Intelligent decision support system of operation-optimization in copper smelting converter

Abstract

An artificial intelligence technique was applied to the optimization of flux-adding systems and airblasting systems, the display of on-line parameters, forecasting of mass and compositions of slag in the slagging period, optimization of cold material-adding systems and air-blasting systems, the display of on-line parameters, and the forecasting of copper mass in the copper blow period in copper smelting converters. They were integrated to build the Intelligent Decision Support System of the Operation-Optimization of Copper Smelting Converter (IDSSOOCSC), which is self-learning and self-adaptating. Development steps, monoblock structure and basic functions of the IDSSOOCSC were introduced. After it was applied in a copper smelting converter, every production quota was clearly improved after IDSSOOCSC had been run for 4 months. Blister copper productivity is increased by 6%, processing load of cold input is increased by 8% and average converter life-span is improved from 213 to 235 furnace times.

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