1. International Journal of JOURNAL OF MECHANICAL ENGINEERING AND
INTERNATIONAL Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue(IJMET) (2012) © IAEME
TECHNOLOGY 3, Sep- Dec
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online) IJMET
Volume 3, Issue 3, Septmebr - December (2012), pp. 57-66
© IAEME: www.iaeme.com/ijmet.html
Journal Impact Factor (2012): 3.8071 (Calculated by GISI) ©IAEME
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OPTIMIZING CHEMICAL PROCESS THROUGH ROBUST
TAGUCHI DESIGN: A CASE STUDY
Sachin Modgil1, Vishal Singh Patyal2, Koilakuntla Maddulety3,4Padmavati Ekkuluri
1
Research Scholar, 2Research Scholar, 3Assistant Professor,4Research Scholar
123
National Institute of Industrial Engineering (NITIE), Mumbai, India 400087
4
K.N Modi University Rajasthan, India, 304021
1
sachin1115@nitie.edu, 2vishalsp1115@nitie.edu , 3koila@nitie.edu,
4
padmavathi9999@rediffmail.com
ABSTRACT
The aim of this study is to design process optimization for chemical process through robust
Taguchi design to identify the best parameter setting for purity maximization of chemical
‘X’.
In this study author has taken four factors each at three levels with a nuisance factor with
three levels, for maximization of ‘purity percentage’ at two stages of design and analyses.
The means (purity-percentage) ‘signal to noise ratio’ and standard deviation are predicted for
optimal setting and validated by producing 15 batches of inorganic chemical ‘X’ with
optimal setting.
Finding of the study reveals that breakthrough improvement can be achieved depending upon
the customer orientation viz. when customer is interested in average of lot/batch purity or
minimum batch to batch variation in purity, then customer will opt means (average) and
signal to noise ratio (batch to batch variation) respectively.
Key Words:Factors, Factor Levels, Main-Effect-plots-for-Means, Main-Effect-Plots-for-SN
Ratio, Robust Design.
1. INTRODUCTION
1.1 Brief Profile of XYZ Ltd.
Company XYZ is in the manufacturing of alfa (assumed name), which has applications
invarious industries, like polymer, textiles and pharmaceuticals. One of the alfaproducts is
Chemical X. Chemical X is used as printing agent for synthetic fibers, as a catalyst for
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2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME
emulsion in polymerization process and as stabilizer agent for pharmaceutical bulk
formulations.
1.2 Literature Review
Taguchi technique is step by step approach to identify causal relationship between design
factors and performance, which results in enhanced quality performance into processes and
products at development as well as production level. Taguchi’s technique used by a numerous
industries to optimize their process design, through identifying independent and dependent
variables with the help of identified factors and factor levels.
DoE (Design of Experiment) is an approach that facilitates analytically alters in number of
inputs and output variables and examines the impact on response variables. The authors like
Taguchi and Wu [1]; Taguchi [2]; Ross [3] discovered analytical techniques to design highly
efficient and cost effective experiments.
The foundation of Taguchi's philosophy is the loss function concept. "…The quality of a
product is the (minimum) loss imparted by the product to society from the time the product is
shipped…" [4].The main reason behind loss is not only non–conformance of products, rather
loss increases further if one of the parameter deviates from specification (objective value/
reading/ degree).
Quality should be implantedto products. The author also pointed that quality is best
accomplished by increasing accuracy and the cost of quality should be calculated as a
function of the divergence from the desired specifications. Therobust design concept given by
Taguchi can be realized with DoE. This design refers to design aprocess or a product in a way
that it has minimal sensitivity to the external nuisance factors [5].
Klien, I.E [6] has emphasized the importance signal-to-noise ratio analyses which was given
by Taguchi to develop a design for Rayleigh surface acoustic wave (SAW) gas sensing
device operated in a conservative delay-line configuration. Recently Chen [7] calculated
signal-to-noise ratio on the basis of ANOVA.In this paper author has used 10 step
methodology as mention by koilakuntla [8] for deploying robust Taguchi design in process
optimization of a molding operation by using MINITAB.
1.3 The Problem Statement
The problem faced by XYZLimited Company was low purity of chemical X at product
development level.
2. METHODOLOGY SELECTED FOR SOLVING ABOVE PROBLEM
Methodology for deploying Robust Taguchi approach for process optimization (10 step
methodology for problem solving)
1. Defining the statement of problem
2. Determination of the objectives
3. Ensuring correctness of measurement system
4. Identification of chemical X quality characteristics that are to be optimized
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3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME
5. Identification of the controllable and noise factors that are influencing the above
identified performance characteristics and determination of the levels and values for
all identified controllable and noise factors
6. Developing ‘Design for Experimentation (DoE)’ with the help of Minitab Software
7. Conducting the experiments as per designs, analyzing the chemical product produced
as per designed experiments for selected quality characteristics and posting the values
in Minitab worksheet as needed
8. Analysis of data of chemical X for selected quality characteristics by Taguchi
approach with the help of Minitab software and interpretation of analyses and
selection of the optimum levels of the significant factors
9. Prediction of the expected results for optimal setting with the help of Minitab
10. Validation of optimal setting by a confirmation Trails.
2.1 Step 1: Statement of the Problem
The problem faced by XYZLimited Company was low purity percentage of Chemical
X at product development level. .
2.2 Step 2: Objectives of Study
• Acquiring knowledge of deployment of robust Taguchi approach for solving problem
• Deploying the robust Taguchi approach at problem area systematically in 10 steps as
above
• Ensuring maximization ofpurity %by optimum setting of input parameters
2.3 Step 3: Measurement System Analyses
Gauge R&R calculated for all applicable measurement-systems of purity percentage
maximization and found it is well within limits.
2.4 Step 4: Identification of chemical X Quality Characteristics ‘Y’ that is to be optimized
The brainstorming technique was used by involving all the concerned employees and
executives and decided to optimize ‘purity percentage’ of chemical X.
2.5 Step 5: Identification of the controllable Noise factors and factor levels that are
influencing Purity percentage.
After application of brainstorming technique with all the concerned employees and
executives and after establishing cause and effect relations between input- parameters and
output-parameters of purity for chemical X, the most significant four process parameters are
identified as control parameters along with levels as shown in table1 inner array and one
noise factor i.e room temperature with three levels as shown in table1 outer array.
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4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME
Table 1 Factor and factor levels
Outer Array
Inner Array
Sl.N Controlla Levels Noise Factor (Room
o ble Temperature) Ni
Factors N1: 12 N2 = 24 N3=36
0 0 0
1 2 3 C C C
A1 A2 A3
1 A Q. 1 5 9
2 T 15 25 35
3 pH 8.5 9.5 10.5
4 Gpl 120 240 360
Description of Factors, Notation and Unit of Measures
Sl. Name of the Factor Notation Unit of Measure
No.
1 Additive quantity A Q. Kgs
0
2 Reaction Temperature T C
3 Slurry pH pH --
4 Chemical X quantity (gram Gpl gr/lt
per litre )in Slurry
0
5 Noise Factor (Room Ni C
Temperature)
Step 6: Development of Experimentation Design with the help of Minitab Software
The above factors and levels have been used and developed the L9 Robust Taguchi design
for experimentation with the help of Minitab software is shown in table 2
Table 2 Controllable and noise factors and factor levels
Outer Array:
Three readings are taken at three different noise
Inner Array level 120C, 240C & 360C
Noise 120C, 240C 360C
Sl.
No. AQ T pH Gpl A1 A2 A3
1 1 15 8.5 120
2 1 25 9.5 240
3 1 35 10.5 360
4 5 15 9.5 360
5 5 25 10.5 120
6 5 35 8.5 240
7 9 15 10.5 240
8 9 25 8.5 360
9 9 35 9.5 120
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5. 2.6 Step 7: Conducting Experimentation
As per above design each of nine treatments three experiments, one at each noise level are
conducted (9*3*1 = 27) and Chemical X was produced . The chemical X is tested and
percentage of purity is calculated for each combination. For each combination the test for
purity percentage is carried out three times. The calculated value of purity percentage is
posted in Minitab worksheet shown in table 3:
Table 3 L9 Robust Taguchi design for experimentation is developed by minitab software
Outer Array: Three readings are taken
at three different noise level 120C,
Inner Array 240C & 360C
Noise 120C, 240C 360C
Sl. No. AQ T pH Gpl A1 A2 A3
1 1 15 8.5 120 14.37 27.13 19.47
2 1 25 9.5 240 42.45 46.11 47.16
3 1 35 10.5 360 38.01 42.17 39.70
4 5 15 9.5 360 41.15 53.74 42.87
5 5 25 10.5 120 35.60 6.54 16.80
6 5 35 8.5 240 48.14 45.20 42.97
7 9 15 10.5 240 17.11 28.00 21.61
8 9 25 8.5 360 33.57 47.19 42.41
9 9 35 9.5 120 22.99 32.14 31.29
2.7 Step 8: Analyses of data of ‘Chemical X’ for ‘Purity percentage’maximization by
ANOVA and Robust Taguchi Approach with the help of Minitab Software, Interpretation of
Analyses and selection of the optimum levels:
Based on the ‘General Liner Model ANOVA’ developed by Minitab software, shown below
for purity percentage (A1) as response variable for investigating significance effect of four
input variables AQ, T, pH and gpl. From ANOVA table it is concluded that three of four
input variable T, pH, and gpl except AQ (all the p-values are 0.00 i.e. less than0.05) are
significantly affecting the response i.epurity percentage (A1).
The optimal setting as shown in table 4 has been arrived after developing and observing main
effect plots for means shown in graph.1, main effect plots for SN ratios in graph 2, main
effect plots for standard deviation in graph 3, and by considering all the delta values for
means, SN ratios and standard deviations .

6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME
Table 4 Experimental output (when mean is important)
Sl. No. Name of the Factor Notation Optimal Level
1 Additive Quantity AQ 5 kg
2 Temperature T 35 °C
3 pH pH 9.5
4 Chemical X content Gpl 360 gm/ltr
Table 5 Experimental output (when S/N ratio is important)
Sl. No. Name of the Factor Notation Optimal Level
1 Additive Quantity AQ 1 kg
2 Temperature T 35 °C
3 pH pH 9.5
4 Chemical X content Gpl 360 gm/ltr
Table 6 Experimental output (when std. dev. is important)
Sl. No. Name of the Factor Notation Optimal Level
1 Additive Quantity AQ 1 kg
2 Temperature T 35 °C
3 pH pH 9.5
4 Chemical X content Gpl 240gm/ltr
Table 4, Table 5 and Table 6 Optimal setting is arrived after considering main effect plots
and delta values
Optimal input parameter setting is done in two ways. The customer who is more concerned
about the average value of purity percentage of the Chemical X, the optimal settings is as i.e.
Additive Quantity (AQ) is 5 kg, Temp. (T) is 35 °C, pH is 9.5 and gpl is 360 gm/ltr.The
customer who wants batch to batch minimum variation for purity percentage of Chemical X,
the optimal settings are as i.e. Additive Quantity (AQ) is 1 kg, Temp. (T) is 35 °C, pH is 9.5
and gpl is 360 gm/ltr.
(The Minitab generated ANOVA, three main effect plots and delta value for three different
scenarios are shown below in graph 1, graph 2 and graph 3)
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7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME
Main Effects Plot for Means
Data Means
A Q T
45
40
35
30
Mean of Means
25
1 5 9 15 25 35
pH gpl
45
40
35
30
25
8.5 9.5 10.5 120 240 360
Graph 1 Main Effect Plot for Means (Minitab15 Software Output)
Main Effects Plot for SN ratios
Data Means
A Q T
32
30
28
Mean of SN ratios
26
24
1 5 9 15 25 35
pH gpl
32
30
28
26
24
8.5 9.5 10.5 120 240 360
Signal-to-noise: Larger is better
Graph 2 Main Effect Plot for SN Ratios (Minitab15 Software Output)
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8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME
Main Effects Plot for StDevs
Data Means
A Q T
9.0
7.5
6.0
Mean of StDevs
4.5
3.0
1 5 9 15 25 35
pH gpl
9.0
7.5
6.0
4.5
3.0
8.5 9.5 10.5 120 240 360
Graph 3 Main Effect Plot for St Deviation (Minitab15 Software Output)
General Linear Model: A1 versus A Q, T, pH, gpl
Factor TypeLevels Values
A Q fixed 83 1, 5, 9
T fixed 3 15, 25, 35
pH fixed 3 8.5, 9.5, 10.5
gpl fixed 3 120, 240, 360
Analysis of Variance for A1, using Adjusted SS for Tests
Source DFSeq SS Adj SS Adj MS F P
A Q 2 189.11 189.11 94.56 2.00 0.164
T 2 344.87 344.87 172.43 3.65 0.047
pH 2 749.85 749.85 374.93 7.93 0.003
gpl 2 1842.50 1842.50 921.25 19.48 0.000
Error 18 851.04 851.04 47.28
Total 26 3977.36
S = 6.87604 R-Sq = 78.60% R-Sq(adj) = 69.09%
2.8 Step 9: The predicted value for optimal setting has been arrived by Minitab Software
for Purity percentage maximization are as follows:
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9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME
Optimal setting-1: Factor levels for predictionswhen mean (average) is important for
customer
A Q T pH gpl
1 35 9.5 360
Predicted values when mean (average) is important for customer
S/N Ratio Mean StDevLn(StDev)
37.4263 52.6667 -0.557016 0.510943
Optimal setting-2: Factor levels for predictionswhen S/N ratio is important to customer
A Q T pH gpl
5 35 9.5 360
Predicted values when S/N ratio is important to customer
S/N Ratio Mean StDevLn(StDev)
36.1366 54.4933 3.83301 1.19788
2.9 Step 10: Validation of Optimal Setting
20 batches each of 8000 kg produced with the above optimal setting 1 (10 batches) and
optimal setting 2 (10 batches) with slight machine to machine variations as validations trails
with the mentioned optimal parameter setting and proved that all the batches had been
crossed Purity percentage more than 53 %.
3. IMPLICATIONS
The above ten step methodology which is used in this paper can be used for any
manufacturing processes of following industry, automobile, pharmaceuticals, textiles,
chemicals etc. The results are highly specific to chemical manufacturing company, but the
methodology is highly generic, can be used in any manufacturing process.
4. CONCLUSIONS
The Robust Parameter Design through Taguchi Approach has shown a breakthrough
improvement in Purity percentage atXYZ limited company which in-turn ensured a net
saving of Rs. 7, 50,000/- (Total Saving Rs. 800000 – Rs. 50000 Project Cost). The author has
given two robust designs for Chemical X. First one is on the basis of means (should be used
when average is important for customer) and 2nd one is on the basis of S/N ratio (should be
used when batch to batch variation is important for customer).
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10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME
REFERENCES
[1] Taguchi, G., Wu, Y.Introduction to off-line Quality Control. Nagaya, Japan: Central
Japan Quality Control Organization, 1979.
[2] Taguchi, G. Introduction to Quality Engineering. Tokyo, Japan: Asian Productivity
Organization, 1986.
[3] Ross, Philip J., ‘Taguchi techniques for Quality Engineering' Prentice hall, 1989.
[4] Byrne, D. M. and Taguchi, Shin. "The Tamchiapuroach to parameter design."40th
Annual Quality Congress Transactions, 1987.
[5] Montgomery, Douglas C., “Design and Analysis of Experiment”, Wiley edition, 2006.
[6] I.E. Klein, (1996) "Application of Taguchi Methods to the Production of Integrated
Circuits", Microelectronics International, Vol.13, no. 3, pp.12 – 14. Available:
http://www.emeraldinsight.com/journals.htm?articleid=1455588&show=html[Accessed on
1st august, 2012]
[7] Chen, W. C, Tsai, H. C, Lai, T. T.”Optimization of MIMO Plastic Injection Molding
Using DOE, BPNN, and GA.”, 17th (IEEE) International conference on Industrial
Engineering & Engineering Management’, 2010, pp. 676 – 680.
[8] Maddulety, K, Modgil, S,Patyal V.S, "Application of ‘Taguchi Design and Analyses’
for ‘Molding Operation Optimization’," International Conference on Advances in
Engineering, Science and Management (ICAESM), 2012,pp.85-92
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