New approach to mimic rheological actual shear rate under wall slip condition

  • Ren Jie Chin
  • Sai Hin LaiEmail author
  • Shaliza Ibrahim
  • Wan Zurina Wan Jaafar
  • Ahmed Hussein Kamel Ahmed Elshafie
Original Article


The presence of wall slip in concentrated suspensions affect the rheological measurements such as shear stress, shear rate, and viscosity. The measured shear rate will have a value lower than the actual shear rate. Therefore, it is important to have a study on such scenario as the concentrated suspensions have a wide industrial application. The current method for actual shear rate prediction is a challenging task and quite time-consuming as several experimental works are required. Therefore, the development of a mathematical model with an acceptable accuracy is required. Multi-layer perceptron neural network (MLP-NN) is employed to develop the prediction model by applying shear stress, volumetric concentration, particle size, and temperature as the input parameters while the actual shear rate is kept as the output variable. MLP-NN model with 9 hidden neurons is the model that has achieved the best performance in term of statistical analyses. Besides, MLP-NN models for five different temperature ranges (i.e., 11–20 °C, 21–30 °C, 31–40 °C, 41–50 °C, and 51–60 °C) are also proposed to investigate the potential of the developed model to achieve a better accuracy and ease the user while dealing with the specified temperature range. It is found that the MLP-NN models with the best performance for each temperature range are the models with 8 hidden neurons, 10 hidden neurons, 8 hidden neurons, 9 hidden neurons, and 9 hidden neurons respectively. The novelty of this research study is the application of artificial intelligence method to mimic rheological actual shear rate under wall slip condition.


Corrected shear rate Neural network Rheology Suspension Temperature 



The authors would like to express the highest gratitude for financial support from FRGS Grant no. FP015-2014A awarded by Ministry of Higher Education (MOHE) Malaysia. The authors would like to thank Faculty of Engineering University of Malaya for the facilities provided for the experimental works. The authors also would like to thank Dr. Ashok Kumar for his contribution.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Ren Jie Chin
    • 1
  • Sai Hin Lai
    • 1
    Email author
  • Shaliza Ibrahim
    • 1
  • Wan Zurina Wan Jaafar
    • 1
  • Ahmed Hussein Kamel Ahmed Elshafie
    • 1
  1. 1.Department of Civil Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

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