Predicting the temperature of the coal spontaneous combustion (CSC) is essential for preventing and managing coal mine fires. In this paper, a roughset-Stacking-SHapley Additive Explanations (RS-Stacking-SHAP) predic...
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Predicting the temperature of the coal spontaneous combustion (CSC) is essential for preventing and managing coal mine fires. In this paper, a roughset-Stacking-SHapley Additive Explanations (RS-Stacking-SHAP) prediction model of CSC based on grid search optimized is proposed. Compared with the traditional machine learning model, the model has better prediction accuracy and generalization ability. Based on the data collected from experimental coal samples in Lijiahao Coal Mine, rough set algorithm was used for attribute approximation to identify O2, CO, CH4, C2H4, C2H6, C3H8, CO/CH4, C2H4/C2H6 as the model indexes, thereby establishing the system of warning indexes for spontaneous combustion of coal. XGBoost, SVR, RF, LightGBM and BP models were selected as base models to establish an early warning model for CSC based on the stacking integration architecture. The grid search algorithm was utilized to optimize the model parameters, ensuring the selection of the most suitable parameter configurations. The dataset was then divided into the training and test sets in a 7:3 ratio, and the extracted indicators of each gas were used as inputs to the model and the temperature was used as outputs. The mean absolute error (MAE), root mean square error (RMSE), r-square (R2), mean absolute percentage error (MAPE), weighted mean absolute percentage error (WMAPE) and variance account for (VAF) were chosen to evaluate the results. The predictive performance of the model was compared with that of the individual base models, and the results displayed that the R2 value of the RS-Stacking model was 0.991, representing improvements of 12.7%, 14.1%, 0.6%, 3.5% and 17.7% over the XGBoost, SVR, RF, LightGBM, and BP models, respectively. GS-RS-Stacking was considered to be the best model, where MAPE = 5.14%, WMAPE = 3.76%, VAF
In recent years, finite element simulation has been increasingly combined with optimization techniques and applied to optimization of various metal-forming processes. The robustness and efficiency of process optimizat...
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In recent years, finite element simulation has been increasingly combined with optimization techniques and applied to optimization of various metal-forming processes. The robustness and efficiency of process optimization are critical factors to obtain ideal results, especially for those complicated metal-forming processes. Gradient-based optimization algorithms are subject to mathematical restrictions of discontinuous searching space, while nongradient optimization algorithms often lead to excessive computation time. This paper presents a novel intelligent optimization approach that integrates machine learning and optimization techniques. An intelligent gradient-based optimization scheme and an intelligent response surface methodology are proposed, respectively. By machine learning based on the rough set algorithm, initial total design space can be reduced to self-continuous hypercubes as effective searching spaces. Then optimization algorithms can be implemented more effectively to find optimal design results. An extrusion forging process and a U channel roll forming process are studied as application samples and the effectiveness of the proposed approach is verified.
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