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With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.

The detection of paraffin deposits in the systems of main oil pipelines today is a very important problem, since they lead to emergency oil spills, environmental disasters and economic losses both for the enterprise and for the country as a whole. This work is aimed at studying the physicochemical properties of asphaltic resin paraffins, as well as the mechanism of phase transition from liquid to crystalline. Such studies make it possible to estimate the absorption coefficient of the paraffin phase, which was previously not possible due to the complex nature of oil, consisting of hydrocarbons and many organic compounds of various molecular weights, and to provide high-precision non-contact measurements of the concentration of suspended asphalt-resin-paraffins in the oil flow in the pipeline. The analysis of the morphology and chemical composition of paraffins of various deposits is carried out, the dependence of the phase transition depending on the temperature gradient is determined.

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