Gazpromneft-Logistics is one of Russia’s top three oil-refining companies. For 2017, the company’s refining were 40.11 million tonnes of crude oil.
Gazpromneft-Logistics currently owns five refining assets, including three own (Moscow Oil Refinery,
Omsk Oil Refinery and refinery complex NIS) and two companies in joint use (Slavneft-
YANOS and Mozyr Oil Refinery). Omsk and Moscow refineries are major refining capacity of company.
– total refining volumes at Gazprom Neft refineries in 2017
– investment in modernising the Omsk and Moscow Refineries over the past five years
– post-modernisation conversion rate at the company’s Russian refineries
Gazpromneft-Logistics has been implementing ambitious programmes to modernise its production facilities since 2009. These programmes not only enhance the quality of the Company’s products, but also improve the environmental performance of motor fuel and minimise the environmental impact of its plants.
– the total number of production parameters
that will be monitored by the centre
transmitting information to the ECC
– the anticipated reduction in energy consumption as a result of process optimisation
In 2017 Gazpromneft-Logistics established its unique Downstream Efficiency Control Centre in St Petersburg, controlling the entire process from oil being received at refineries, to retail sales of oil products.
The range of products produced at the Company’s refineries includes gasoline, diesel fuel, lubricants, construction and road bitumen, marine fuels, heavy fuel oil (mazut), jet fuel, paraffin, wax products and a range of aromatic hydrocarbons, liquefied petroleum gases.
errors in equipment operation reduced two- to three -fold
equipment run times increased to six to seven years
Digital twins are virtual copies of actual physical objects.
A digital twin of an oil refinery is a complex mathematical model that uses “Artificial Intelligence” (AI) algorithms, containing full information on every element of the facility, production process data, energy consumption, and the performance and specifications of raw materials and finished products. It helps select optimum equipment operating parameters in predictive mode, predict failures or breakdowns, and take decisions on repair run times.
A further area of strategic development is improving production efficiency through the use of cutting-edge digital technologies — including, in particular, digital twins. Using complex mathematical models of actual refinery facilities makes it possible to select optimum equipment operating parameters, predict failures or breakdowns, and take decisions on repair run times.
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