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In the process of smelting copper mineral a large amount of sulphuric dioxide (SO2) is produced. This compound would be highly pollutant if it was emitted to the atmosphere. By means of an acid plant it is possible to transform SO2 into sulphuric acid. However, there are certain situations in the process of smelting copper mineral, in which SO2 escape to the atmosphere. This would be avoidable if we exactly knew under which circumstances this problem is produced. In this paper we present a practical application of KDD process, with an evolutionary algorithm as Data Mining technique, to the chemical industry. With this technique we obtain rules that make possible the definition of procedures that should help to optimize the functioning of the sulphuric acid production system. By means of the obtained results we show the viability of using automatic classifiers to improve a productive process, with decrease of the environmental pollution.