Abstract
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.
Highlights
Blast furnace sinter Return fines –5 mm performance. e location of burn-through point (BTP) is the number of wind box which reached the highest temperature. e quality of sinter cake is determined by the BTP
The root mean square error (RMSE) of the di erent input features set of the model are compared with each other, and the corresponding features set with the lowest value of RMSE is considered as the optimal variable group for the extreme learning machine (ELM) networks. erefore, the virtual model of sintering is optimized to predict the sintering characters
E total data available for modeling reduced to 270 data sets, which are used for training and testing with a network model
Summary
An ELM [19] is an e cient learning algorithm for single hidden layer feedforward neural networks (SLFNs) used to solve the classi cation and regression problems. In order to simplify the computational complexity of ELM, two solutions can be obtained according to the scale of training samples by solving the dual optimization problem. We propose a dynamically self-adjustable modifying the value of φ method to improve the AdaBoost.RT by embedding the statistics theory related to the regression capability of the weak learner into the training of ensemble predictor. E μt and σt in equation (17) are the expected value and the standard deviation of the weak learners’ predictions for the training set in the tth network. Erefore, the proposed approach to improve the AdaBoost.RT algorithm is capable to output the final hypotheses in optimally weighted ensemble of the weak learners. Pdiff C|diff A P (di erent targets | di erent value of A and nearest instances). e primary idea of RReliefF is that good attributes should separate instances with signi cantly different target values and not separate instances with close target values
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call