The iron ore sintering process is the second most energy-consuming process in iron and steel production, where the main energy consumption results from the combustion of carbon. In order to optimize the utilization of carbon, it is necessary to predict the carbon efficiency first. Thus, this paper develops a multilevel prediction model to predict the carbon efficiency. First, the comprehensive coke ratio (CCR) is employed as an index to measure the carbon efficiency. Based on the mechanism analysis, the sintering parameters affecting the CCR are determined. Next, an improved fuzzy C-means (FCM) clustering method is used to automatically identify the optimal number of working conditions for the sintering parameters. Then, the least-squares support vector machine (LS-SVM) submodels are established for different working conditions. To improve the generalization ability of the model, a differential evolution algorithm is developed to optimize the parameters and the weights of the LS-SVM submodels. Finally, the multilevel prediction model for the CCR is established by aggregating LS-SVM submodels. The simulation results based on actual run data demonstrate that the prediction accuracy of the multilevel prediction model is higher than that of a backpropagation neural network model and an FCM-LSSVM model, and the proposed modeling method satisfies the requirements of the actual sintering production.

Sinter ore is the main raw material for ironmaking, and burn-through point (BTP) is one of the significant factors to measure the stability of the sintering process. In this paper, through the feature extraction of time series trend, a fuzzy control strategy is presented for the BTP. First, the Hurst Exponent of the time series for the BTP is calculated by resorting to the rescaled range analysis method, by which the trend feature is analyzed. Then, by using the Mann-Kendall test, both global and local trend feature variable of the time series for the BTP are extracted and regarded as the inputs of the fuzzy controller. Next, a fuzzy controller for the BTP is designed to produce the control quantity of the strand velocity. Finally, based on a semi-physical simulation system and the raw data collected from an iron and steel plant, an experiment is carried out to demonstrate the effectiveness of the proposed control strategy.

Burning-through point (BTP) control is the key to guranteeing the sintering process and sinter quality. Due to an opening combustion and multiple input conditions, the combustion process of the sintering process shows a typical characteristic of time-varying operation conditions. This paper analyzes the influence factors resulting in time-varying operation conditions and then constructs an improved Takagi-Sugeno (T-S) fuzzy model of BTP based on the linear parameter varying (LPV-TS-BTP model), in which the BTP temperature and the central bellows temperature are taken as time-varying parameters. The model explains how the different working conditions change the systems dynamics and describes this time-varying nonlinear model in the form of a linear combination model. Then, a fuzzy robust control method based on the LPV-TS-BTP model is designed by using the parallel distributed compensation principle to realize the BTP control of complex process, in which the controller parameters are achieved by using the linear matrix inequality (LMI) method. The simulations about modeling and control, which are carried out on a semiphysical simulation platform for the sintering process, verify that the model has a high prediction accuracy, and the fuzzy robust control makes BTP stable around a given bellows.

The iron ore sintering process is an important step in preparing raw material for ironmaking. How to reduce carbon consumption while ensuring the stable running of the sintering process is an urgent problem to be solved. In this brief, an intelligent integrated control strategy for the burn-through point (BTP) to carbon efficiency optimization in the sintering process is presented. The comprehensive coke ratio (CCR) is employed as a measure of carbon efficiency, and the BTP is a measure of the stability of the sintering process. First, a short time scale model is established to predict the CCR, and the carbon efficiency is optimized by using the particle swarm optimization algorithm. This yields an optimal carbon efficiency and one control quantity of strand velocity. Another control quantity of strand velocity is obtained by a BTP expert-fuzzy controller. Both control quantities are integrated by a well-designed intelligent integrated controller, so that the optimal strand velocity, as the final control input, is determined. An experiment is carried out to verify the effectiveness of the proposed strategy. The experimental results show that the proposed strategy improves the carbon efficiency while ensuring the stable running of the sintering process, which has a good application prospect in the industrial site.