parameter setting on catalytic controller1109988/fulltext01.pdf · parameter setting on catalytic...

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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


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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)


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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)


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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)


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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


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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.


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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:


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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)


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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


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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


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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)


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 + + +


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,


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.


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


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,


25

Table 5 : Fractional factorial design where ABC interaction is at high level


Factorial Effect


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


Factorial Effect


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:


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


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


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)


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)


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


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.


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


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


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.


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


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.


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


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


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


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


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


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.


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)


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


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.


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


49



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





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)


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.



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





Figure 32 Pareto Chart for Coil on/off


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)


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


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


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


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


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.


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.


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


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


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;


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;


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);


%% 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


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


o=2; else o=1;%virtually perfect flow end end


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)

qua

lity o

f flo

w (

gen

era

l ve

ntila

tion

)

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


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


Inner surface of aged big catalyst: Outer surface of aged big catalyst:


[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


[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

parameter setting on catalytic controller1109988/fulltext01.pdf · parameter setting on catalytic...

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