As energy efficiency becomes increasingly important to the steel industry, the iron ore sintering process is attracting more attention since it consumes the second large amount of energy in the iron and steel making processes. The present work aims to propose a prediction model for the iron ore sintering characters. A hybrid ensemble model combined the extreme learning machine (ELM) with an improved AdaBoost.RT algorithm is developed for regression problem. First, the factors that affect solid fuel consumption, gas fuel consumption, burn-through point (BTP), and tumbler index (TI) are ranked according to the attributes weightiness sequence by applying the RReliefF method. Second, the ELM network is selected as an ensemble predictor due to its fast learning speed and good generalization performance. Third, an improved AdaBoost.RT is established to overcome the limitation of conventional AdaBoost.RT by dynamically self-adjusting the threshold value. Then, an ensemble ELM is employed by using the improved AdaBoost.RT for better precision than individual predictor. Finally, this hybrid ensemble model is applied to predict the iron ore sintering characters by production data from No. 4 sintering machine in Baosteel. The results obtained show that the proposed model is effective and feasible for the practical sintering process. In addition, through analyzing the first superior factors, the energy efficiency and sinter quality could be obviously improved.

The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher than its operating restriction, within the operation restriction and lower than the operating restriction. Based on these black-box model results, an integrated three-step approach for rule extraction is constructed to interpret the understandability of the proposed ELM classifier. First, the irrelevant attributes are pruned without decreasing the classification accuracy. Second, fuzzy rules are generated in the form of discrete input attributes and the target classification. Last but not the least, the rules are refined by generating rules with continuous attributes. The novelty of the proposed rule extraction approach lies in the generation of rules using the discrete and continuous attributes at different stages. The proposed method is analyzed and validated on actual production data derived from a No.2 steelmaking workshop in Baosteel. The experimental results revealed that the extracted rules are effective for the VTD system in classifying the end temperature of liquid steel into high, normal, and low ranges. In addition, much fewer input attributes are needed to implement the rules for the manufacturing process of VTD. The extracted rules serve explicit instructions for decision-making for the VTD operators.

The present work aimed to develop a predictive model for the end temperature of liquid steel in advance to support the smooth functioning of a vacuum tank degasser (VTD). An ensemble model that combines extreme learning machine (ELM) with a self-adaptive AdaBoost.RT algorithm was established for the regression problem. Based on analyzing the energy equilibrium of the VTD system, the factors were determined for predicting the end temperature of liquid steel. To establish a hybrid ensemble prediction model, an ELM algorithm was selected as the ensemble predictor due to its strong performance and robustness, and a modification of the AdaBoost.RT algorithm is proposed to overcome the drawback of the original AdaBoost.RT by embedding statistical theory to dynamically self-adjust the threshold value. For efficient VTD operations, an ensemble model that combines ELM with the self-adaptive AdaBoost.RT algorithm was established to model the end temperature of liquid steel. The proposed approach was analyzed and validated on actual production data derived from a steelmaking workshop in Baosteel. The experimental results reveal that the proposed model can improve the generalization performance, and the accuracy of the model is feasible for the secondary steel refining process. In addition, a polynomial equation is obtained from the ensemble predictive model for calculating the value of the end temperature. The predicted results are in good agreement with the actual data with <1.7% error.

Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently, the extreme learning machine (ELM) approach is applied to develop a predictive model of RUL based on the optimal features. The model is trained by optimizing its parameters via the grid search approach. The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method. The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database. The comparison is constructed with other traditional models. The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.