Optimal Computer Control Of The Basic Oxygen Furnace Process By Self-Adapting Systems For Static And Dynamic Models

A preliminary description is made of the field of automation and optimization of the oxygen blast furnace process. The concept of static and dynamic process control is illustrated. After a short general description of all factors concerning the optimization of this process, a learning system is derived which gives better results than models previously used. This learning or self-adaptive control system uses statistical methods with exponential aging of process data and requires derivation of multi-variable functions of the process for which measurements can be made available. The practical application of this new procedure and of additional measuring instruments are discussed, and results obtained in plants using the adaptive control system are described. A survey of European steel plant automation using the Siemens technique is presented.