 𝑎 − 𝑏]
(5)
Table 3 : Signs for the Effects
Treatment Combination
Factorial Effect
A B AB
(1) - - +
A + - -
B - + -
AB + + +
Theoretical background
22
As we can see that in the table each treatment are assigned either with +1 or -1. These signs are determined by contrasts. The quantities from the equations 1, 2 and are called contrasts (C.Runger, 2002). The contrast of A is
ContrastA = a + ab – b − (1) (6)
2.4.3.4 Two factorial design (k>=3)
The number of factors (k) may exceed 2, for example k=3. The factorial design for this design will have 8 runs (2^3=8) and geometrically it forms a cube where each run represents a corner of the cube as we can see in Figure 9.
Figure 9 : Geometric view of 2^3 factorial design (C.Runger, 2002)
With this we can estimate the main effects A, B and C along with the two factor interactions (AB, AC and BC) and one three factor interaction ABC. Figure 10, illustrates how the estimation of main effects can be achieved.
Figure 10 : Main Effects (C.Runger, 2002)
The main effect of A from the cube gives,
𝐴 = 𝑎𝑣𝑔𝐴+ − 𝑎𝑣𝑔𝐴−
=𝑎+𝑎𝑏+𝑎𝑐+𝑎𝑏𝑐
4𝑛−
(1)+𝑏+𝑐+𝑏𝑐
4𝑛
𝐴 =1
4𝑛[𝑎 + 𝑎𝑏 + 𝑎𝑐 + 𝑎𝑏𝑐 − (1) − 𝑏 − 𝑐 − 𝑏𝑐]
(7)
The effect of B,
Theoretical background
23
𝐵 = 𝑎𝑣𝑔𝐵+ − 𝑎𝑣𝑔𝐵−
𝐵 =1
4𝑛[𝑏 + 𝑎𝑏 + 𝑏𝑐 + 𝑎𝑏𝑐 − (1) − 𝑎 − 𝑐 − 𝑎𝑐]
(8)
The effect of C,
𝐶 = 𝑎𝑣𝑔𝐶+ − 𝑎𝑣𝑔𝐶−
𝐶 =1
4𝑛[𝑐 + 𝑎𝑐 + 𝑏𝑐 + 𝑎𝑏𝑐 − (1) − 𝑎 − 𝑏 − 𝑎𝑏]
(9)
The effect of two factor interactions using the cube is shown in the Figure 11. The one-half of the difference between the averages A effect at the two levels of B gives the measure of AB interaction.
Figure 11 : Two-factor interactions (C.Runger, 2002)
The effect of AB interaction is obtained by,
B Average A Effect
High (+) [(𝑎𝑏𝑐−𝑏𝑐)+(𝑎𝑏−𝑏)]
2𝑛
Low (-) [(𝑎𝑐−𝑐)+(𝑎−(1))]
2𝑛
Difference [𝑎𝑏𝑐−𝑏𝑐+𝑎𝑏−𝑏−𝑎𝑐+𝑐−𝑎+(1)]
2𝑛
Now we take the one-half of this difference,
𝐴𝐵 =1
4𝑛[𝑎𝑏𝑐 − 𝑏𝑐 + 𝑎𝑏 − 𝑏 − 𝑎𝑐 + 𝑐 − 𝑎 + (1)]
(10)
By using the same way we can get the AC and AB interactions
𝐴𝐶 =1
4𝑛[(1) − 𝑎 + 𝑏 − 𝑎𝑏 − 𝑐 + 𝑎𝑐 − 𝑏𝑐 + 𝑎𝑏𝑐]
(11)
𝐵𝐶 =1
4𝑛[(1) + 𝑎 − 𝑏 − 𝑎𝑏 − 𝑐 − 𝑎𝑐 + 𝑏𝑐 + 𝑎𝑏𝑐]
(12)
By the average difference between the AB interactions for the two different levels of C we can obtain the ABC interaction.
Theoretical background
24
Figure 12 : Three factor interaction
𝐴𝐵𝐶 =1
4𝑛[𝑎𝑏𝑐 − 𝑏𝑐 − 𝑎𝑐 + 𝑐 − 𝑎𝑏 + 𝑏 + 𝑎 − (1)]
(13)
Table 4 : Effects in the 2^3 Design
Treatment Combination
Factorial Effect
I A B AB C AC BC ABC
(1) + - - + - + + -
A + + - - - - + +
B + - + - - + - +
AB + + + + - - - -
C + - - + + - - +
AC + + - - + + - -
BC + - + - + - + -
By multiplying the treatment combinations in the table with respect to the signs in the corresponding main effect or the interaction column we can estimate the main effect or interaction effect of a 2^k factorial design. (C.Runger, 2002)
2.4.4 Fractional Factorial Design The number of runs required for a 2^k design becomes high as the number of factors increase. For example, if the number of factors are 9 then the 2^9 factorial design will have 512 runs. In such cases fractional factorial design can be used. The fractional factorial design is based on the sparsity of effects principle which means that in most cases the responses by a small number of main effects and lower order interactions are very important whereas responses of higher order interactions are less important. (Experimental Design & Analysis Reference, 2015)
2.4.4.1 One-Half Fraction of the 2^k Design
This type of design is called as 2^k-1 fractional factorial design. For example if we consider a 2^3-1 design it is a one-half fraction of 2^3 design, where the fractional design has only four runs in contrast to the 2^3 full factorial design in which it has eight runs. (C.Runger, 2002)
Let us consider from the above table we select the treatments where the ABC interaction is at high level (i.e. where the entries of ABC is ‘+’). Table 5 shows the resulting the 2^3 -1 fractional factorial design,
Theoretical background
25
Table 5 : Fractional factorial design where ABC interaction is at high level
Treatment Combination
Factorial Effect
I A B AB C AC BC ABC
A + + - - - - + +
B + - + - - + - +
C + - - + + - - +
ABC + + + + + + + +
And the following Table 6 shows if we consider the ABC interaction at low level (i.e. where the entries of ABC is ‘-‘)
Table 6 : Fractional factorial design where ABC interaction at low level
Treatment Combination
Factorial Effect
I A B AB C AC BC ABC
AB + + + + - - - -
AC + + + - + + - -
BC + - - - + - + -
(1) + - - + - + + -
In both the cases the interaction ABC is included at the same level hence it is not possible to measure ABC interaction effect. Now, the effect ABC is called the generator for this design. The identity element is also same with respect to ABC interaction level. Therefore we can write the identical columns as
𝐼 = 𝐴𝐵𝐶
(14)
The above equation is called the defining relation for the design.
Figure 13 : Defining Relation (C.Runger, 2002)
2.4.4.2 Calculation of Effects
For the 2^3-1 fractional factorial design the response of the main effects are calculated below:
Theoretical background
26
𝐴 =(𝑎 + 𝑎𝑏𝑐)
2−
(𝑏 + 𝑐)
2
𝐴 =1
2(𝑎 − 𝑏 − 𝑐 + 𝑎𝑏𝑐)
(15)
𝐵 =(𝑏 + 𝑎𝑏𝑐)
2−
(𝑎 + 𝑐)
2
𝐵 =1
2(−𝑎 + 𝑏 − 𝑐 + 𝑎𝑏𝑐)
(16)
𝐶 =(𝑐 + 𝑎𝑏𝑐)
2−
(𝑎 + 𝑏)
2
𝐶 =1
2(−𝑎 − 𝑏 + 𝑐 + 𝑎𝑏𝑐)
(17)
Similarly we can obtain the two factor interactions also,
𝐵𝐶 =1
2(𝑎 − 𝑏 − 𝑐 + 𝑎𝑏𝑐)
(18)
𝐴𝐶 =1
2(−𝑎 + 𝑏 − 𝑐 + 𝑎𝑏𝑐)
(19)
𝐴𝐵 =1
2(−𝑎 − 𝑏 + 𝑐 + 𝑎𝑏𝑐)
(20)
From the above relations we can see that the quantity of effect A is similar to the effect of BC interaction which means the effect A and AB are cofounded in this design. Thus the quantity 1
2(𝑎 − 𝑏 − 𝑐 + 𝑎𝑏𝑐) estimates both the main effect A and the two factor interaction BC.
Thereafter the effects A and BC are called aliases (Experimental Design & Analysis Reference, 2015). Similarly from the remaining equations we can see that B and AC, and C and AB are the two other aliases. Now the equations to calculate the effects in the 2^-3 factorial design can be written as,
𝐴 + 𝐵𝐶 =1
2(𝑎 − 𝑏 − 𝑐 + 𝑎𝑏𝑐)
(21)
𝐵 + 𝐴𝐶 =1
2(−𝑎 + 𝑏 − 𝑐 + 𝑎𝑏𝑐)
(22)
𝐶 + 𝐴𝐵 =1
2(−𝑎 − 𝑏 + 𝑐 + 𝑎𝑏𝑐)
(23)
By multiplying defining relation with any effect the aliases for that effect can be attained. For our 2^3-1 design the alias of A is,
𝐴 = 𝐴. 𝐴𝐵𝐶 = 𝐴2𝐵𝐶 = 𝐵𝐶
(24)
Since 𝐴. 𝐼 = 𝐴 and𝐴2 = 𝐼. The aliases of B and C are
Theoretical background
27
𝐵 = 𝐵. 𝐴𝐵𝐶 = 𝐴𝐵2𝐶 = 𝐴𝐶
(25)
𝐶 = 𝐶. 𝐴𝐵𝐶 = 𝐴𝐵𝐶2 = 𝐴𝐵
(26)
2.4.4.3 Smaller Fraction Designs
In some cases the number of runs required in a half-fraction design might also be large. In such cases smaller fractions are preferred. When a 2^k design run as a ½^p fraction design it is called as 2^k-p fractional factorial design (C.Runger, 2002)
1/4 fractional design is represented as 2^k-2 design
1/8 fractional design as 2^k-3
1/16 fractional design as 2^k-4.
A smaller fraction design requires two defining relations. In the first defining relation it returns the half-fraction (2^k-1) of the design and in the second defining relation the half of the runs of 2^k-1 design is selected to give the quarter-fraction (Experimental Design & Analysis Reference, 2015) .
For example let us consider a 2^4 design and if we use smaller fraction 2^4-2 design for the 2^4 design the first half-fraction for the design is attained by using a defining relation where I=ABCD. Table 7 shows the resulting 2^4-1 design matrix.
Table 7 : 2^4-1 Design Matrix
I A B AB C AC BC D AD BD ABC ABD CD ACD BCD ABCD
1 -1 -1 1 -1 1 1 -1 1 1 -1 -1 1 -1 -1 1
1 1 -1 -1 -1 -1 1 1 1 -1 1 -1 -1 -1 1 1
1 -1 1 -1 -1 1 -1 1 -1 1 1 -1 -1 1 -1 1
1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1
1 -1 -1 1 1 -1 -1 1 -1 -1 1 1 1 -1 -1 1
1 1 -1 -1 1 1 -1 -1 -1 1 -1 1 -1 -1 1 1
1 -1 1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
From the above design matrix for the 2^4-1 design a quarter fraction design 2^4-2 using the second defining relation I=AD can be achieved. The resulting design is shown in Table 8.
Table 8 : 2^4-2 Design Matrix
I A B AB C AC BC D AD BD ABC ABD CD ACD BCD ABCD
1 -1 -1 1 -1 1 1 -1 1 1 -1 -1 1 -1 -1 1
1 1 -1 -1 -1 -1 1 1 1 -1 1 -1 -1 -1 1 1
1 -1 1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
The defining relation for the 2^4-2 design is,
𝐼 = 𝐴𝐵𝐶𝐷 = 𝐴𝐷 = 𝐵𝐶
(27)
The aliases structure calculated for the above design using the defining relation are as follows,
𝐴. 𝐼 = 𝐴. 𝐴𝐵𝐶𝐷 = 𝐴. 𝐴𝐷 = 𝐴. 𝐴𝐵𝐶
Theoretical background
28
𝐴 = 𝐴2𝐵𝐶𝐷 = 𝐴2𝐷 = 𝐴𝐵𝐶
𝐴 = 𝐵𝐶𝐷 = 𝐷 = 𝐴𝐵𝐶
(28)
From the above structure we can see that two main effects are aliased together (A with D) which is not a useful design. While deciding the half-fraction design it is necessary to ensure that the main effects and lower order interactions are not aliased together. Using the resolution of the fractional factorial design this can be achieved.
2.4.5 Design Resolution The number of factors in the lowest order effect in the defining relation is defined as the resolution of a fractional factorial design. The defining relation in 2^4-2 design I =ABCD=AD=BC. In this AD or BC containing two factors is the lowest order effect in the designing relation therefore the design resolution for this fractional factorial is II, which will be represented as2𝐼𝐼
4−2. (Experimental Design & Analysis Reference, 2015)
2.4.5.1 Resolution III Designs
In this type of design no main effects are aliased with any other main effect whereas main effects are aliased with two-factor interactions and some two-factor interactions may be aliased with each other. The 2^3-1 design with I=ABC is an example of resolution III design. To indicate the design resolution it is usually mentioned with a Roman numeral subscript. The above example design is mentioned as 2𝐼𝐼𝐼
3−1 design.
2.4.5.2 Resolution IV Designs
In this type of design no main effect is aliased with any other main effect or two-factor interactions whereas two-factor interactions are aliased with each other. For example this type of design will be mentioned as 2𝐼𝑉
4−1 design.
2.4.5.3 Resolution V Designs
In a design where no main effect r two-factor interactions is aliased with any other main effect or two-factor interaction, but two-factor interactions are aliased with three-factor interactions are resolution V type designs. For example 2^5-1 design with I=ABCDE is a resolution V design
mentioned as2𝑉5−1.
In most of the screening experiments resolution III and IV designs are preferred as the resolution IV design provides a better information regarding the main effects and about all two-factor interactions. (C.Runger, 2002)
Theoretical background
29
Figure 14 : Resolution designs for fractional factorial (Experimental Design & Analysis Reference, 2015)
Figure 15 : Available factorial designs in Minitab (Experimental Design & Analysis Reference, 2015)
2.5 Pareto Chart
The Pareto chart is an important variation of histogram for categorical data. This chart is widely used in quality improvement efforts and the categories usually represent different type of defects, failure modes or product or process problems. The categories are ordered so that the category with largest frequency is on the left, followed by the category with the second largest frequency and so on. It is named after an Italian economist V.Pareto and the well-known “Pareto law”; that is most of the defects can be accounted for by only a few categories. (C.Runger, 2002)
2.6 Minitab Minitab is a comprehensive statistic application covering a wide range of statistical techniques (Tania Prvan, 2002). It is used for learning about statistics as well as statistical research. The advantage of using this application is, it is more accurate, reliable and generally faster than computing statistics and drawing graphs by hand (Ginger Holmes Rowell, 2004). Using Minitab Statistical analysis such as ANOVA, regression analysis, quality charts and time series along with built-in graphics to visualize the data and their results can be performed and store statistics and diagnostic measures. (Minitab, 2009)
Theoretical background
30
Features offered by Minitab to perform DOE are (Minitab, 2009),
Catalogs of designed experiments to create a design
Automatic creation and storage of the design
Display and storage of diagnostic statistics
Graphs to interpret and present the results
Method and implementation
31
3. Method and implementation
The research in this thesis work is focused on finding the parameters responsible for the
performance of the catalytic converters used in the test rigs of Husqvarna AB. The common
approach in tracing the cause and effect relationships between defined variables is the
experimental research method. (Williamson, 2002 )
Even though one-factor at a time experiment will provide better understanding about the
effect of each factor it doesn’t provide the information on how the factor affects the product
or process in the presence of other factors. In most cases the interaction effects are more
important than the effect of individual factors. By using DOE one guarantees the complete
investigation about all the factors and their interactions which is more reliable than one-
factor at a time experiment. (Experimental Design & Analysis Reference, 2015)
3.1 Experimental Approach
Figure 16 shows the research approach followed in this thesis work. The initial study about
the catalytic converter and the DOE are explained in the Catalytic Converter2.2 and 2.4.
Figure 16 : DOE Test Approach
Method and implementation
32
3.2 Measurement Relations
The data collected from the experiment will represent the volume of hydrocarbon (HC)
content of the exhaust in general ventilation, before catalyst and after catalyst regions.
Along with these it also provides the data regarding the flow in the exhaust, heating coil
ON/OFF, flow in the general ventilation and catalyst temperature. Using these data the
degree of collection, degree of conversion and the percentage of time the heating coil was
ON/OFF can be calculated.
3.2.1 Degree of Collection
The degree of collection represents the overall percentage of exhaust gas from the product
being taken by the collector which will be sent to the catalytic converter for purification
before entering the atmosphere along with the amount of exhaust being leaked into the
general ventilation. The degree of collection can be calculated by the relation given below:
𝐷𝑒𝑔𝑟𝑒𝑒 𝑜𝑓 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑖𝑜𝑛
= ((𝐻𝐶 𝑏𝑒𝑓𝑜𝑟𝑒 𝑐𝑎𝑡𝑎𝑙𝑦𝑠𝑡 + 𝐻𝐶 𝑔𝑒𝑛𝑒𝑟𝑎𝑙 𝑣𝑒𝑛𝑡𝑖𝑙𝑎𝑡𝑖𝑜𝑛)
(𝐻𝐶 𝑏𝑒𝑓𝑜𝑟𝑒 𝑐𝑎𝑡𝑎𝑙𝑦𝑠𝑡 ∗ 𝐹𝑙𝑜𝑤 𝑖𝑛 𝑒𝑥ℎ𝑎𝑢𝑠𝑡 𝑑𝑢𝑐𝑡) + (𝐻𝐶 𝑔𝑒𝑛𝑒𝑟𝑎𝑙 𝑣𝑒𝑛𝑡𝑖𝑙𝑎𝑡𝑖𝑜𝑛 ∗ 𝐹𝑙𝑜𝑤 𝑖𝑛 𝑔𝑒𝑛𝑒𝑟𝑎𝑙 𝑣𝑒𝑛𝑡𝑖𝑙𝑎𝑡𝑖𝑜𝑛)) ∗ 100
(29)
3.2.2 Conversion Level
The conversion level represents the percentage of hydrocarbon getting burned in the
catalytic converter. Conversion level is calculated by comparing the volume of hydrocarbon
in the exhaust before and after catalyst. The relation for the conversion level is given below:
𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑙𝑒𝑣𝑒𝑙 =𝐻𝐶 𝑏𝑒𝑓𝑜𝑟𝑒 𝑐𝑎𝑡𝑎𝑙𝑦𝑠𝑡 − 𝐻𝐶 𝑎𝑓𝑡𝑒𝑟 𝑐𝑎𝑡𝑎𝑙𝑦𝑠𝑡
𝐻𝐶 𝑏𝑒𝑓𝑜𝑟𝑒 𝑐𝑎𝑡𝑎𝑙𝑦𝑠𝑡∗ 100
(30)
3.2.3 Heating Coil ON/OFF
The percentage of time the heating coil was ON during the test run will give an idea about
the power consumption for the test. As the energy used during testing is also a constrain.
This is calculated by the relation given below:
% 𝑜𝑓 𝑡𝑖𝑚𝑒 ℎ𝑒𝑎𝑡𝑖𝑛𝑔 𝑐𝑜𝑖𝑙 𝑤𝑎𝑠 𝑜𝑛 =𝑇𝑖𝑚𝑒 (𝑠) 𝑐𝑜𝑖𝑙 𝑤𝑎𝑠 𝑂𝑁
𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑡𝑒𝑠𝑡 𝑟𝑢𝑛(𝑠)∗ 100
(31)
The relations mentioned above are coded in Matlab Appendix 4: for ease of calculation.
Method and implementation
33
3.3 Test setup
3.3.1 Test Rig:
Figure 17 : Product placement in the test rig
The major component in the experimental set is the test rig Figure 17 which is basically a box
in which the product (chainsaw) will be placed and tested for several hours. The major
components in the test rig are mention in Figure 19 which are,
1. Speed sensor to monitor the speed of the chainsaw (RPS) 2. Thermal sensor to monitor the temperature in the box which will work as a safety
mechanism in order to maintain the temperature in the test rig below the set alarm
temperature 3. Flow sensor in the exhaust duct which will give feedback to the blower inside the exhaust
duct to maintain the constant set flow value. 4. The general ventilation duct 5. The exhaust gas from the product is collected by a collector (which is a simple tube with
a lip)
Figure 18 : Collector used in the test rig
Method and implementation
34
6. A heat exchanger which will act as an extra heating source for the exhaust gas before
entering into the catalytic converter
7. A heating coil acting as a temperature feedback from the catalytic converter to maintain
it at constant temperature.
8. In the rig there are two catalytic converters which are shown in Figure 20. The small
catalyst is placed first to enhance the reaction and the big catalyst will burn the HC and
CO in the exhaust. Both the catalyst are kept at 10-20mm apart. The specification of the
catalyst used the test rigs can be seen in Table 9.
Figure 19 Layout of the test rig
Table 9 : Chemical composition of the Catalyst
Part Material/dimension
Mantel Aisi 409, thickness 1,5 mm
Pins W 1.4828 or W 1.4833
Metal Foil (flat and corrugated) W 1.4767, thickness 0,05 mm
Wash coat Alumina based with rear earths and additives/promoters
Precious metal Palladium, Platinum & Rhodium
Method and implementation
35
Figure 20 : Catalytic converters used in the test rig
Then there is the measuring system for calculating the volume of hydrocarbons (in ppm). The
measuring system used here is FID (Flame Ionization Detector). The measurements are taken
from three points in the test system which are before catalyst, after catalyst and in the general
ventilation. As there is only one FID, all the tree points cannot be measured at the same time so
the measurements has to be done one after other and the best way to do it is to measure in the
general ventilation in the beginning followed by after catalyst then before catalyst. The FID has
to be calibrated at each step.
Method and implementation
36
3.3.2 Measurement Instrument (FID):
Figure 21 : FID
The HC level in the test system is measured by means of FID. The working principle of FID is upon combustion of hydrocarbons in hydrogen flame ions are generated. The rate of generation of these ions from the combustion is directly proportional to the volume of HC in the exhaust gas. The measured data obtained using FID are often titled as total hydrocarbons or total hydrocarbon content. (International, 2012)
3.3.3 Flow in the general ventilation The flow inside the general ventilation needs to be measured periodically as it is not fixed. As
the general ventilation is common for all the test rigs the flow will depend on the number of
products getting tested at the same time which will lead to changes in the temperature of the
air in the ventilation which in turn will lead to the change in density and on variation of the
flow. The flow in the general ventilation is an important criteria in determining the degree of
collection.
Figure 22 : General ventilation flow measurement setup
Method and implementation
37
3.3.4 Optimal position of the collector: The positon of the collector with respect to the product is very important. For each of the
product the angle at which the exhaust gas leaving is different. So the optimal position has to
be set by means of the measurement of hydrocarbon content in the exhaust duct before entering
the catalyst. The product is started and the position at which the measure of hydrocarbon
content is high will be set as the optimum position to place the collector.
If the collector is set away from this optimal position there will be generation of back pressure
as the exhaust will hit on the surface of the collector. This backpressure will lead to leakage of
HC and CO inside the test rig. As the test involves use of collectors with different diameters and
varying distance with respect to the muffler, finding the optimal position of the collector is very
important. Collecting the maximum amount of exhaust will prevent the leakage of CO inside
the test rig which leads to environmental and health risk for the engineer performing the test.
Figure 23, shows the equipment used to set the optimal position of the collector.
Figure 23 : Emission analyzer used to set the optimal position
3.4 Initial Test Runs: To check the reliability of the measurement method which will be used to perform the DOE test
runs, a set of initial test runs were conducted. In order to achieve this, six iterations of test runs
are conducted under a constant setup of parameters and environment. The table below shows
the standard deviation of the degree of collection and degree of conversion which are obtained
during the six trial runs.
Method and implementation
38
Table 10 : Initial test analysis
Test Degree of collection Degree of conversion
1 99,8 91,8
2 99,08 92,066
3 99,29 93,9
4 98,9 90,4
5 98,92 93,6
6 98,18 89,2
Average 99,03 91,83
Standard deviation 0,49 1,66
3.4.1 Determining the duration of experiment for data collection The Figure 24 shows there is some instability of data in the beginning and as it proceeds it
attains a stable state. This initial instability is due to the influence of noise during the data
collection which will affect the collection and conversion results. For the calculation, the data
has to be extracted once the stability in the measurement is attained. To achieve this an
approach of comparing averages is used.
But the data from the test system is fluctuating so for the ease of analysis the data is made stable
by means of moving average/floating average.
Figure 24 : Results attained after using the floating average on the initial data
Method and implementation
39
After getting the stable data for the floating average, the time at which the stable region is reached is obtained by comparing the averages in between the cut time (Tc) and the measured time (Tm) from the data. As the same comparison has to be iterated a lot of times, Matlab is the tool used to do the calculations can be seen in Appendix 3:.
3.4.2 Initial observations From Table 11 it can be observed that the FID value in the general ventilation and before the
catalyst are not dependent on the catalyst temperature but dependent on the product features
and the type of test cycle used for testing. It can be seen that the FID value after the catalyst is
dependent on the catalyst temperature.
Table 11 : Variation in HC with time
0
200
400
600
800
1000
1200
11
20
23
93
58
47
75
96
71
58
34
95
31
07
21
19
11
31
01
42
91
54
81
66
71
78
61
90
52
02
42
14
32
26
22
38
12
50
02
61
92
73
82
85
72
97
63
09
53
21
43
33
3
FID
Val
ue
Time (seconds)
FID value after catalyst
FID
Kattemp
0
1
2
3
4
5
6
7
1
48
95
14
2
18
9
23
6
28
3
33
0
37
7
42
44
71
51
8
56
5
61
2
65
9
70
6
75
3
80
0
84
7
89
49
41
98
8
10
35
10
82
11
29
11
76
12
23
12
70
13
17
13
64
14
11
14
58
15
05
15
52
15
99
16
46
FID
Val
ue
Time (seconds)
FID value in general ventilation
Method and implementation
40
3.5 DOE Test
3.5.1 Factors which has to be considered for DOE To list the factors which are considered to be affecting the performance of the catalytic converter
in the test rig an initial brainstorming with the engineers who are involved with the test rig was
conducted. Suggestions from all their viewpoint are listed out and discussed which is shown in
Appendix 1:.
As a list of factors affecting the catalytic converter are to be decided before the designing of the
experiment (DOE) several brain storming sessions were conducted and we ended up with a list
of factors. Then the interaction between the factors were looked into so as to reduce the risk to
be taken while taking the effects of the parameters and the interactions from DOE in Minitab.
Table 12 shows the interaction between the parameters where the highlighted are the
interaction which are possible to have more effects on the measured variables.
Table 12 : List of parameters and its interactions
0
500
1000
1500
2000
2500
3000
3500
4000
4500
15
71
13
16
92
25
28
13
37
39
34
49
50
55
61
61
76
73
72
97
85
84
18
97
95
31
00
91
06
51
12
11
17
71
23
31
28
91
34
51
40
11
45
71
51
31
56
9
FID
Val
ue
Time
FID value before catalyst
FID
Kattemp
Method and implementation
41
Finally after several discussions 7 parameters are listed out and their maximum and minimum
values to be used during the DOE experiment were decided.
Collector lip: It is an excess surface which will be projecting from the top of the collector. When
the collector is connected to the product, the lip provides better covering to the muffler.
Flow in the exhaust duct: The flow in the exhaust duct can be set within the test rig’s interface
(LP99), which will give the exhaust fan in the duct the feedback which will regulate the flow in
the range of the set value.
Exhaust hose distance to the first curve: The distance to the first curve in the exhaust hose is a
function of the length of the collector used. So, to increase the distance of the bend the length
of the collector has to be increased.
Collector angle of lateral deviation: As the experimental setup inside the test rig is not flexible
enough to change the angle of the collector covering the muffler, in order to do this
trigonometric relations were used to calculate the distance which the collector to be displaced
to obtain the required angle.
From Figure 25 the value of X and ϴ are known where x is the distance between the muffler and
the joint in the collector and ϴ is the angle to be displaced so Y which is the distance to be moved
can be calculated by using the relation,
Y = tanϴ ∗ X
(32)
Product feature- power: As changing the product to obtain a different volume of hydrocarbon
is hard, the air/fuel ratio value is regulated to replicate the same effect as that is obtained by
changing the product. This can be done by changing the engine tuning.
Figure 25 : Angle of lateral deviation
Method and implementation
42
Figure 26 : Types of collector arrangement used during the test
Table 13 : Parameter and their levels while performing the DOE
Parameter Low High
Collector Lip With lip Without lip
Collector Angle 0 30
Collector distance to muffler Optimum Optimum+30
Collector Diameter 80 120
Collector Length Minimum Maximum
Flow 80 140
Air/fuel Ratio Minimum Maximum
Method and implementation
43
3.5.2 DOE Design: As the number of parameters are seven, the total number of test runs required will be 2^7 which
is 128 but that’s not feasible so the concept of fractional reduction of DOE was selected. Then
after looking the alias structure, confounding of parameters and interactions the 2^7-3 design
is taken, as the major parameters and the interactions are not getting confounded in this design.
All these parameters can be inserted into Minitab to obtain the alias structure and the test plan.
The test plan obtained from Minitab is shown below in which A, B, C etc. denotes the parameters
which can be seen in Table 12
Fractional Factorial Design
Factors: 7 Base Design: 7; 16 Resolution: IV
Runs: 16 Replicates: 1 Fraction: 1/8
Blocks: 1 Centre pts (total): 0
Design Generators: E = ABC; F = BCD; G = ACD
Alias Structure
I + ABCE + ABFG + ACDG + ADEF + BCDF + BDEG + CEFG
A + BCE + BFG + CDG + DEF + ABCDF + ABDEG + ACEFG
B + ACE + AFG + CDF + DEG + ABCDG + ABDEF + BCEFG
C + ABE + ADG + BDF + EFG + ABCFG + ACDEF + BCDEG
D + ACG + AEF + BCF + BEG + ABCDE + ABDFG + CDEFG
E + ABC + ADF + BDG + CFG + ABEFG + ACDEG + BCDEF
F + ABG + ADE + BCD + CEG + ABCEF + ACDFG + BDEFG
G + ABF + ACD + BDE + CEF + ABCEG + ADEFG + BCDFG
AB + CE + FG + ACDF + ADEG + BCDG + BDEF + ABCEFG
AC + BE + DG + ABDF + AEFG + BCFG + CDEF + ABCDEG
AD + CG + EF + ABCF + ABEG + BCDE + BDFG + ACDEFG
AE + BC + DF + ABDG + ACFG + BEFG + CDEG + ABCDEF
AF + BG + DE + ABCD + ACEG + BCEF + CDFG + ABDEFG
AG + BF + CD + ABDE + ACEF + BCEG + DEFG + ABCDFG
BD + CF + EG + ABCG + ABEF + ACDE + ADFG + BCDEFG
ABD + ACF + AEG + BCG + BEF + CDE + DFG + ABCDEFG
Major interactions: (highlighted in yellow)
AG, BG, CG, DG, FG, AC, BD, CE, BF
Minor interactions (highlighted in green)
AD, CF, DF, EF
No main effects are aliased any other main effect and 2-factor interactions. 2-factor interaction aliased with other 2-factor interaction. Main effect are aliased with 3-factor interaction.
Method and implementation
44
3.6 SEM Analysis An SEM analysis is done to study the factors responsible for the aging of the catalytic converter
and also to determine the area at which the reaction is happening as there are chances for an
uneven flow into the catalyst which will accelerate the aging of the catalyst and will also affect
the conversion rate in the catalyst.
Factors affecting the aging of catalyst are studied by analyzing and comparing the size of the
particles in the wash coat, porosity of the wash coat and the chemical composition of the
materials which are accumulated on the aged catalyst from chain saw test rig and in the new
catalytic converter as a reference. The effect of uneven flow leading to depletion of wash coat is
studied by analyzing the outer and inner surfaces of the catalytic converter.
3.6.1 Sample preparation As the surface which needs to be analyzed is the internal surface of the catalyst, first the stainless
steel mantle of the catalyst is removed and the samples from catalyst are taken at multiple
locations in the same catalyst.
After the removal of the outer mantle a section of the catalyst is cut and as we see from Figure
27 and Figure 28 below flakes of the catalyst are being collected and depending on the variant
in size we can identify how close they were with respect to the center. According to the position
from the center they are classified into inner, middle and outer. The test was done on new
catalyst and aged catalyst used in the chainsaw test rig.
Figure 27 : Sample preparation – New Catalyst (Outer & Inner)
Method and implementation
45
Figure 28 : Sample preparation – Aged Catalyst (Outer & Inner)
The samples are being labelled according to the type of catalyst and their distance from the
center inside the mantel. Before analyzing the samples in the SEM they are cleaned by placing
in ultrasonic cleaner with ethanol solution for about 10 minutes and the samples are cut into
the desired size as to be placed inside the SEM which can be seen in Figure 29.
Figure 29 : Samples used in the SEM analysis
3.6.2 Tests Performed
In order to compare the aged catalyst with new catalyst the chemical composition on the wash
coat was analyzed in the SEM with Energy Dispersive spectroscopy (EDS). It is performed to
check the presence of any chemical on the wash coat that is responsible for the catalytic
deactivation.
The molecular structure of the wash coat was analyzed to check the possibility of sintering or
thermal degradation of the wash coat. To do this backscattering SEM micrograph images were
obtained from the samples.
Findings and analysis
46
4. Findings and analysis
4.1 DOE Test Results
The results from the test plan Appendix 5: are inserted into MINITAB to analyze the effect
of each factors and their interactions upon Degree of collection, Degree of conversion and
heating coil ON/OFF in the test rig. The results are expressed in the form of Pareto chart.
4.1.1 Response for Degree of Collection The response of degree of collection according to the variation of various parameters can
be seen in Figure 30, the factors and interactions which has more effect were sorted out and
considered to have major influence on the degree of collection. Those major factors and
interactions are given in Table 14.
Table 14 : Major parameters responsible for degree of collection
Factors
Exhaust Flow(F)
Distance to muffler(C)
Collector diameter(D)
Interactions Collector diameter / Exhaust Flow(DF)
Lip / Collector angle(AB)
From Table 14 it can be seen that the parameter interaction AE is being replaced with DF.
From Table 12, it is observed that the interaction AE doesn’t have a major effect and from
the alias structure in the DOE test design it can be seen that the DF which is a major
interaction is getting aliased by AE, so AE can be replaced with DF.
Figure 30 : Pareto Chart of degree of collection
Findings and analysis
47
Table 15 : Effect of each parameter and their coefficient
Term Effect Coefficient
Constant 95,55
lip -0,64 -0,32
collector angle 1,474 0,737
distance to muffler -2,434 -1,217
hose diameter -2,18 -1,09
distance to first curve 1,6513 0,8256
exhaust flow 3,873 1,936
Air/Fuel Ratio -0,9562 -0,4781
lip*collector angle 2,222 1,111
lip*distance to muffler -1,1264 -0,5632
lip*hose diameter -0,012927 -0,006463
Collector diameter*Exhaust Flow 3,652 1,826
lip*exhaust flow -1,0864 -0,5432
lip*Air/Fuel Ratio -0,3104 -0,1552
collector angle*hose diameter 0,8514 0,4257
lip*collector angle*hose diameter -0,4889 -0,2444
In Table 15, the effects of each of the parameters and its coefficients are listed. The
parameters which are highlighted are the major parameters affecting the degree of
collection. The coefficients will give an idea regarding the variation in the degree of
collection with the variation of each of the parameters which can also be seen in the
regression equation for degree of collection shown below,
𝐷𝑒𝑔𝑟𝑒𝑒 𝑜𝑓 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑖𝑜𝑛
= 95,55 − 0,3200 𝑙𝑖𝑝 + 0,7370 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 − 1,217 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑚𝑢𝑓𝑓𝑙𝑒𝑟
− 1,090 ℎ𝑜𝑠𝑒 𝑑𝑖𝑎 + 0,8256 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑓𝑖𝑟𝑠𝑡 𝑐𝑢𝑟𝑣𝑒 + 1,936 𝑒𝑥ℎ 𝑓𝑙𝑜𝑤
− 0,4781 𝐴𝑖𝑟\𝐹𝑢𝑒𝑙 𝑅𝑎𝑡𝑖𝑜 + 1,111 𝑙𝑖𝑝 ∗ 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 − 0,5632 𝑙𝑖𝑝
∗ 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑚𝑢𝑓𝑓𝑙𝑒𝑟 − 0,006463 𝑙𝑖𝑝 ∗ ℎ𝑜𝑠𝑒 𝑑𝑖𝑎 + 1,826 𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑑𝑖𝑎𝑚𝑒𝑡𝑒𝑟
∗ 𝐸𝑥ℎ𝑎𝑢𝑠𝑡 𝐹𝑙𝑜𝑤 − 0,5432 𝑙𝑖𝑝 ∗ 𝑒𝑥ℎ 𝑓𝑙𝑜𝑤 − 0,1552 𝑙𝑖𝑝 ∗ 𝐴𝑖𝑟\𝐹𝑢𝑒𝑙 𝑅𝑎𝑡𝑖𝑜
+ 0,4257 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 ∗ ℎ𝑜𝑠𝑒 𝑑𝑖𝑎 − 0,2444 𝑙𝑖𝑝 ∗ 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒
∗ ℎ𝑜𝑠𝑒 𝑑𝑖𝑎
(33)
From the regression equation it can be observed that the increase in the distance to muffler
and hose diameter/collector diameter will reduce the degree of collection while the increase
in exhaust flow and the interactions lip*collector angle and collector diameter* exhaust flow
will increase the degree of collection.
Findings and analysis
48
4.1.2 Conversion level
From Figure 31, the major factors and interactions responsible for the variation in the
conversion level can be observed, the factors with higher response are listed in Table 16.
Table 16 : Major parameters affecting conversion level
Factors Distance to first curve(E)
Distance to muffler(C)
Interactions Lip*collector angle(AB)
Figure 31 : Pareto Chart for Conversion level
Findings and analysis
49
Table 17 : Effect of each parameter and their coefficient
Term Effect Coefficient
Constant 91,45
lip 4,393 2,196
collector angle 4,074 2,037
distance to muffler -5,489 -2,745
hose diameter -4,182 -2,091
distance to first curve -5,632 -2,816
exhaust flow -1,295 -0,6475
Air/Fuel Ratio 3,076 1,538
lip*collector angle -5,797 -2,898
lip*distance to muffler 2,647 1,323
lip*hose diameter 1,7554 0,8777
lip*distance to first curve 2,25 1,125
lip*exhaust flow -2,638 -1,319
lip*Air/Fuel Ratio -2,623 -1,311
collector angle*hose diameter 1,4351 0,7176
lip*collector angle*hose diameter -4,245 -2,123
From Table 17 and the regression equation below, it can be seen that the coefficient of all
the major factors are negative which means that the increase in these factors will lead to a
reduction in the conversion rate.
𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑙𝑒𝑣𝑒𝑙 = 91,45 + 2,196 𝑙𝑖𝑝 + 2,037 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 − 2,745 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑚𝑢𝑓𝑓𝑙𝑒𝑟
− 2,091 ℎ𝑜𝑠𝑒 𝑑𝑖𝑎 − 2,816 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑓𝑖𝑟𝑠𝑡 𝑐𝑢𝑟𝑣𝑒 − 0,6475 𝑒𝑥ℎ 𝑓𝑙𝑜𝑤
+ 1,538 𝐴𝑖𝑟\𝐹𝑢𝑒𝑙 𝑅𝑎𝑡𝑖𝑜 − 2,898 𝑙𝑖𝑝 ∗ 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 + 1,323 𝑙𝑖𝑝
∗ 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑚𝑢𝑓𝑓𝑙𝑒𝑟 + 0,8777 𝑙𝑖𝑝 ∗ ℎ𝑜𝑠𝑒 𝑑𝑖𝑎 + 1,125 𝑙𝑖𝑝 ∗ 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑓𝑖𝑟𝑠𝑡 𝑐𝑢𝑟𝑣𝑒
− 1,319 𝑙𝑖𝑝 ∗ 𝑒𝑥ℎ 𝑓𝑙𝑜𝑤 − 1,311 𝑙𝑖𝑝 ∗ 𝐴𝑖𝑟\𝐹𝑢𝑒𝑙 𝑅𝑎𝑡𝑖𝑜 + 0,7176 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 ∗ ℎ𝑜𝑠𝑒 𝑑𝑖𝑎 − 2,123 𝑙𝑖𝑝 ∗ 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 ∗ ℎ𝑜𝑠𝑒 𝑑𝑖𝑎
(34)
Findings and analysis
50
4.1.3 Coil ON/OFF
While optimizing the performance of the catalytic converter the running cost of the test
runs has to be considered, in this case the main consumer of electricity while running the
test is the heating coil installed in the test rig. From Figure 32 it can be seen that the flow
in the exhaust duct is a major factor to the duration of coil running time.
Table 18 : Effect of each parameter and their coefficient
Term Effect Coefficient
Constant 58,73
lip -8,24 -4,12
collector angle -13,998 -6,999
distance to muffler 15,331 7,666
hose diameter 18,09 9,045
distance to first curve 7,87 3,935
exhaust flow 54,52 27,26
Air/Fuel Ratio 1,4748 0,7374
lip*collector angle -17,457 -8,728
lip*distance to muffler 27,27 13,63
lip*hose diameter 11,003 5,502
lip*distance to first curve -0,03983 -0,01991
lip*exhaust flow -15,08 -7,54
lip*Air/Fuel Ratio -6,684 -3,342
collector angle*hose diameter 9,797 4,899
lip*collector angle*hose diameter -1,3162 -0,6581
Figure 32 Pareto Chart for Coil on/off
Findings and analysis
51
From Table 18 and the regression equation of coil running time, it can be understood that
the increase in flow will increase the heating coil running duration. The flow has to be set
as a boundary condition while setting the parameter in the test rig while running the tests
to minimize the power consumption.
𝐶𝑜𝑖𝑙 𝑂𝑁/𝑂𝐹𝐹 = 58,73 − 4,120 𝑙𝑖𝑝 − 6,999 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 + 7,666 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑚𝑢𝑓𝑓𝑙𝑒𝑟
+ 9,045 ℎ𝑜𝑠𝑒 𝑑𝑖𝑎 + 3,935 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑓𝑖𝑟𝑠𝑡 𝑐𝑢𝑟𝑣𝑒 + 27,26 𝑒𝑥ℎ 𝑓𝑙𝑜𝑤
+ 0,7374 𝐴𝑖𝑟\𝐹𝑢𝑒𝑙 𝑅𝑎𝑡𝑖𝑜 − 8,728 𝑙𝑖𝑝 ∗ 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 + 13,63 𝑙𝑖𝑝
∗ 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑚𝑢𝑓𝑓𝑙𝑒 + 5,502 𝑙𝑖𝑝 ∗ ℎ𝑜𝑠𝑒 𝑑𝑖𝑎 − 0,01991 𝑙𝑖𝑝
∗ 𝑑𝑖𝑠𝑡 𝑡𝑜 𝑓𝑖𝑟𝑠𝑡 𝑐𝑢𝑟𝑣𝑒 − 7,540 𝑙𝑖𝑝 ∗ 𝑒𝑥ℎ 𝑓𝑙𝑜𝑤 − 3,342 𝑙𝑖𝑝 ∗ 𝐴𝑖𝑟\𝐹𝑢𝑒𝑙 𝑅𝑎𝑡𝑖𝑜
+ 4,899 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 ∗ ℎ𝑜𝑠𝑒 𝑑𝑖𝑎 − 0,6581 𝑙𝑖𝑝 ∗ 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑛𝑔𝑙𝑒 ∗ ℎ𝑜𝑠𝑒 𝑑𝑖𝑎
(35)
Findings and analysis
52
4.2 Results from SEM analysis
SEM analysis is carried out to get the variation in chemical composition and the molecular
structure of the wash coat of the aged and new catalyst.
4.2.1 Chemical composition With Energy Dispersive Spectroscopy (EDS) the chemical composition on the catalytic
converter is analyzed on the samples and the results are obtained as X-ray spectra. The
weight percentage if each of the elements are shown in Appendix 6:
Figure 33, shows the chemical composition on the wash coat of the new catalyst obtained by EDS in SEM which will be used as reference to analyze the variation of the chemical composition in old catalysts. It can be observed that there is a considerable volume of phosphorus in the new catalyst which will act as a poison in the catalyst deactivation.
Figure 34 shows the chemical composition in the aged small catalyst. It can be observed that
except for the small volume of Sulphur, there are no other foreign chemicals which act as a
0 2 4 6 8 10
keV
0
5
10
15
20
25
cps/eV
C O Mg Al P Cr
Cr
Fe
Fe
Pd Pd
Pd
Ce Ce
Ce
Figure 33 : EDS graph of new catalyst
Figure 34 EDS graph of aged small catalyst
Findings and analysis
53
poison in the catalytic deactivation. Phosphorus was present on the catalyst even before being
used in the test rig which can be seen from Figure 33.
From Figure 35 and Figure 36, it can be seen that except for calcium there are no chemicals
which can act as a poison in the deactivation of the catalyst are present on the inner and
outer surface of the big catalyst used in the chain saw test rig.
0 2 4 6 8 10
keV
0
5
10
15
20
25
cps/eV
C O Na Al Si Ca
Ca
Cr
Cr
Fe
Fe
Zr Zr
Zr
Pd Pd
Pd
Te
Te
Te
Ce Ce
Ce
0 2 4 6 8 10
keV
0
5
10
15
20
25
cps/eV
C O Na
Mg
Al Si Ca
Ca
Cr
Cr
Fe
Fe
Zr Zr
Zr
Ag Ag
Ag
Ba Ba
Ba
Ce Ce
Ce
Figure 35 : EDS graph of aged Catalyst - Inner region
Figure 36 : EDS graph of aged catalyst - Outer region
Findings and analysis
54
Figure 37 displays the surface of the wash coat of the aged catalyst without backscattering
in SEM. The white layer over the wash coat is the organic compounds accumulated over the
wash coat. These organic compound will lead to the reduction the porosity and in turn it
will reduce the efficiency of the catalytic converter.
4.2.2 Microstructure analysis on wash coat
The variation in the micro structure of the wash coat on the catalyst is studied by means of
backscattering image of the wash coat using SEM.
Figure 38 (b) shows the microstructure of the aged catalyst. It can be seen that there is
major depletion of the wash coat layer on the aged catalyst which is due to both thermal
degradation and sintering effect and attrition and crushing of catalyst layer compare to the
microstructure of the wash coat in new catalyst shown in Figure 38(a). In Figure 38(b), it
can be observed that there are large white dots which are highlighted in red circles. These
Figure 37 : SEM image of the aged catalyst
Figure 38 : Backscattering SEM micrograph of the catalyst wash coat (a) New Catalyst and (b) Aged Catalyst
Findings and analysis
55
are the precious metals which are formed due to the agglomeration of smaller precious
metal particles to larger ones of size ranging from 150 to 250 Nano meter thereby reducing
the ratio between the area and volume and hence reducing the reaction surface area.
Figure 39 displays the variation in the depletion of the wash coat layer over the catalyst
from the outer surface of the catalyst to the inner surface of the catalyst. In Figure 39(a), it
can be seen that the wash coat is getting depleted more on the inner surface compared to
the one in the outer surface which can be seen in Figure 39(b). This is due to the non-
uniformly distributed flow of the exhaust from the small catalyst to the big one. As a result
of the non- uniform flow, a large part of the reaction in the catalyst is happening the middle.
This will lead to the reduction of both the efficiency and life time of the catalyst. This result
is backed by (G. Bella, 1991)
Figure 39 : Backscattering SEM micrograph of the aged catalyst wash coat (a) Inner region and (b) Outer region
Discussions and conclusions
56
5. Discussion and conclusions
5.1 Discussion of method
Figure 40 shows the process carried out in this thesis work to find the parameters that are
responsible for the performance of the catalytic converter in the test rig. Initially to
understand the test rig and the conditions which seem to affect it and to list out the factors
which are in the interest to be tested, several brainstorming sessions were conducted.
Initial test runs were conducted to check the reliability of the test measurement system and
to determine the required duration needed to perform the DOE experiment.
During the brainstorming sessions several factors were suggested for the test but regarding
the time constraint only a set of major factors were selected for the test. In order to identify
the factors with large response a screening design experiment using DOE was planned.
With the total of 7 factors that are decided to be tested and the total runs needed were 128
which would require a huge investment of time and hence a reduced factorial design with
16 runs was designed using Minitab. But during the DOE test runs, the experiment is
supposed to be done twice for a more reliable result but due to the issue with the
maintenance of FID, it was once done only once.
Figure 40 : Process of this Thesis work
Discussions and conclusions
57
After performing the DOE test, the results are then added in the Minitab to check for the
statistical analysis of the data collected from the test.
Catalyst deactivation is another factor which was brought up during the brainstorming
session. A theoretical study about the effect of lubricating oil, fuel, metal/dirt accumulation
onto the catalyst structure was done. An SEM analysis was done on the catalytic converter
and got to the conclusion by taking the results from the theoretical analysis into account.
The results from the analysis of the aged catalyst is compared with the analysis of the new
catalyst.
5.2 Discussion of findings
The results from the thesis are discussed here. The DOE test results are discussed first
followed by the SEM analysis results.
5.2.1 DOE test results
The standard deviation obtained after the initial test runs to check the reliability of the
measurement system is shown in Table 10 : Initial test analysis from which confirms that
the test system is reliable. But as mentioned in section 5.1 the reliability of the results will
be affected due to the lack of the repetition of the test runs of the DOE plan.
From the statistical results using Minitab the factors and its effects on the degree of
collection and conversion are obtained.
The collector diameter, collector distance to the muffler, exhaust flow and the interaction
effect of collector diameter with respect to exhaust flow and collector lip with respect to the
collector angle shows higher response regarding the degree of collection.
In the case of degree of conversion distance of the first curve (collector length), distance to
the muffler and the interaction effect of collector lip with respect to collector angle.
5.2.2 SEM test results
After analyzing the samples from the catalyst in SEM the reason for the catalytic
deactivation are obtained,
From the EDS results it can be see that the weight percentage of the chemicals which
are acting as a poison in the catalyst deactivation which are discussed in literature are
negligible.
Figure 37 shows the microstructure of the wash coat in SEM without backscattering.
This image reveals a layer of organic compounds over the wash coat which is due to the
Fouling, coking or carbon deposition on the catalyst.
From Figure 38, it can be observed that there is depletion of wash coat layer and also
the agglomeration or precious metal particles. Which are due to thermal degradation
and Attrition or crushing of catalyst.
Figure 39 shows the variation in the wash coat layer depletion on the catalyst and it can
be seen that the inner layer is much more depleted compared to the outer layer which
is due to the uneven flow of exhaust into the catalyst surface.
Discussions and conclusions
58
5.3 Conclusions
What are the parameters affecting the collection and conversion rate of a catalytic
converter in 2 stroke handheld equipment in the test rig?
Based on the results from the DOE the collector diameter, collector distance to the muffler,
exhaust flow and the interaction effect of collector diameter with respect to exhaust flow
and collector lip with respect to the collector angle shows higher response regarding the
degree of collection.
In the case of degree of conversion distance of the first curve (collector length), distance to
the muffler and the interaction effect of collector lip with respect to collector angle. The
exhaust flow is affecting the heating coil running duration.
From the SEM analysis results it can be concluded that the chemical poisoning on the
catalytic converter is minimal, while the effect of fouling on catalytic converter is
considerable. Another reason for the deactivation of catalyst is the high operating
temperature which is leading to the depletion of the wash coat layer and agglomeration of
precious metal particles. From the analysis of the variation on the catalyst surface on the
inner and outer region of catalyst, it is also observed that there is an uneven flow
distribution of exhaust on the catalyst surface.
How are these parameters influencing the catalyst performance in the test rig?
Degree of collection
From the equation it can be observed that the increase in the distance to muffler and hose
diameter/collector diameter will reduce the degree of collection while the increase in
exhaust flow and the interactions lip*collector angle and collector diameter* exhaust flow
will increase the degree of collection.
Conversion level
From the equation it can be observed that the increase in the distance to muffler and
distance to first curve (collector length) will reduce the conversion level. The increase in the
interaction lip*collector angle will also lead to the reduction in conversion of exhaust.
But while considering all the parameters the flow in the exhaust has to be considered as a
boundary condition to reduce the duration of the running of the heating coil which will
increase the running cost.
SEM
From section 2.2.4 Catalyst Deactivation it is known that the catalytic deactivation will lead
to the reduction of conversion level. High operating temperature and fouling are
responsible for the deactivation of the catalyst. The small catalyst in the test rig is installed
in a way that the flow from it is concentrated on to the center region of the big catalyst which
is observed from the comparison of wash coat depletion in the outer and inner region Figure
39, due to which the efficiency of the catalyst is reduced and at the same time the
deactivation process is also accelerated.
Discussions and conclusions
59
5.4 Future work
Some suggestion for further study on the development of the system are listed below,
The experiments from the DOE plan has to be repeated to get a more reliable result
from the experiment
Full factorial experiment using the parameters from the test results has to be
carried out to get the effects of each parameter on degree of collection and
conversion.
Optimal design of collectors can be made to check for the effect of it on degree of
collection and conversion.
More SEM analysis on catalytic converters which are run under controlled
parameters can be done to see the effects more precisely.
Tests can be done to study the effects of uniform flow on the catalyst temperature,
conversion rate and life time of the catalyst.
Based on the tests on the uniform flow the box in which the catalysts are installed
has to be redesigned.
The temperature distribution over center to the outer surface has to be considered
while setting the temperature for the feedback on heating coil.
Experiment has to be conducted to study the particles in the exhaust leading to the
Attrition or crushing of catalyst.
References
60
6. References
1939-, A. G. (2013). A Dictionary of Mechanical Engineering. Oxford : Oxford University Press. Avneet Kahlon, T. T. (2015, November 16). Catalytic Converters. Retrieved from
https://chem.libretexts.org/Core/Physical_and_Theoretical_Chemistry/Kinetics/Case_Studies%3A_Kinetics/Catalytic_Converters
Bartholomew, C. H. (2001). Mechanisms of catalyst deactivation. Applied Catalysis A: General,
17-60. Blair, G. P. (1996). Design and simulation of two-stroke engines. Society of Automotive
Engineers, Inc. C.Runger, D. C. (2002). Applied Statistics and Probability for Engineers Third Edition. Jonh
Wiley & sons,Inc. (January 2009). Emission Control of SmallSpark-Ignited Off-Road Engines and Equipment.
Washington: Manufacturers of Emission Controls Association. Engineering Statistics Handbook. (2013, 10 30). Retrieved from
http://www.itl.nist.gov/div898//handbook/pri/section3/pri333.htm Experimental Design & Analysis Reference. (2015, April 29). Retrieved from
http://www.synthesisplatform.net/references/Experiment_Design_and_Analysis_Reference.pdf
G. Bella, V. R. (1991). A Study of Inlet Flow Distortion Effects on Automotive Catalytic
Converters. Journal of Engineering for Gas Turbines and Power, 419-426. Ginger Holmes Rowell, P. D. (2004). Introduction to Minitab. MTSU. Hakan Kaleli. (2001). The impact of crankcase oil containing phosphorus on catalytic
converters and engine exhaust. Industrial Lubrication and Tribology, 237-255. Heikkinen Tim & Müller, J. (2015). Multidisciplinary analysis of jet engine components:
Development of methods and tools for design automatisation in a multidisciplinary context.
Hussain, N. (2014). Phosphorous Poisioning and Characterization of Al2O3 Based Support
Material. Helsinki Metropiloa University of Applied Sciences. International, A. (2012). Standard Test Method for Test Method for the Determination of Total
Hydrocarbons in Hydrogen by FID Based Total Hydrocarbon (THC) Analyzer1. McCartney, K. S. (2003). Catalytic Converter Theory, Operation and Testing. Minitab. (2009). Quality Companion 3. Minitab, Inc. (www.minitab.com). Nicholson, J. (2014). The Conise Oxford Dictionary ofMathematics 5th Edition. O’Regan, G. (2016). Guide to Discrete MAthematics. Peters, C. A. (2001). Statistic Analysis of Experimental Data. Environmental Engineering
Processes Laboratory Manual. Rushing, H., Karl, A., & Wisnowski, J. (2013). The 2k Factorial Design. In Design and Analysis
of Experiments by Douglas Montgomery: A Supplement for Using JMP. SAS Institute. Tania Prvan, A. R. (2002). Statistical Laboratories Using Minitab, SPSS and Excel: A Practical
Comparison. Teaching Statistics, 68-75.
References
61
Walker, J. (2017, 06 6 ). Moving Average. Retrieved from Fourmilab: https://www.fourmilab.ch/hackdiet/www/subsection1_2_4_0_4.html
Williamson, K. (2002 ). Research methods for students, academics and professionals :
information management and systems. Wagga Wagga, N.S.W. : Centre for Information Studies, Charles Sturt University .
Appendices
62
7. Appendices
Appendix 1:
Initial list of parameters decided after brain storming session.
Appendix 2:
List of test runs from Minitab.
Appendix 3:
Matlab code for calculating the duration of test runs.
Appendix 4:
Matlab code calculating the degree of collection, conversion, duration of coil on/off time
and quality of flow.
Appendix 5:
Results from the 16 test runs.
Appendix 6:
Weight percentage of materials on the catalyst surface from SEM.
Postadress: Besöksadress: Telefon: Box 1026 Gjuterigatan 5 036-10 10 00 (vx) 551 11 Jönköping
7.1 Appendix 1:
Initial list of parameters decided after brain storming session.
Group Factor that might affect catalytic converter performance
Priority 1 = Must be
tested 2 = Could be tested 3 = Do not
test
Is possible to test
in regular
test cell?
Collector
Collector design 3 x
Collector designed for specific product, yes/no 2 x
Lip or no lip 1 x
Angular vertical displacement of collector, from optimal position 2
x
Angular horizontal displacement of collector, from optimal position 1
x
Distance to muffler 1 x
Diameter of exhaust gas tube 1 x
Distance to first bend of tube 1 x
Cat. Conv.
Degree of dirt in cat. Conv. 1
Position of temperature sensor in cat. Box. 2
Product
Leakage between muffler and cylinder 3 x
Leakage between cylinder and crankcase 1 x
Modified power level 1 x
Specified power in kW 2 x
Specified emission level in g/h 2 x
Muffler, direction of exhaust gas beam 3 x
Muffler, concentration of exhaust gas beam 2 x
Testing conditions
Exhaust gas tube flow [m3/h] 1 x
Flow sensor function (OK/NOK) 3 x
Test cycle 2 x
Leakage from heat exchanger 1
Leakage from cat. Box. 3
Oxygen concentration in exhaust gases 3 ?
Flow in general ventilation [m3/h] 3 x
Level of clutch lubrication x
Test cell temperature 3 ?
Leakage from clutch lubrication system 3
Postadress: Besöksadress: Telefon: Box 1026 Gjuterigatan 5 036-10 10 00 (vx) 551 11 Jönköping
7.2 Appendix 2:
List of test runs from Minitab.
Std
Ord
er
Ru
n O
rder
Ce
nte
rPoin
t
Blo
cks
Co
llecto
r L
ip
Co
llecto
r A
ngle
Co
llecto
r D
ista
nce
to M
uffle
r
Co
llecto
r D
iam
ete
r
nea
r e
xha
ust
Co
llecto
r L
en
gth
Flo
w
Air/F
ue
l R
atio
1 1 1 1 No 0 Optimum 80 Min 80 Min
2 2 1 1 Yes 0 Optimum 80 Max 80 Max
3 3 1 1 No 30 Optimum 80 Max 140 Min
4 4 1 1 Yes 30 Optimum 80 Min 140 Max
5 5 1 1 No 0 Optimum + 50 80 Max 140 Max
6 6 1 1 Yes 0 Optimum + 50 80 Min 140 Min
7 7 1 1 No 30 Optimum + 50 80 Min 80 Max
8 8 1 1 Yes 30 Optimum + 50 80 Max 80 Min
9 9 1 1 No 0 Optimum 120 Min 140 Max
10 10 1 1 Yes 0 Optimum 120 Max 140 Min
11 11 1 1 No 30 Optimum 120 Max 80 Max
12 12 1 1 Yes 30 Optimum 120 Min 80 Min
13 13 1 1 No 0 Optimum + 50 120 Max 80 Min
14 14 1 1 Yes 0 Optimum + 50 120 Min 80 Max
15 15 1 1 No 30 Optimum + 50 120 Min 140 Min
16 16 1 1 Yes 30 Optimum + 50 120 Max 140 Max
Postadress: Besöksadress: Telefon: Box 1026 Gjuterigatan 5 036-10 10 00 (vx) 551 11 Jönköping
7.3 Appendix 3:
Matlab code for calculating the duration of test runs.
%% INITIALIZATION
clear; [fileName,pathname] = uigetfile({'*.xlsx'},'Select Location'); n= input('Enter the number of divisions'); a =strcat(pathname,fileName); A=xlsread(a, 'B:B');
%% CALCULATION PART
m=size(A); m=m(1,1); s=zeros((m-(n-1)),1); for i=n:m for j=(n-1):-1:1 s(i-(n-1),1)= s(i-(n-1),1)+A(i-j);% we're calculating the sum of
each section %which returns an array of sum of each section end end s=s./n;% the floating average
%% RESULTS SHOWN
subplot(2,1,2); plot(s); subplot(2,1,1); plot(A); %Plotting the actual result and the result after applying
floating average
%% INITIALIZATION
p= input('Enter the size of the cut average'); k=size(s); k=k(1,1); n6=1;
%% CALCULATION PART
for i=1:p:(k-p)%assigning the position of Tc ranges from 1 to (k-p) p1=p; if k-i>2*p%if position of Tc is less greater than (k-2*p) n5=0; for j=(p1+i):p:k%assigning position for Tm
sum=0; n1=0; for h=i:j%taking values for cut average sum=sum+s(h,1); n1=n1+1;
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end sum1=0; n2=0; for y=j+1:k%taking values for comparing averages
sum1=sum1+s(y,1); n2=n2+1; end n5=n5+1; avg1(n5,n6)=sum1/n2;%average avg(n5,n6)=sum/n1;%cut average end else break end n6=n6+1;%updating the position of values in the array of averages end diff= avg-avg1;%comparing the averages s= size(diff); s1=s(1,1); s2=s(1,2); u=1; for i=1:s1 for j=1:s2 if diff(i,j)~=0 diff1(1,u)=diff(i,j); u=u+1; end end b=min(abs(diff1)); end
%% RESULTS DISPLAYED
Tc=1+((row-1)*p); Tm=Tc+(p*col)-1;
Postadress: Besöksadress: Telefon: Box 1026 Gjuterigatan 5 036-10 10 00 (vx) 551 11 Jönköping
7.4 Appendix 4:
Matlab code calculating the degree of collection, conversion, duration of coil on/off time
and quality of flow.
%% INITIALIZATION
X= input('Enter the maximum value for general ventilatiion HC'); Y= input('Enter the number of the experiment the data is from'); avgflow_GV= input('Enter the flow for general ventilatiion HC'); genflow=input('Enter the set value for flow in the exhaust duct'); h=msgbox('Selcet the excel for after catalyst');
pause(3); close(h); [fileName,pathname] = uigetfile({'*.xlsx'},'Select Location'); aftercatalyst =strcat(pathname,fileName); b=msgbox('Selcet the excel for before catalyst');
pause(3); close(b); [fileName,pathname] = uigetfile({'*.xlsx'},'Select Location'); beforecatalyst =strcat(pathname,fileName); c=msgbox('Selcet the excel for general ventilation');
pause(3); close(c); [fileName,pathname] = uigetfile({'*.xlsx'},'Select Location'); generalventelation =strcat(pathname,fileName);
n1= coiltemp(aftercatalyst); %coiltemp is the function which will
determine the duration heating on time
fprintf('The percentage of time the coil was on during the reading in
after catalyst is %d .\n',n1); m1= katflow(aftercatalyst,genflow); %katflow is the function that
determine the quality of the flow in the exhaust duct
fprintf('The quality of flow in the exhaust duct during the measurement
in after catalyst is %d .\n',m1);
n2= coiltemp(beforecatalyst); fprintf('The percentage of time the coil was on during the reading in
before catalyst is %d .\n',n2); m2= katflow(beforecatalyst,genflow); fprintf('The quality of flow in the exhaust duct during the measurement
in before catalyst is %d .\n',m2);
n3= coiltemp(generalventelation); fprintf('The percentage of time the coil was on during the reading in
general ventilation is %d .\n',n3); m3= katflow(generalventelation,genflow); fprintf('The quality of flow in the exhaust duct during the measurement
in general ventilation is %d .\n',m3);
Postadress: Besöksadress: Telefon: Box 1026 Gjuterigatan 5 036-10 10 00 (vx) 551 11 Jönköping
%% READING THE EXCEL SHEET
HCA=xlsread(aftercatalyst, 'C600:C1800');%HC rate after catalyst HCB=xlsread(beforecatalyst, 'C300:C1200');%HC rate before catalyst HCV=xlsread(generalventelation, 'C180:C1200');%HC rate in ventilation flow_E=xlsread(beforecatalyst, 'D:D');%flow of exhaust
%% CALCULATION PART
i=size(HCV); i=i(1,1); sumHCV=0; M=0; for n=1:i%for avoiding the higher values from the fid value of general
ventilation if HCV(n,1)<X sumHCV = sumHCV+ HCV(n,1); M=M+1; end end
avgHCV= sumHCV/M; avgHCA= mean(HCA); avgHCB= mean(HCB); j=size(flow_E); j=j(1,1); m=0; sum_flow_E=0; for n=1:j %for avoiding zeros from the flow measurement in the exhaust
duct if flow_E(n,1)>0 if flow_E(n,1)<200 sum_flow_E=sum_flow_E+ flow_E(n,1); m=m+1; end end end
avgflow_E= sum_flow_E/m; ConversionLevel=((avgHCB-avgHCA)/avgHCB)*100; degree_of_collection=
((avgHCB*avgflow_E)/((avgHCB*avgflow_E)+(avgHCV*avgflow_GV)))*100;
%% RESULTS SHOWN
fprintf('the ConversionLevel is %d .\n',ConversionLevel); fprintf('the degree of collection is %d .\n',degree_of_collection); Y=Y+1; p1=te('L',Y);p2=te('M',Y);
p3=te('N',Y);p4=te('O',Y);p5=te('P',Y);p6=te('Q',Y);p7=te('R',Y);p8=t
e('S',Y);
%% WRITE RESULTS IN EXCEL SHEET
Postadress: Besöksadress: Telefon: Box 1026 Gjuterigatan 5 036-10 10 00 (vx) 551 11 Jönköping
filename = 'Results.xlsx'; xlswrite(filename,ConversionLevel,1,p1) xlswrite(filename,degree_of_collection,1,p2) xlswrite(filename,n1,1,p3) xlswrite(filename,n2,1,p4) xlswrite(filename,n3,1,p5) xlswrite(filename,m1,1,p6) xlswrite(filename,m2,1,p7) xlswrite(filename,m3,1,p8) disp('The quality of flow is show in numbers which means 1= perfect
flow 0= flow goes to zero in between 2= 60% of is less than 60% of the
set value');
%% COIL FUNCTION DEFINITION
function x= coiltemp(b) a=xlsread(b, 'B:B'); n=size(a); n=n(1,1); m=0; u=0; for i=1:n if a(i,1) == 1 m=m+1; end u=u+1; end x = (m/u)*100;
%% CATFLOW FUNCTION
function o= katflow(h,f) a=xlsread(h, 'D:D'); n=size(a); n=n(1,1); m=0; for i=1:n if a(i,1)==0%checking the if the values are zero somewhere m=m+1;%counting the number of zeroes end end if m>=1%if there are zeroes o=0; else y=0; u=0; for j=1:n if a(j,1)<((f/100)*60)%checking if flow is less than 60 y=y+1;%counting number of data less than 60 end u=u+1;%counting total number of data end perc=(y/u)*100; if perc>60%checking whether more than 60% of value is less than 60
Postadress: Besöksadress: Telefon: Box 1026 Gjuterigatan 5 036-10 10 00 (vx) 551 11 Jönköping
o=2; else o=1;%virtually perfect flow end end
Postadress: Besöksadress: Telefon: Box 1026 Gjuterigatan 5 036-10 10 00 (vx) 551 11 Jönköping
7.5 Appendix 5:
Results from the 16 test runs.
N
o o
f te
st ru
n
Co
nve
rsio
n leve
l
De
gre
e o
f co
llection
perc
enta
ge o
f tim
e th
e c
oil
wa
s o
n (
aft
er
ca
taly
st)
perc
enta
ge o
f tim
e th
e c
oil
wa
s o
n (
Befo
re c
ata
lyst)
perc
enta
ge o
f tim
e th
e c
oil
wa
s o
n (
gen
era
l ve
ntila
tion
)
qua
lity o
f flo
w (
afte
r ca
taly
st)
qua
lity o
f flo
w (
befo
re c
ata
lyst)
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1 94,61 97,49 23,28 26,59 28,95 0,00 0,00 1,00
2 96,24 97,06 9,31 16,71 34,95 0,00 0,00 0,00
3 92,27 98,36 91,42 96,95 100,00 1,00 1,00 1,00
4 96,77 97,88 5,64 6,91 23,43 0,00 0,00 0,00
5 85,64 98,50 100,00 100,00 100,00 1,00 1,00 1,00
6 92,39 92,25 99,57 99,66 100,00 1,00 1,00 1,00
7 96,37 93,45 10,04 14,06 20,42 0,00 0,00 0,00
8 94,03 98,10 27,34 22,46 34,80 0,00 0,00 0,00
9 90,04 98,30 100,00 100,00 100,00 1,00 1,00 1,00
10 92,23 98,56 83,89 79,98 92,88 1,00 1,00 1,00
11 96,36 91,93 38,20 37,08 83,37 0,00 0,00 0,00
12 95,02 94,53 14,05 11,36 20,62 1,00 0,00 1,00
13 66,98 90,68 18,15 19,52 16,98 1,00 1,00 1,00
14 97,16 85,64 67,55 62,52 97,05 0,00 0,00 0,00
15 91,75 98,23 91,26 92,83 99,34 1,00 1,00 1,00
16 85,31 97,79 100,00 100,00 100,00 1,00 1,00 1,00
Postadress: Besöksadress: Telefon: Box 1026 Gjuterigatan 5 036-10 10 00 (vx) 551 11 Jönköping
7.6 Appendix 6:
Weight percentage of materials on the catalyst surface from SEM. New catalyst: aged small catalyst: El AN Series Net unn. C norm. C Atom. C Error (1 Sigma)
[wt.%] [wt.%] [at.%] [wt.%]
------------------------------------------------------------
C 6 K-series 7 0,01 0,01 0,02 0,05
O 8 K-series 162090 46,15 43,41 61,19 6,07
Al 13 K-series 522878 45,92 43,19 36,11 2,22
P 15 K-series 1816 0,20 0,19 0,14 0,03
S 16 K-series 924 0,09 0,08 0,06 0,03
Fe 26 K-series 4308 0,96 0,91 0,37 0,06
Pd 46 L-series 23171 3,13 2,95 0,62 0,13
Ce 58 L-series 47691 9,86 9,27 1,49 0,30
------------------------------------------------------------
Total: 106,32 100,00 100,00
El AN Series Net unn. C norm. C Atom. C Error (1 Sigma)
[wt.%] [wt.%] [at.%] [wt.%]
------------------------------------------------------------
C 6 K-series 37 0,03 0,04 0,07 0,09
O 8 K-series 253785 36,62 42,76 60,11 5,94
Mg 12 K-series 4646 0,22 0,26 0,24 0,04
Al 13 K-series 963195 37,81 44,15 36,80 1,83
P 15 K-series 4728 0,25 0,29 0,21 0,04
Cr 24 K-series 4216 0,35 0,41 0,18 0,06
Fe 26 K-series 7440 0,86 1,00 0,40 0,05
Pd 46 L-series 47135 3,42 3,99 0,84 0,14
Ce 58 L-series 57533 6,08 7,10 1,14 0,19
------------------------------------------------------------
Total: 85,63 100,00 100,00
Postadress: Besöksadress: Telefon: Box 1026 Gjuterigatan 5 036-10 10 00 (vx) 551 11 Jönköping
Inner surface of aged big catalyst: Outer surface of aged big catalyst:
El AN Series Net unn. C norm. C Atom. C Error (1 Sigma)
[wt.%] [wt.%] [at.%] [wt.%]
------------------------------------------------------------
C 6 K-series 4374 4,50 5,25 10,72 0,85
O 8 K-series 128002 29,25 34,17 52,35 3,57
Na 11 K-series 2912 0,33 0,39 0,42 0,10
Mg 12 K-series 1874 0,15 0,17 0,17 0,03
Al 13 K-series 363931 22,84 26,68 24,24 1,12
Si 14 K-series 75791 4,52 5,28 4,61 0,22
Ca 20 K-series 31434 2,47 2,89 1,76 0,10
Cr 24 K-series 9211 1,06 1,24 0,59 0,12
Fe 26 K-series 9269 1,49 1,74 0,77 0,07
Zr 40 L-series 39156 4,33 5,06 1,36 0,19
Ag 47 L-series 3320 0,35 0,41 0,09 0,04
Ba 56 L-series 4930 0,67 0,78 0,14 0,19
Ce 58 L-series 93053 13,63 15,93 2,79 0,88
------------------------------------------------------------
Total: 85,59 100,00 100,00
El AN Series Net unn. C norm. C Atom. C Error (1 Sigma)
[wt.%] [wt.%] [at.%] [wt.%]
------------------------------------------------------------
C 6 K-series 1364 3,06 3,64 7,61 2,98
O 8 K-series 56273 29,50 35,11 55,11 6,84
Na 11 K-series 548 0,17 0,20 0,22 0,04
Al 13 K-series 126263 20,24 24,10 22,43 0,99
Si 14 K-series 30013 4,72 5,61 5,02 0,23
Ca 20 K-series 20116 3,92 4,67 2,93 0,16
Cr 24 K-series 6070 1,46 1,74 0,84 0,13
Fe 26 K-series 8101 2,75 3,27 1,47 0,11
Zr 40 L-series 10941 3,47 4,13 1,14 0,17
Pd 46 L-series 3450 1,06 1,26 0,30 0,08
Te 52 L-series 5861 1,68 2,00 0,39 0,31
Ce 58 L-series 39340 11,99 14,27 2,56 2,68
------------------------------------------------------------
Total: 84,00 100,00 100,00