A Soft Sensing Scheme of Gas Utilization Ratio Prediction for Blast Furnace Via Improved Extreme Learning Machine

摘要

Gas utilization ratio (GUR) is an important indicator reflecting the operating state and energy consumption of blast furnace (BF). Skilled operators usually refer to changing trends of GUR to guide the next step of production. For these reasons, this paper establishes a soft sensing scheme based on an improved extreme learning machine (ELM) to predict GUR. In order to enhance the modeling capability of ELM for industrial data, an improved ELM, named GR-ELM, is proposed based on grey relational analysis (GRA) and residual modification mechanism. In GR-ELM, considering the different effective information contained in each input attribute for modeling, the input attribute optimization is proposed combining with GRA and entropy weight method. Then, because the modeling capability of ELM is limited and the data collected from industrial process are usually contaminated, the residual modification mechanism is implemented to improve the reliability of the model. In addition, considering the influence of time delay in BF ironmaking process, generalized correlation coefficient method based on mutual information is used for time delay analysis to eliminate the influence. The real data collected from a BF are applied and validated the performance and effectiveness of the proposed soft sensing scheme. The experimental results show that the proposed soft sensing scheme is available and can achieve better performance than some state-of-the-art algorithms. The soft sensing scheme can provide effective decision support and guidance for further optimization operation.