Saint Petersburg Mining University, Saint Petersburg, Russia:

N. V. Vasilieva, Associate Professor at the Department of Industrial Automation, Сandidate of Technical Sciences, e-mail: vasileva_nv@pers.spmi.ru
E. R. Fedorova, Assistant Professor at the Department of Industrial Automation, Сandidate of Technical Sciences, e-mail: fedorova_er@pers.spmi.ru

All production processes are controlled by control systems, in which several information flows are generated. However, operators only use a small percent of this information as the processing capacity of a human mind is limited. The paper demonstrates that production process control is to a great extent influenced by human factor. The paper describes a production data processing technique that enables the personnel to make a better use of their resources for operations control. An approach is considered to studying metallurgical processes through analysis of indirect indicators – i.e. the spectral density and autocorrelation function of the key process indicator signals. A method is described to check the efficiency of material flow control systems. The above techniques were applied to a big array of monitoring data collected during a 24-hour smelting operation for copper-nickel sulphide material. Data from three different shifts were used for this analysis. Different operators have different control patterns. The proposed technique, which enables to analyze big arrays of monitoring data, helps minimize the human factor. The adopted experimental data processing technique helps interpret the obtained results for further practical use, development of new control algorithms and optimization of the current control system.

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