This article proposes a comprehensive approach to training process operators, based on computer simulators integrated with digital twins, SCADA/DCS systems, real production data, and artificial intelligence algorithms. This approach is particularly relevant given the increased requirements for safety, productivity, and reliability of industrial facilities, as human factors are often the main cause of accidents (up to 60-80% of cases). The architecture of the simulator complex is designed to accurately emulate steady-state, transient, and pre-emergency operating modes of equipment, which are not achievable under actual production conditions. The experiment compared the training effectiveness of two groups of operators: one that used a digital simulator and another that underwent traditional theoretical training. The study revealed that operators trained using the simulator achieved a 90-95% mastery level in knowledge and practical skills by the twelfth day of training. In contrast, operators trained using the traditional method did not exceed a 65-75% mastery level. Using the simulator led to a 30-45% reduction in critical errors and a 20-35% increase in reaction speed to environmental changes. Furthermore, it significantly enhanced the reliability of decision-making algorithms. The artificial intelligence module can automatically classify errors, create personalized learning plans, and track the progress of professional skill development. Research confirms both the scientific and practical value of the proposed approach. The integration of digital twins and artificial intelligence into the educational process serves as an effective means of training specialists for the chemical, oil and gas, and related industries. The use of computer simulators significantly improves the quality of training, minimizes the impact of human factors, and prepares personnel for effective response to emergency and non-standard situations. The widespread application of such systems in industry, as well as their incorporation into personnel retraining programs and technical education, appears highly beneficial.
| Mualliflar | Kamola Rustamovna Abdullaeva |
|---|---|
| Jurnal | Кимёвий технология. Назорат ва бошқарув |
| Nashr sanasi | 2025-12-28 |
| Jild | 2025 |
| Son | 6 |
| Betlar | 117-123 |
| Til | en |
| DOI | 10.59048/2181-1105.1742 |
DOI: 10.59048/2181-1105.1742 · Maqolaning asl sahifasi
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