화학공학소재연구정보센터
Journal of Industrial and Engineering Chemistry, Vol.100, 399-409, August, 2021
Application of artificial intelligence to magnetite-based magnetorheological fluids
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Magnetorheological (MR) fluids are intelligent fluids that change their state under a magnetic field and can be extensively applied in several industries. In this study, a model was presented to predict the MR behavioral trend of magnetite-based MR fluids using deep neural networks. The MR data of nine samples with several magnetite nanoparticle concentrations and different silicone oil viscosities were used for network construction and testing; the aforementioned data were obtained under several magnetic field strengths. Seven samples were used for network training/testing within the training interval and two samples were applied for evaluating the network accuracy outside the network training interval. Several networks, such as the multi-layer perceptron (MLP), radial basis function, and adaptive neuro-fuzzy inference system, were employed, and the results were analyzed. The accuracy parameters (R2 and RMSE) of the MLP network for the training data (0.99625 and 0.00867) and test data (0.99130 and 0.01621), as well as a comparison between the predicted and laboratory-measured results of the two samples that had not been used in the modeling step, demonstrated the exceptional performance of the proposed method and an equation that was derived for predicting the shear stress. The latter equation enables researchers to achieve their needs without performing time-and cost-consuming MR tests in the laboratory.
  1. Esmaeilnezhad E, Choi HJ, Schaffie M, Gholizadeh M, Ranjbar M, Kwon SH, J. Magn. Magn. Mater., 444, 161 (2017)
  2. Zhao D, Shi X, Liu S, Wang F, J. Intell. Mater. Syst. Struct., 31, 882 (2020)
  3. Han WJ, An JS, Choi HJ, Smart Mater. Struct., 29, 055022 (2020)
  4. Gopinath B, Sathishkumar G, Karthik P, Charles MM, Ashok K, Ibrahim M, Akheel MM, Mater. Today: Proceed., 37, 1721 (2021)
  5. Ashtiani M, Hashemabadi SH, Ghaffari A, J. Magn., 374, 716 (2015)
  6. Esmaeilnezhad E, Karimian M, Choi HJ, J. Ind. Eng. Chem., 71, 402 (2019)
  7. Singh A, Thakur MK, Sarkar C, Proc. Inst. Mech. Eng., 234, 1252 (2020)
  8. Azar BF, Veladi H, Talatahari S, Raeesi F, J. Korean Soc. Civ. Eng., 24, 867 (2020)
  9. Dong YZ, Han WJ, Choi HJ, J. Ind. Eng. Chem., 93, 210 (2021)
  10. Esmaeilnezhad E, Van SL, Choi HJ, Chon BH, Schaffie M, Gholizadeh M, Ranjbar M, J. Environ. Manage., 231, 1127 (2019)
  11. Caizer C, Nanomaterials, 11, 40 (2021)
  12. Tian F, Zhou JF, Shao CI, Wu HB, Hao L, Colloids Surf. A: Physicochem. Eng. Asp., 591, 124531 (2020)
  13. Covarrubias-Garcia I, Osorio-Gonzalez CS, Ramirez AA, Rodriguez-Lopez JL, Brar SK, Arriaga S, Microporous Mesoporous Mater., 31, 110592 (2021)
  14. Esmaeilnezhad E, Choi HJ, Schaffie M, Gholizadeh M, Ranjbar M, J. Clean Prod., 171, 45 (2018)
  15. Wallyn J, Anton N, Vandamme TF, Pharmaceutics, 11, 601 (2019)
  16. Fei C, Haopeng L, Mengmeng H, Zuzhi T, Aimin L, Mater. Manuf. Process, 35, 1077 (2020)
  17. Elsaady W, Oyadiji SO, Nasser A, Int. J. Mech. Sci., 167, 105265 (2020)
  18. Kumbhar BK, Patil SR, Sawant SM, Int. J. Eng. Sci. Technol., 18, 432 (2015)
  19. Dresp JH, Graefes Arch. Clin., 259, 13 (2021)
  20. Wang Y, Xie W, Wu D, Carbohydr. Polym., 231, 115776 (2020)
  21. Arief I, Sahoo R, Mukhopadhyay P, J. Magn. Magn. Mater., 412, 194 (2016)
  22. Zhang Y, Li D, Cui H, Yang J, J. Magn. Magn. Mater., 500, 166377 (2020)
  23. Kim M, Park SJ, J. Magn. Magn. Mater., 404, 40 (2016)
  24. Steel DH, Wong D, Sakamoto T, Graefes Arch. Clin., 259, 11 (2021)
  25. Rabbani Y, Ashtiani M, Hashemabadi SH, Soft Matter 11, 11, 4453 (2015)
  26. Wang N, Liu X, Sun S, Krolczyk G, Li Z, Li W, J. Magn. Magn. Mater., 501, 166443 (2020)
  27. Rahmanifard H, Plaksina T, Artif. Intell. Rev., 52, 2295 (2019)
  28. Esmaeilnezhad E, Ranjbar M, abadi-pour HN, Fard FS, Khamseh, Pet. Sci. Technol., 31, 1647 (2013)
  29. Nasr MS, Nasr HS, Karimian M, Esmaeilnezhad E, Nat. Resour. (2021).
  30. Esmaeilnezhad E, Hajiabadi SH, Choi HJ, J. Ind. Eng. Chem., 80, 197 (2019)
  31. Chen YY, Lin YH, Kung CC, Chung MH, Yen IH, Sensors, 19, 2047 (2019)
  32. Mohaghegh S, J. Pet. Technol., 52(9), 64 (2000)
  33. Karamichailidou D, Kaloutsa V, Alexandridis A, Renew. Energy, 163, 2137 (2021)
  34. Koh BHD, Lim CLP, Rahimi H, Woo WL, Gao B, Sensors, 21, 603 (2021)
  35. Koh BHD, Woo WL, IEEE Access, 7, 32482 (2019)
  36. Liu T, Li Y, Jing Q, Xie Y, Zhang D, Int. J. Heat Mass Transf., 165, 120684 (2021)
  37. Jin X, Liu Q, Long H, Comput. Appl. Math., 384, 113172 (2021)
  38. Pashaei E, Pashaei E, Arab. J. Sci. Eng (2021).
  39. Kamal K, Qayyum R, Mathavan S, Zafar T, Adv. Eng. Inform., 34, 125 (2017)
  40. Sharma S, Int. J. Appl. Sci., 4, 310 (2017)
  41. Moosavi SR, Wood DA, Ahmadi MA, Choubineh A, Nat. Resour. Res., 28, 1619 (2019)
  42. Park J, Sandberg IW, Neural Comput., 3, 246 (1991)
  43. Zadeh LA, Computer, 21, 83 (1988)
  44. Jang JS, IEEE Trans. Syst. Man Cynern. Syst., 23, 665 (1993)
  45. Sabah M, Talebkeikhah M, Agin F, Talebkeikhah F, Hasheminasab E, J. Pet. Sci. Eng., 177, 236 (2019)
  46. Zanganeh M, J. Ocean. Eng. Sci., 5, 84 (2020)
  47. Bemani A, Baghban A, Mosavi A, Eng. Appl. Comput. Fluid Mech., 14, 818 (2020)
  48. Baghban A, Bahadori M, Ahmad Z, Kashiwao T, Bahadori A, Pet. Sci. Technol., 34, 933 (2016)
  49. Wang Z, Ye P, Qiu F, Tian G, Woo WL, J. Magn. Magn. Mater., 500, 166412 (2020)
  50. Rabbani Y, Shirvani M, Hashemabadi S, Keshavarz M, Colloids Surf. A: Physicochem. Eng. Asp., 520, 268 (2017)