Multilayer Perceptron Artificial Neural Network (Mlpann) Model to Predict Temperature During Rotary Drilling

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Authors

  • K. R. Varadaraj
     School of Mechanical Engineering, Reva University, Bangalore - 560064, Karnataka ,IN
  • S. Vijay Kumar
     Department of Mechanical Engineering, Nitte Meenakshi Institute of Technology, Bangalore - 560064, Karnataka ,IN
  • D. Chethan
     Department of Mechanical Engineering, Nitte Meenakshi Institute of Technology, Bangalore - 560064, Karnataka ,IN
  • S. C. Ramesh Kumar
     School of Mechanical Engineering, Reva University, Bangalore - 560064, Karnataka ,IN
  • S. Basavaraju
     Department of Mechanical Engineering, Sapthagiri College of Engineering, Bangalore - 560057, Karnataka ,IN
  • B. M. Kunar
     Department of Mining Engineering, National Institute of Technology Karnataka, Surathkal - 575025, Karnataka ,IN
  • Jose de Jesus Agustin Flores Cuautle
     CONACYT, Orizaba Technological Institute, Orizaba – 94320 ,MX

DOI:

https://doi.org/10.18311/jmmf/2023/36268

Keywords:

Interface Temperature, MLPANN, Transfer Function

Abstract

In this paper, a multilayer perceptron neural network has been used to represent temperature measurement during rotary drilling of five types of rock samples. To forecast the temperature at various thermocouple depths, the experimentally collected data was standardized. Indicators of model performance was also obtained in order to assess the correctness of the model. One hidden layer and one output layer were employed with MLPANN, which has ten input parameters (bit diameter (DD), Spindle Speed (SS), Penetration Rate (PR), thrust, and torque) and rock properties. Levenberg Marquardt learning algorithm with transfer function of logsig is the most optimal neuron number of 10-16-1 was successfully forecasting the temperature with a correlation of 0.9936 and 0.9941 for training and testing algorithm during drilling after analysis based on the trial-and- error approach to identify the optimum algorithm. Ten input parameters, a logsig sigmoid transfer function, and the trainlm algorithm in this study provide good prediction ability with tolerable accuracy.

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Published

2023-11-30

How to Cite

Varadaraj, K. R., Vijay Kumar, S., Chethan, D., Ramesh Kumar, S. C., Basavaraju, S., Kunar, B. M., & Agustin Flores Cuautle, J. de J. (2023). Multilayer Perceptron Artificial Neural Network (Mlpann) Model to Predict Temperature During Rotary Drilling. Journal of Mines, Metals and Fuels, 71(11), 1979–1983. https://doi.org/10.18311/jmmf/2023/36268

Issue

Volume 71, Issue 11, November 2023

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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