Optimizing chemical process through robust taguchi design a case study
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  • 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- DecISSN 0976 – 6340 (Print)ISSN 0976 – 6359 (Online) IJMETVolume 3, Issue 3, Septmebr - December (2012), pp. 57-66© IAEME: www.iaeme.com/ijmet.htmlJournal Impact Factor (2012): 3.8071 (Calculated by GISI) ©IAEMEwww.jifactor.com 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 57
  • 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEemulsion in polymerization process and as stabilizer agent for pharmaceutical bulkformulations.1.2 Literature ReviewTaguchi technique is step by step approach to identify causal relationship between designfactors and performance, which results in enhanced quality performance into processes andproducts at development as well as production level. Taguchi’s technique used by a numerousindustries to optimize their process design, through identifying independent and dependentvariables with the help of identified factors and factor levels. DoE (Design of Experiment) is an approach that facilitates analytically alters in number ofinputs and output variables and examines the impact on response variables. The authors likeTaguchi and Wu [1]; Taguchi [2]; Ross [3] discovered analytical techniques to design highlyefficient and cost effective experiments.The foundation of Taguchis philosophy is the loss function concept. "…The quality of aproduct is the (minimum) loss imparted by the product to society from the time the product isshipped…" [4].The main reason behind loss is not only non–conformance of products, ratherloss 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 bestaccomplished by increasing accuracy and the cost of quality should be calculated as afunction of the divergence from the desired specifications. Therobust design concept given byTaguchi can be realized with DoE. This design refers to design aprocess or a product in a waythat 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 givenby Taguchi to develop a design for Rayleigh surface acoustic wave (SAW) gas sensingdevice operated in a conservative delay-line configuration. Recently Chen [7] calculatedsignal-to-noise ratio on the basis of ANOVA.In this paper author has used 10 stepmethodology as mention by koilakuntla [8] for deploying robust Taguchi design in processoptimization of a molding operation by using MINITAB.1.3 The Problem StatementThe problem faced by XYZLimited Company was low purity of chemical X at productdevelopment level.2. METHODOLOGY SELECTED FOR SOLVING ABOVE PROBLEMMethodology for deploying Robust Taguchi approach for process optimization (10 stepmethodology 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 58
  • 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 parameters2.3 Step 3: Measurement System AnalysesGauge R&R calculated for all applicable measurement-systems of purity percentagemaximization and found it is well within limits.2.4 Step 4: Identification of chemical X Quality Characteristics ‘Y’ that is to be optimizedThe brainstorming technique was used by involving all the concerned employees andexecutives and decided to optimize ‘purity percentage’ of chemical X.2.5 Step 5: Identification of the controllable Noise factors and factor levels that areinfluencing Purity percentage.After application of brainstorming technique with all the concerned employees andexecutives and after establishing cause and effect relations between input- parameters andoutput-parameters of purity for chemical X, the most significant four process parameters areidentified as control parameters along with levels as shown in table1 inner array and onenoise factor i.e room temperature with three levels as shown in table1 outer array. 59
  • 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 SoftwareThe above factors and levels have been used and developed the L9 Robust Taguchi designfor 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 360CSl.No. AQ T pH Gpl A1 A2 A31 1 15 8.5 1202 1 25 9.5 2403 1 35 10.5 3604 5 15 9.5 3605 5 25 10.5 1206 5 35 8.5 2407 9 15 10.5 2408 9 25 8.5 3609 9 35 9.5 120 60
  • 5. 2.6 Step 7: Conducting ExperimentationAs per above design each of nine treatments three experiments, one at each noise level areconducted (9*3*1 = 27) and Chemical X was produced . The chemical X is tested andpercentage of purity is calculated for each combination. For each combination the test forpurity percentage is carried out three times. The calculated value of purity percentage isposted 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 31.292.7 Step 8: Analyses of data of ‘Chemical X’ for ‘Purity percentage’maximization byANOVA and Robust Taguchi Approach with the help of Minitab Software, Interpretation ofAnalyses and selection of the optimum levels:Based on the ‘General Liner Model ANOVA’ developed by Minitab software, shown belowfor purity percentage (A1) as response variable for investigating significance effect of fourinput variables AQ, T, pH and gpl. From ANOVA table it is concluded that three of fourinput variable T, pH, and gpl except AQ (all the p-values are 0.00 i.e. less than0.05) aresignificantly affecting the response i.epurity percentage (A1).The optimal setting as shown in table 4 has been arrived after developing and observing maineffect plots for means shown in graph.1, main effect plots for SN ratios in graph 2, maineffect plots for standard deviation in graph 3, and by considering all the delta values formeans, 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/ltrTable 4, Table 5 and Table 6 Optimal setting is arrived after considering main effect plotsand delta valuesOptimal input parameter setting is done in two ways. The customer who is more concernedabout 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.Thecustomer 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 9.5and gpl is 360 gm/ltr.(The Minitab generated ANOVA, three main effect plots and delta value for three differentscenarios are shown below in graph 1, graph 2 and graph 3) 62
  • 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 360Graph 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) 63
  • 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, gplFactor TypeLevels ValuesA Q fixed 83 1, 5, 9T fixed 3 15, 25, 35pH fixed 3 8.5, 9.5, 10.5gpl fixed 3 120, 240, 360Analysis of Variance for A1, using Adjusted SS for TestsSource DFSeq SS Adj SS Adj MS F PA Q 2 189.11 189.11 94.56 2.00 0.164T 2 344.87 344.87 172.43 3.65 0.047pH 2 749.85 749.85 374.93 7.93 0.003gpl 2 1842.50 1842.50 921.25 19.48 0.000Error 18 851.04 851.04 47.28Total 26 3977.36S = 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 Softwarefor Purity percentage maximization are as follows: 64
  • 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEOptimal setting-1: Factor levels for predictionswhen mean (average) is important forcustomerA Q T pH gpl 1 35 9.5 360Predicted values when mean (average) is important for customerS/N Ratio Mean StDevLn(StDev) 37.4263 52.6667 -0.557016 0.510943Optimal setting-2: Factor levels for predictionswhen S/N ratio is important to customerA Q T pH gpl 5 35 9.5 360Predicted values when S/N ratio is important to customerS/N Ratio Mean StDevLn(StDev) 36.1366 54.4933 3.83301 1.197882.9 Step 10: Validation of Optimal Setting20 batches each of 8000 kg produced with the above optimal setting 1 (10 batches) andoptimal setting 2 (10 batches) with slight machine to machine variations as validations trailswith the mentioned optimal parameter setting and proved that all the batches had beencrossed Purity percentage more than 53 %. 3. IMPLICATIONSThe above ten step methodology which is used in this paper can be used for anymanufacturing processes of following industry, automobile, pharmaceuticals, textiles,chemicals etc. The results are highly specific to chemical manufacturing company, but themethodology is highly generic, can be used in any manufacturing process. 4. CONCLUSIONSThe Robust Parameter Design through Taguchi Approach has shown a breakthroughimprovement in Purity percentage atXYZ limited company which in-turn ensured a netsaving of Rs. 7, 50,000/- (Total Saving Rs. 800000 – Rs. 50000 Project Cost). The author hasgiven two robust designs for Chemical X. First one is on the basis of means (should be usedwhen average is important for customer) and 2nd one is on the basis of S/N ratio (should beused when batch to batch variation is important for customer). 65
  • 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEREFERENCES [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 66