Journal of Failure Analysis and Prevention

, Volume 14, Issue 6, pp 772–783 | Cite as

Optimizing Process Parameters of Screw Conveyor (Sugar Mill Boiler) Through Failure Mode and Effect Analysis (FMEA) and Taguchi Method

Technical Article---Peer-Reviewed

Abstract

This paper exhibits the failures of the boiler during the cogeneration process and provides solution to overcome the failures. The failures are frequently occurring in the screw conveyor of fuel-feeding system of the boiler and rarely occurring in the grate of the boiler. In this research work, three important statistical tools are employed to identify and further rectify the failures of the screw conveyor. The different techniques, viz., cause-and-effect diagram, failure mode and effect analysis (FMEA), and the Taguchi method have been applied. The cause-and-effect diagram, is the primary tool used to sort out all the possible root causes of the failures. The process parameters that cause the failures in the screw conveyor are identified by FMEA. Since the conventional FMEA has some limitations, fuzzy FMEA is employed. The most critical parameters selected by conventional FMEA and fuzzy FMEA are fuel type, fuel moisture, drum speed, and air flow. Finally, the selected process parameters are optimized by the Taguchi method to prevent the failures occurring in the screw conveyor. Among the various process parameters, the parameter, fuel type, significantly affects the performance of the screw conveyor.

Keywords

Industrial boiler Ishikawa diagram FMEA Fuzzy FMEA Taguchi method 

List of Symbols

R

Number of tests in a trial

yi

Response value of observation in the ith test

DOE

Design of experiments

ν

Degrees of freedom

νA

Degrees of freedom for factor A

kA

Number of levels for factor A

νrequired

Total degrees of freedom required

νLN

Total degrees of freedom of the available orthogonal array

N

Number of trials

ANOVA

Analysis of variance

M

Overall mean percentage defects

VFactor

Variance of factor

SS

Sum of square

V

Expected amount of variation

P

Percent contribution

μ

Mean

α

Level of risk

νe

Degrees of freedom for the error

Ve

Error variance

F(α, 1, νe)

F ratio required at the level of risk

ηeff

Effective number of replications

n

Total number of experiments

CI

Confidence interval

T

Average values of defects at different levels

References

  1. 1.
    A.M. Ghosh, M. Balakrishnan, Pilot demonstration of sugarcane juice ultrafiltration in an Indian sugar factory. J. Food Eng. 58, 143–150 (2003)CrossRefGoogle Scholar
  2. 2.
    M.P. Sharma, J.D. Sharma, Bagasse based co-generation system for Indian sugar mills. Renew. Energy 16, 1011–1014 (1999)CrossRefGoogle Scholar
  3. 3.
    M. Siddhartha Bhatt, N. Rajkumar, Mapping of combined heat and power systems in cane sugar industry. Appl. Therm. Eng. 21, 1707–1719 (2001)CrossRefGoogle Scholar
  4. 4.
    G.R. Lobley, W.L. Al-Otaibi, Understanding boiler tube failures. Saudi Aramco J. Technol. 7–11 (2008)Google Scholar
  5. 5.
    A.C. Dunn, Y.Y. Du, Optimal load allocation of multiple fuel boilers. ISA Trans. 48, 190–195 (2009)CrossRefGoogle Scholar
  6. 6.
    A. Wienese, Boilers, boiler fuel and boiler efficiency. Proc. S. Afr. Sug. Technol. Ass. 75, 275–281 (2001)Google Scholar
  7. 7.
    E. Bas, An investment plan for preventing child injuries using risk priority number of failure mode and effects analysis methodology and a multi-objective, multi-dimensional mixed 0–1 knapsack model. Reliab. Eng. Syst. Saf. 96, 748–756 (2011)CrossRefGoogle Scholar
  8. 8.
    T. Buksa, D. Pavletic, M. Sokovic, Shipbuilding pipeline production quality improvement. J. Achiev. Mater. Manuf. Eng. 40(2), 160–166 (2010)Google Scholar
  9. 9.
    M. Boldrin, A. De Lorenzi, A. Fiorentin, L. Grando, D. Marcuzzi, S. Peruzzo, N. Pomaro, W. Rigato, G. Serianni, Potential failure mode and effects analysis for the ITER NB injector. Fusion Eng. Des. 84, 466–469 (2009)CrossRefGoogle Scholar
  10. 10.
    A.C.F. Guimaraes, C.M.F. Lapa, Fuzzy inference to risk assessment on nuclear engineering systems. Appl. Soft Comput. 7, 17–28 (2007)CrossRefGoogle Scholar
  11. 11.
    A.C.F. Guimaraes, C.M.F. Lapa, M.D.L. Moreira, Fuzzy methodology applied to probabilistic safety assessment for digital system in nuclear power plants. Nucl. Eng. Des. 241, 3967–3976 (2011)CrossRefGoogle Scholar
  12. 12.
    M.K. Pınar, Y. Kumru, Fuzzy FMEA application to improve purchasing process in a public hospital. Appl. Soft Comput. 13, 721–733 (2013)CrossRefGoogle Scholar
  13. 13.
    S. Khanmohammadi, K. Rezaie, J. Jassbi, S. Tadayon, Development of failure occurrence model based on fuzzy inference system for control center of power system. Am. J Sci. Res. 38, 131–139 (2011)Google Scholar
  14. 14.
    S. Khanmohammadi, K. Rezaie, J. Jassbi, S. Tadayon, A model of the failure detection based on fuzzy inference system for the control center of a power system. Appl. Math. Sci. 6(36), 1747–1758 (2012)Google Scholar
  15. 15.
    S.K. Oraee, A. Yazdani-Chamzini, M.H. Basiri, Evaluating Underground Mining Hazards by Fuzzy FMEA (SME Annual Meeting, Denver, 2011)Google Scholar
  16. 16.
    M. Oudjene, L. Ben-Ayed, On the parametrical study of clinch joining of metallic sheets using the Taguchi method. Eng. Struct. 30, 1782–1788 (2008)CrossRefGoogle Scholar
  17. 17.
    U. Esme, Application of Taguchi method for the optimization of resistance spot welding process. The Arab. J. Eng. 34(2B), 519–528 (2009)Google Scholar
  18. 18.
    D. Bajic, S. Jozic, S. Podrug, Design of experiment’s application in the optimization of milling process. Metalurgija 49, 123–126 (2010)Google Scholar
  19. 19.
    A.H. Suhail, N. Ismail, S.V. Wong, N.A. Abdul Jalil, Optimization of cutting parameters based on surface roughness and assistance of work piece surface temperature in turning process. Am. J Eng Appl. Sci. 3, 102–108 (2010)CrossRefGoogle Scholar
  20. 20.
    T.-S. Li, S.-H. Chen, H.-L. Chen, Thermal-flow techniques for sub-35 nm contact-hole fabrication using Taguchi method in electron-beam lithography. Microelectron. Eng. 86, 2170–2175 (2009)CrossRefGoogle Scholar
  21. 21.
    S.H. Sadeghi, V. Moosavi, A. Karami, N. Behnia, Soil erosion assessment and prioritization of affecting factors at plot scale using the Taguchi method. J. Hydrol. 448–449, 174–180 (2012)CrossRefGoogle Scholar
  22. 22.
    C.-W. Hong, Using the Taguchi method for effective market segmentation. Expert Syst. Appl. 39, 5451–5459 (2012)CrossRefGoogle Scholar
  23. 23.
    I. Kotcioglu, A.C.M.N. Khalaji, Experimental investigation for optimization of design parameters in a rectangular duct with plate-fins heat exchanger by Taguchi Method. Appl. Therm. Eng. 50, 604–613 (2013)CrossRefGoogle Scholar
  24. 24.
    A. Hamdan, A.A.D. Sarhan, M. Hamdi, An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish. Int. J Adv. Manuf. Technol. 58, 81–91 (2012)CrossRefGoogle Scholar
  25. 25.
    C.N. Madu, Competing through maintenance strategies. Int. J. Qual. Reliab. Manag. 17(9), 937–948 (2000)CrossRefGoogle Scholar
  26. 26.
    B.S. Dhillon, Methods for performing human reliability and error analysis in health care. Int. J. Health. Qual. Assur. 16(6), 306–317 (2003)CrossRefGoogle Scholar
  27. 27.
    M. Sen, H.S. Shan, Analysis of Roundness error and surface roughness in the electro jet drilling process. Mater. Manuf. Process. 21, 1–9 (2006)CrossRefGoogle Scholar
  28. 28.
    Z. Zhang, X. Chu, Risk prioritization in failure mode and effects analysis under uncertainty. Expert Syst. Appl. 38, 206–214 (2011)CrossRefGoogle Scholar
  29. 29.
    N. Seliger, E. Wolfgang, G. Lefranc, H. Berg, T. Licht, Reliable electronics for automotive applications. Microelectron. Reliab. 42, 1596–1604 (2002)CrossRefGoogle Scholar
  30. 30.
    I.H. Afefy, Reliability-centered maintenance methodology and application: a case study. Sci. Res. Eng. 2, 863–873 (2010)Google Scholar
  31. 31.
    K.-H. Chang, Evaluate the orderings of risk for failure problems using a more general RPN methodology. Microelectron. Reliab. 49, 1586–1596 (2009)CrossRefGoogle Scholar
  32. 32.
    K. Ranjbar, Failure analysis of boiler cold and hot reheater tubes. Eng. Fail. Anal. 14, 620–625 (2007)CrossRefGoogle Scholar
  33. 33.
    K. Xu, L.C. Tang, M. Xie, S.L. Ho, M.L. Zhu, Fuzzy assessment of FMEA for engine systems. Reliab. Eng. Syst. Saf. 75, 17–29 (2002)CrossRefGoogle Scholar
  34. 34.
    M. Momeni, M.H. Moayed, A. Davoodi, Tuning DOS measuring parameters based on double-loop EPR in H2SO4 containing KSCN by Taguchi method. Corros. Sci. 52, 2653–2660 (2010)CrossRefGoogle Scholar
  35. 35.
    G.J. Tzou, C.C. Tsao, Y.C. Lin, Improvement in the thermal conductivity of aluminum substrate for the desktop PC Central Processing Unit (CPU) by the Taguchi method. Exp. Therm. Fluid Sci. 34, 706–710 (2010)CrossRefGoogle Scholar
  36. 36.
    S. Ebrahimiasl, W.M.Z.W. Yunus, A. Kassim, Z. Zainal, Prediction of grain size, thickness and absorbance of nanocrystalline tin oxide thin film by Taguchi robust design. Solid State Sci. 12, 1323–1327 (2010)CrossRefGoogle Scholar
  37. 37.
    F. Demir, B. Dönmez, Optimization of the dissolution of magnesite in citric acid solutions. Int. J. Miner. Process. 87, 60–64 (2008)CrossRefGoogle Scholar
  38. 38.
    H.K. Kansal, S. Singh, P. Kumar, Parametric optimization of powder mixed electrical discharge machining by response surface methodology. J. Mater. Process. Technol. 169, 427–436 (2005)CrossRefGoogle Scholar
  39. 39.
    H. Singh, Optimizing tool life of carbide inserts for turned parts using Taguchi’s design of experiments. in Approach Proceedings of the International Multi Conference of Engineers and Computer Scientists, Hong Kong, vol. II, IMECS 2008, 19–21 March 2008Google Scholar
  40. 40.
    F.T.S. Chan, R. Bhagwat, S. Wadhwa, Flexibility performance: Taguchi’s method study of physical system and operating control parameters of FMS. Robot. Comput. Integr. Manuf. 23, 25–37 (2007)CrossRefGoogle Scholar
  41. 41.
    F. Mustaphaa, M. Mustapha, K. Noorsal, O. Mamat, P. Hussain, F. Ahmad, N. Muhamad, S.M. Haris, Preliminary study on the fabrication of aluminium foam through pressure assisted sintering dissolution process. J. Mater. Process. Technol. 210, 1598–1612 (2010)CrossRefGoogle Scholar
  42. 42.
    M. Altan, Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Mater. Des. 31, 599–604 (2010)CrossRefGoogle Scholar
  43. 43.
    F.-J. Shiou, C.-C. Shiou, Surface finishing of hardened and tempered stainless tool steel using sequential ball grinding, ball burnishing and ball polishing processes on a machining centre. J. Mater. Process. Technol. 205, 249–258 (2008)CrossRefGoogle Scholar
  44. 44.
    Y.-T. Liu, W.-C. Chang, Y. Yamagata, A study on optimal compensation cutting for an aspheric surface using the Taguchi method. CIRP J. Manuf. Sci. Technol. 3, 40–48 (2010)CrossRefGoogle Scholar
  45. 45.
    J. Singaravelu, D. Jeyakumar, B. Nageswara Rao, Taguchi’s approach for reliability and safety assessments in the stage Separation process of a multistage launch vehicle. Reliab. Eng. Syst. Saf. 94, 1526–1541 (2009)CrossRefGoogle Scholar
  46. 46.
    A.K. Sahoo, M.K. Tiwari, A.R. Mileham, Six sigma based approach to optimize radial forging operation variables. J. Mater. Process. Technol. 202, 125–136 (2008)CrossRefGoogle Scholar
  47. 47.
    A.K. Lakshminarayanan, V. Balasubramanian, Process parameters optimisation for friction stir welding of RDE-40 aluminium alloy using Taguchi technique. Trans. Nonferrous Met. Soc. China 18(3), 548–554 (2008)CrossRefGoogle Scholar

Copyright information

© ASM International 2014

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringRajiv Gandhi College of Engineering and TechnologyKirumampakkamIndia
  2. 2.Department of Mechanical EngineeringPondicherry Engineering CollegePillaichavadyIndia

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