Monitoring and Quality Control of Diesel Fraction Production Process

In this work the mathematical model of diesel fraction and atmospheric gasoil catalytic dewaxing process has been developed. Also the pattern of applying the created model to solving such problems as monitoring and quality control of diesel fraction production in the catalytic dewaxing process. It has been represented that to meet such challenges, the model should take into consideration thermodynamic and kinetic laws of hydrocarbon conversion on the catalyst surface, and instability factors that are specified by catalyst deactivation. The developed model allows controlling the quality of obtained diesel fraction depending on feed and temperature regime in the reactor. The value of model calculation absolute error does not exceed 2%, which corroborates the adequacy of the model to actual process. The computations using the model have shown that to provide the desired product yield (not less than 40% wt. of overall yield of the unit products) of programmed quality (cold filtering plugging point not higher than minus 34°C for winter diesel fuels and not lower than minus 40°C for arctic ones) at long-time catalyst operation (during 4 years), it is necessary to sustain the reactor temperature at the average level of 19°C higher than when working with fresh catalyst. This must be done to compensate catalyst activity loss due to its deactivation

ANALYSIS OF THE USE OF BIG DATA TO IDENTIFY THE LEVEL OF ECONOMIC LOSSES OF ELECTRIC GRID COMPANIES

В статье проанализированы возможности использования Big Data для выявления уровня экономических потерь электросетевых компаний. представлены целевые ориентиры ПАО «Россети». На сегодняшний день электросетевой комплекс остается консервативной отраслью, в которой внедрение Big Data не так активно, как в финансовой сфере. Внедрение технологии Big Data во всю энергетическую систему РФ невозможно без создания активно-адаптивных сетей. Проанализированы источники экономических потерь электросетевых компаний на основе зарубежного опыта.The article analyzes the possibilities of using Big Data to identify the level of economic losses of electric grid companies. The targets of PJSC ROSSETI are presented. To date, the electric grid complex remains a conservative industry in which the introduction of Big Data is not as active as in the financial sector. The introduction of Big Data technology into the entire energy system of the Russian Federation is impossible without the creation of active adaptive networks. The sources of economic losses of electric grid companies are analyzed on the basis of foreign experience