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.

The heating furnace is a sizeable energy-consuming device in iron and steel industry. In compact strip production (CSP), the size of the heating furnace is large, and the working conditions are complicated. Furthermore, the large fluctuation of furnace temperature leads to large loss of billet combustion and energy consumption. This article deals with the design of hybrid intelligent control based on condition identification for the combustion process of the CSP heating furnace. By analyzing the process of the heating furnace and existing problems, the structure of a hybrid intelligent control system is proposed, and the working conditions are divided into two categories: stable and fluctuating. A fuzzy controller is designed to improve the control accuracy of furnace temperature under the stable working condition, and an expert controller is used to adjust the temperature rapidly under the fluctuating working condition. Besides, calorific value compensation is introduced to reduce the influence of calorific value of gas fluctuation on temperature. After verifying the effectiveness, the proposed method has been used in a steel plant and achieved a good control result. The intelligent control system improves the control precision of the furnace temperature and reduces energy consumption compared with the traditional control system.

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.