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Effects of nano-clay content, foaming temperature and foaming time on density and cell size of PVC matrix foam by presented Least Absolute Shrinkage and Selection Operator statistical regression via suitable experiments as a function of MMT content

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  • Li, Zhixiong
  • Shahrajabian, Hamzeh
  • Bagherzadeh, Seyed Amin
  • Jadidi, Hamid
  • Karimipour, Arash
  • Tlili, Iskander

Abstract

Present article aims to investigate the effect of nano-clay content, foaming temperature and foaming time on the density and cell size of the PVC matrix foam. The cell size would affect the insulating and mechanical properties. The foaming temperature is set in three levels of 70, 80 and 90 °C, foaming time is set in three levels of 10, 20, and 30 s; and nano-clay is in content of 1, 3, and 5 wt%. Outputs consist the density and cell size, which affect impact the thermal conductivity, mechanical properties and the weight of the polymer foam. In addition the Least Absolute Shrinkage and Selection Operator (LASSO) regression method is employed in order to improve both the precision and generalization of the estimated foam density and cell size as functions of the MMT content, the foaming temperature and the foaming time. LASSO is found a suitable approach to predict the sample properties.

Suggested Citation

  • Li, Zhixiong & Shahrajabian, Hamzeh & Bagherzadeh, Seyed Amin & Jadidi, Hamid & Karimipour, Arash & Tlili, Iskander, 2020. "Effects of nano-clay content, foaming temperature and foaming time on density and cell size of PVC matrix foam by presented Least Absolute Shrinkage and Selection Operator statistical regression via s," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315079
    DOI: 10.1016/j.physa.2019.122637
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    References listed on IDEAS

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    1. Jiang, Yu & Bahrami, Mehrdad & Bagherzadeh, Seyed Amin & Abdollahi, Ali & Sulgani, Mohsen Tahmasebi & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 2019. "Propose a new approach of fuzzy lookup table method to predict Al2O3/deionized water nanofluid thermal conductivity based on achieved empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    2. Jiang, Yu & Sulgani, Mohsen Tahmasebi & Ranjbarzadeh, Ramin & Karimipour, Arash & Nguyen, Truong Khang, 2019. "Hybrid GMDH-type neural network to predict fluid surface tension, shear stress, dynamic viscosity & sensitivity analysis based on empirical data of iron(II) oxide nanoparticles in light crude oil mixt," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    3. Bagherzadeh, Seyed Amin & Sulgani, Mohsen Tahmasebi & Nikkhah, Vahid & Bahrami, Mehrdad & Karimipour, Arash & Jiang, Yu, 2019. "Minimize pressure drop and maximize heat transfer coefficient by the new proposed multi-objective optimization/statistical model composed of “ANN + Genetic Algorithm” based on empirical data of CuO/pa," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    4. Karimipour, Arash & Bagherzadeh, Seyed Amin & Taghipour, Abdolmajid & Abdollahi, Ali & Safaei, Mohammad Reza, 2019. "A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 89-97.
    5. Bahrami, Mehrdad & Akbari, Mohammad & Bagherzadeh, Seyed Amin & Karimipour, Arash & Afrand, Masoud & Goodarzi, Marjan, 2019. "Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: Measure MSEs between targets & ANN for Fe–CuO/Eg–Water nanofluid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 159-168.
    6. Bagherzadeh, Seyed Amin & D’Orazio, Annunziata & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 2019. "A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 406-415.
    7. Nafchi, Peyman Mirzakhani & Karimipour, Arash & Afrand, Masoud, 2019. "The evaluation on a new non-Newtonian hybrid mixture composed of TiO2/ZnO/EG to present a statistical approach of power law for its rheological and thermal properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 1-18.
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    Cited by:

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    2. Wei, Li & Arasteh, Hossein & abdollahi, Ali & Parsian, Amir & Taghipour, Abdolmajid & Mashayekhi, Ramin & Tlili, Iskander, 2020. "Locally weighted moving regression: A non-parametric method for modeling nanofluid features of dynamic viscosity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).

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