Internal heat transfer characteristics of large-particle oil shale during pyrolysis

Ma, Yue; Zhu, Yukai; Li, Shuyuan; Jian, Shi; Hou, Jili; Zhang, Luchao

doi:10.1007/s10973-018-7622-z

Cited by 10 publications

(1 citation statement)

References 16 publications

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“…According to the pyrolysis process, the maximum center surface temperature difference constantly rises as the sample's diameter does 16 . Space engineering was very concerned about the temperature and other elements that could affect drilling, especially in a vacuum 6 .…”

Section: Introductionmentioning

confidence: 99%

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

Varadaraj,

Vijay Kumar,

Chethan

et al. 2023

jmmf

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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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Section: Introductionmentioning

confidence: 99%