A modified ELM algorithm for the prediction of silicon content in hot metal

Silicon content in hot metal in the iron-making process has been used as a major indicator of the internal thermal state in blast furnace for many years. Due to the harsh environment in the blast furnace, how to measure the silicon content in hot metal is quite difficult. Many efforts have been made on the estimation of the silicon content in hot metal. In this paper, a soft-sensing modeling method based on a modified extreme learning machine is proposed to tackle the problem. In this approach, a modified pruning algorithm is utilized to optimize the weights which are generated randomly in the original ELM algorithm. The real data collected from a blast furnace in the factory are applied and tested by the proposed algorithm, and the results show that the proposed prediction model has less error than the other algorithm such as BP algorithm and support vector method.