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The prediction model with the sinter drum strength as the evaluation index was established based on the index data and historical sintering data generated during the sintering process. The regression prediction model in the algorithm of machine learning was applied to the prediction of the strength of the sinter drum. After verifying the feasibilit...
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... As shown in Figure 13, Artificial Neural Network (ANN) was a typical ML algorithm [31][32][33][34][35][36] used for predictive analysis of the f ov . Additionally, the ANN model was trained using the Adam optimizer algorithm [37,38]. ...
To address the uncertainty of optimal vibratory frequency fov of high-speed railway graded gravel (HRGG) and achieve high-precision prediction of the fov, the following research was conducted. Firstly, commencing with vibratory compaction experiments and the hammering modal analysis method, the resonance frequency f0 of HRGG fillers, varying in compactness K, was initially determined. The correlation between f0 and fov was revealed through vibratory compaction experiments conducted at different vibratory frequencies. This correlation was established based on the compaction physical–mechanical properties of HRGG fillers, encompassing maximum dry density ρdmax, stiffness Krd, and bearing capacity coefficient K20. Secondly, the gray relational analysis algorithm was used to determine the key feature influencing the fov based on the quantified relationship between the filler feature and fov. Finally, the key features influencing the fov were used as input parameters to establish the artificial neural network prediction model (ANN-PM) for fov. The predictive performance of ANN-PM was evaluated from the ablation study, prediction accuracy, and prediction error. The results showed that the ρdmax, Krd, and K20 all obtained optimal states when fov was set as f0 for different gradation HRGG fillers. Furthermore, it was found that the key features influencing the fov were determined to be the maximum particle diameter dmax, gradation parameters b and m, flat and elongated particles in coarse aggregate Qe, and the Los Angeles abrasion of coarse aggregate LAA. Among them, the influence of dmax on the ANN-PM predictive performance was the most significant. On the training and testing sets, the goodness-of-fit R² of ANN-PM all exceeded 0.95, and the prediction errors were small, which indicated that the accuracy of ANN-PM predictions was relatively high. In addition, it was clear that the ANN-PM exhibited excellent robust performance. The research results provide a novel method for determining the fov of subgrade fillers and provide theoretical guidance for the intelligent construction of high-speed railway subgrades.
As a major burden of blast furnace, sinter mineral with desired quality performance needs to be produced in sinter plants. The tumbler index (TI) is one of the most important indices to characterize the quality of sinter, which depends on the raw materials proportion, operating system parameters and the chemical compositions. To accurately predict TI, an integrate model is proposed in this study. First, to decrease the data dimensionality, the sintering production data is addressed through principal component analysis (PCA) and the principal components with the accumulated contribution rate no more than 95% are extracted as the inputs of the predictive model based on Extreme Learning Machine (ELM). Second, the genetic algorithm (GA) has been applied to promote the improvement of the robustness and generalization performance of the original ELM. Finally, the model is examined using actual production data of a year from a sinter plant, and is compared with the algorithms of single ELM, GA-BP and deep learning method. A comparison is conducted to confirm the superiority of the proposed model with two traditional models. The results showed that an improvement in predictive accuracy can be obtained by the GA-ELM approach, and the accuracy of TI prediction is 81.85% for absolute error under 0.7%.
The particle size distribution of sintered ore is the key indicator reflecting the quality of sintered ore products. Aiming at the problems of low accuracy and lack of interpretability of the existing sinter ore particle size distribution prediction models, this paper proposes a multi-task learning prediction model that uses the bidirectional gated recurrent unit as an information sharing layer to predict the particle size distribution of sinter ore. Firstly, features with high correlation with different particle size ranges (<5 mm; <10 mm) are selected as model inputs by the feature selection method. Secondly, a bidirectional gated recurrent unit is used as the information-sharing layer to construct a multi-task learning model, and the information sharing between two tasks is used to obtain their coupling information to consider the correlation between different particle sizes and improve the prediction accuracy of the model. Additionally, the Shapely interpretation method is introduced to analyse the interpretability of the important features of the model to enhance the predictive model interpretability. Finally, the model proposed in this article is compared with the rest of the combined models, and the results show that the proposed model has high prediction accuracy. This method provides a new direction for the high-quality production of sintered ore.
Since there are many uncertain factors in the actual production process, reliable information cannot be obtained from point prediction results. Therefore, this paper proposes an interval prediction method to evaluate the screening index. Firstly, feature engineering algorithms are used to select the most suitable data and variables for modeling. Secondly, based on the sequential characteristics of the sintering process, the gated cycle unit (GRU) model is used to make point predictions on the indicators. then the kernel density estimation (KDE) algorithm is used to quantify the error of the index to obtain the interval prediction results, and the RandomizedSearchCV algorithm is used to optimize the parameters of the model. Finally, comparison shows that the GRU model has higher point prediction accuracy, and the KDE algorithm can better quantify the prediction error, and then obtain reliable interval prediction results. This method has guiding significance for the high-quality production of sinter.
Machine learning (ML) is a fast-growing field that has vast applications in different areas and sintering has had no exemption from that. In this paper, the application of ML methods in sintering of the various materials has been reviewed. Based on our review, it was used to optimize the sintering process and improve the characteristics of the final product. For instance, a supervised learning algorithm was used to predict the temperature and time based on the raw material properties and the desired properties of the final product in sintering. Among all ML methods, k-nearest neighbor (k-NN), random forest (RF), support vector machine (SVM), regression analysis (RA), and artificial neural networks (ANN) had great applications in the sintering field. There are a limited number of papers that used deep learning in sintering. In conclusion, ML methods can be used to optimize sintering process in energy, cost and time.
