Prediction of penetration rate by Coupled Simulated Annealing-Least Square Support Vector Machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters

Chen, Heng; Duan, Jinying; Yin, Rui; Ponkratov, Vadim V.; Guerrero, John William Grimaldo

doi:10.1016/j.egyr.2021.06.080

Cited by 8 publications

(2 citation statements)

References 73 publications

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“…In the field information analysis of drilling operations, some scholars have used support vector machines combined with simulated annealing algorithms for multi-objective optimization model building. This model has better classification function and stronger problem solving ability in solving multiobjective optimization problems [18].…”

Section: Introductionmentioning

confidence: 99%

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

2024

J. vibroeng.

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This study achieved the goal of guiding bed design and optimization by conducting multi-objective optimization research on the performance of CNC lathe beds. In this study, Morris analysis was first performed on the sensitivity of the parameters, and then out to optimize the parameters using a combination of neural network and genetic algorithm. The loss function value, RMSE error accumulation, recall, sensitivity and specificity of the ASSGA-BP optimization model were better. The maximum error between the predicted and true values of the ASSGA-BP model was 0.28 mm. In the performance study of the multi-objective optimization method based on the Morris sensitivity analysis and the improved GA algorithm, the average MAE value is 0.91 %. The average RMSE value is 0.59 %. Also, the new model is significantly better than the NSGA-II, EGA, and FGA algorithms in terms of both the number of final non-dominated solutions and the speed of reaching convergence. The above results demonstrate that the model proposed in this study has high performance, can achieve faster convergence and has the best stability of the convergence state. The innovation of this article lies in the use of the Morris method to screen and evaluate numerous parameters in order to improve the accuracy of the calculation results and ensure the effectiveness of the optimization results. The improved algorithm overcomes the problems of BP neural network and can effectively improve the generalization performance of the neural network, thereby improving the prediction accuracy of the model.

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Section: Introductionmentioning

confidence: 99%

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

2024

J. vibroeng.

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“…The SSA shows excellent performance in algorithm convergence and global optimization, which encourages us to use this algorithm to optimize the parameters of the least squares support vector machine. Some hybrid least square support vector machine models are proposed and applied to different fields, such as water quality prediction [23], reservoir penetration rate [24], insulator surface contamination prediction [25] and especially PM 2.5 concentration [26] and pollutant prediction [27].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Prediction Model of Air Pollutant Concentration for PM2.5 and PM10

Wang

2023

Atmosphere

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To alleviate the negative effects of air pollution, this paper explores a mixed prediction model of pollutant concentration based on the machine learning method. Firstly, in order to improve the prediction performance of the sparrow search algorithm least square support vector machine (SSA-LSSVM), a reverse learning strategy-lens principle is introduced, and a better solution is obtained by optimizing the current solution and reverse solution at the same time. Secondly, according to the nonlinear and non-stationary characteristics of the time series data of PM2.5 and PM10, the variational mode decomposition (VMD) method is used to decompose the original data to obtain the appropriate K value. Finally, experimental verification and an empirical analysis are carried out. In experiment 1, we verified the good performance of the model on University of California Irvine Machine Learning Repository (UCI) datasets. In experiment 2, we predicted the pollutant data of different cities in the Beijing–Tianjin–Hebei region in different time periods, and obtained five error results and compared them with six other algorithms. The results show that the prediction method in this paper has good robustness and the expected results can be obtained under different prediction conditions.

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Initial Geostress in Underground Cavities Obtained by In Situ Measurements and BP-ANN Inversion

Li,

Yang,

Shi

et al. 2023

Geotech Geol Eng

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Prediction of penetration rate by Coupled Simulated Annealing-Least Square Support Vector Machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters

Cited by 8 publications

References 73 publications

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

Hybrid Prediction Model of Air Pollutant Concentration for PM2.5 and PM10

Initial Geostress in Underground Cavities Obtained by In Situ Measurements and BP-ANN Inversion

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