DOI QR코드

DOI QR Code

Optimization of Distribution Basin Weirs at a Sewage Treatment Plant Based on Computational Fluid Analysis Using the Taguchi and Minitab Method

전산유체해석과 다구찌 및 미니탭 방법을 활용한 하수처리장 분배조 웨어 최적화

  • Received : 2021.10.01
  • Accepted : 2021.12.06
  • Published : 2021.12.31

Abstract

The role of the distribution basin role is to apportion incoming raw water to the primary sedimentation basin as part of the water treatment process. The purpose of this study was to calculate the amount of water in the distribution basin using computational fluid dynamics (CFD) analysis and to find a way to improve any non-uniformity. We used the Taguchi method and the minitab tool as optimization methods. The results of the CFD calculation showed that the distribution flow had a deviation of 5% at the minimum inflow, 10% at the average inflow, and 22% at the maximum inflow. At maximum flow, the appropriate heights of the 7 weirs(C, D, A, B, E, F, G) were 40 mm, 20 mm, 20 mm, 0, 0, 0, and 20 mm, respectively, according to the Taguchi optimization tool. Here, the maximum deviation of the distribution amount was 9% and the standard deviation was 23.7. The appropriate heights of the 7 weirs, according to the Minitab tool, were 40 mm, 20 mm, 20 mm, 0, 0, 0, and 20 mm, respectively, for weirs C, D, A, B, E, F, and G. Therefore, the maximum deviation of the distribution amount was 8% and the standard deviation was 17.1, which was slightly improved compared to the Taguchi method.

Keywords

References

  1. Antony, J., 2000, Multi-response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis, Quality and Reliability Engineering International, 16, 3-8. https://doi.org/10.1002/(SICI)1099-1638(200001/02)16:1<3::AID-QRE276>3.0.CO;2-W
  2. Ardakani, M. K., Wulff, S. S., 2012, An Overview of optimization formulations for multiresponse surface problems, Quality and Reliability Engineering International, 29, 3-16. https://doi.org/10.1002/qre.1288
  3. Canel, T., Zeren, M., Smmazcelik, T., 2019, Laser parameters optimization of surface treating of Al 6082-T6 with Taguchi method, Optics and Laser Technology, 120, 105714. https://doi.org/10.1016/j.optlastec.2019.105714
  4. Cho, Y. M., Yoo, P. J., 2015, Optimization of distribution basin and ratio at valve opening in the water treatment process, Journal of Korean Society of Water and Wastewater, 29, 559-565. https://doi.org/10.11001/jksww.2015.29.5.559
  5. Eslami, N., Hischer, Y., Harms, A., Lauterbach, D., Bohm, S., 2019, Optimization of process parameters for friction Stir welding of aluminum and copper using the Taguchi method, Metals, 9.
  6. Heathcote, D. J., Gursul, I., Cleaver, D. J., 2016, An Experimental study of mini-tabs for aerodynamic load control, 54th AIAA Aerospace Sciences Meeting, IAA Paper, 2016-0325.
  7. Heathcote, D. J., Gursul, I., Cleaver, D. J., 2018, Aerodynamic load alleviation using minitabs, J. of AIRCRAFT 55, 2068-2077. https://doi.org/10.2514/1.c034574
  8. Kim, T. K., Han, H. S., Choi, Y. J., 2019, Analysis of flow distribution and void fraction in distribution basin using CFD, Kor. Soc. of Envi. Health and Toxi., 10, 124.
  9. Leonid, G., Vitaly, H., 2013, Turbulence modeling applied to flow over a hydraulic ball check valve, Eng., 5, 685-691. https://doi.org/10.4236/eng.2013.58081
  10. Menter, F. R., 2002, Two-equation eddy-viscosity turbulence models for engineering applications, AIAA J., 40, 254-266. https://doi.org/10.2514/2.1667
  11. Michael, R. D., Steven, O, 2005, Discovering influential cases in linear regression with Minitab: Peeking into multidimensions with a Mintab macro, Statistical Methodology, 2, 71-81. https://doi.org/10.1016/j.stamet.2004.11.005
  12. Panda, A., Singh, R. K., 2013, Optimization of process parameters in the catalytic degradation of polypropylene to liquid fuel by Taguchi method, Adv. Chem. Eng. Res. ACER, 2, 106-112.
  13. Ramamurthy, A. S., Tim, U. S., Rao, M. V. J., 1987, Weir orifice units for uniform flow distribution, J. Env. Eng., 113, 155-166. https://doi.org/10.1061/(ASCE)0733-9372(1987)113:1(155)
  14. Rao, R. S., Kumar, C. G., Prakasham, R. S., Hobbs, P. J., 2008, The Taguchi methodology as a statistical tool for biotechnological applications, A critical appraisal Bio. J., 3, 510-523. https://doi.org/10.1002/biot.200700201