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

Dual ensemble online modeling for dynamic estimation of hot metal silicon content in blast furnace system

Hot metal silicon content (HMSC) is usually utilized to measure the quality of hot metal and reflect the thermal status of blast furnace (BF) system. However, most state-of-the-arts ignore the time-varying behavior of BF ironmaking process, which are impractical. Accordingly, a novel dual ensemble online sequential extreme learning machine (DE-OS-ELM) is proposed to establish the online estimation model of HMSC, which can update the data-driven model with the latest operation data. Specifically, an online learning method with recursive modification is first proposed based on OS-ELM (referred to as RM-OS-ELM) to address the modeling with uncertainty. To heel, a dynamic forgetting factor is presented for the dynamic tracking capability enhancement and convergence acceleration. Furthermore, a final updating rule for sequential implementation is constructed by combining the output weights of OS-ELM and RM-OS-ELM based on their corresponding contributions on modeling. Considering the modeling accuracy and curve trend consistency, multiobjective parameter optimization model is also implemented to achieve the satisfactory performance. By taking the proposed DE-OS-ELM, the estimation model of HMSC is established using industrial data. Comprehensive experiments demonstrate that DE-OS-ELM-based HMSC estimation model is more feasible and practical.