Data-Driven Control of Ground-Granulated Blast-Furnace Slag Production Based on IOEM-ELM

An improved online error minimized- extreme learning machine (IOEM-ELM) adaptive control method is proposed by introducing the adding and pruning mechanism of hidden nodes to realize the control of a kind of MIMO system with multiple operating modes. The strategy to handle the multiple-operating-modes problem is analyzed by the idea of multiple model adaptive control. Further, considering that the ground-granulated blast-furnace slag (GGBS) production process is a complex system with the characteristics of multiple operating modes, high nonlinearity, strong coupling, and high uncertainty, a data-driven intelligent control scheme is designed based on the proposed IOEM-ELM neural network. By analyzing the numerous production data produced in normal and abnormal situations, three typical operating modes are extracted to fully depict the actual production process as a testing platform. As the network structure adjusts dynamically using the IOEM-ELM method, model, and controller are designed to deal with the GGBS production process operating among multiple modes. The example shows that the proposed method can handle changing modes, and reduce the computation of GGBS production process effectively.