Abstract
Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.
Highlights
Mechanism modeling of the sintering processThe quality of sintered ores greatly influences the yield and efficiency of blast furnaces, so accurately predicting technical indices of sintered ores including iron grade, alkalinity, and drum index is the premise of effectively improving the quality of sintered ores
Introduction to identify the parameters thereinsimulation proves that the model is effective
The errors in the prediction of the mechanism models are adopted as the modeling objective to build the error compensation model based on time-weighted extreme learning machine (ELM)
Summary
The quality of sintered ores greatly influences the yield and efficiency of blast furnaces, so accurately predicting technical indices of sintered ores including iron grade, alkalinity, and drum index is the premise of effectively improving the quality of sintered ores
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