A Model Predictive Approach to Blast Furnace Operational Management Automation
Автоматизация оперативного управления доменным процессом с использованием модельно-упреждающего подхода

L.S. Kazarinov, D.A. Shnayder, T.A. Barbasova, A.A. Basalaev, O.V. Kolesnikova, A.V. Lipatnikov
2016 Bulletin of the South Ural State University Ser Computer Technologies Automatic Control & Radioelectronics  
A promising work direction to improving the efficiency of blast-furnace processes control is application of methods, based on modeling and predictive solutions. In general, the use of blast furnace models has a great history and a large number of sources on this topic is available. It is necessary here to note the works of national authors I. Tovarovskiy, A. Gotliba, G. Efimenko, A. Gimmelfarb, A. Pokhvisnev, O. Onorine, N. Spirin, A. Ramma,. The works of V. Parshakov [12] [13] [14] [15] ,
more » ... 3] [14] [15] , devoted to study of influence of the cohesion zone parameters on the blast furnace process efficiency, deserve special attention. It is necessary to note among foreign authors the works of J. Kule [27][28][29][30] [31] [32] [33][34] [35] [36] [37] [38] [39][40][41]. However, as far as the blast furnace process is quite sophisticated and its parameters are not fully observable the specified problem is not completely solved now and studies on the topic are still conducted. The main features of the proposed approach are: -usage of the operational data mining software to identify effective regions of the blast furnace technical parameters values, providing productivity increase and coke consumption reduction; -real-time software for identification of the furnace cohesion zone current parameters for the operational management correction; -forecasting of the blast furnace thermal state indicators dynamics when the blast parameters or charge load change. Blast furnace operational management automation using modelling and real-time predictive solutions for the object control are considered. Main features of the proposed control are: using of an operational data mining software to identify effective clusters of the furnace regime parameters values; real-time software for identification of the furnace cohesion zone parameters for the operational management correction; dynamics forecasting of the furnace thermal state indicators when charge load and blast parameters change. Usage of the software permits to achieve effective values of the furnace regime parameters with high productivity and reduced coke consumption. It is effective in conditions of the significant charge parameters changes, due to using of source materials from different suppliers. Therewith, forecasting of parameters dynamics allows supervisor to stabilize the blast furnace process in the effective regime. The system is based a joint
doi:10.14529/ctcr160405 fatcat:q6vnri2p7vcf7hn4tztht4lole