The key avenues of research concern the application of the latest techniques, including big data analytics, machine learning, distributed ledger systems to solve a wide spectrum of industry tasks in the area of petrochemistry with the use of specialized software, high-performance computing equipment and quantum technology solutions
Predictive analytics encompasses a variety of statistical and machine learning techniques that analyze historical facts to make predictions about the future
A mathematical model built in the form of Python software allows calculating the solid catalyzed isobutane olefin alkylation process under a range of conditions.
The model utilizes physical-chemical dependencies based on data analysis and performs calculations with a high degree of accuracy and adequacy.
In addition to standard algorithms utilized to run such models (solution techniques for differential equations, optimization, interpolation and extrapolation tasks), this model incorporates experience and knowledge of specialists and subject matter experts in the form formalized rules and heuristics.
A mathematical model built in the form of Python software allows calculating the solid catalyzed hydrotreatment process under a wide range of conditions. The model utilizes physical-chemical dependencies, as well as the most effective and suitable empirical dependencies presently known. In order to bring the model to exact correspondence with the actual refinery process, it could be adapted (calibrated) to various reactor structures and catalyst system operation (catalyst composition, type and activity). The model incorporates a procedure to assess the catalyst activity using data mining techniques based on the reactor operation data.
Two modes are available: thermal or oxidative (with or without a catalyst). The model utilizes physical-chemical dependencies based on experimental data and the analysis of big data collected from actual industrial units. The thermal process is calculated based on Snow-Shatt kinetic equations.
The calculation can be made for a wide range of process parameters. Temperatures are estimated using the Runge–Kutta 4th-Order method. Equation factors for the reactor’s in- and outlet heat levels are calculated by means of interpolation. Inlet temperature and pressure, flow rate and molar composition of raw materials are set by the user. The calculation results allow determining the product material/heat balance, conversion, selectivity, yield and the optimum extent of the reaction.
• Deployment of Ethereum or HyperLedger Fabric based private/consortium blockchain applications
• Solidity, Python or Golang development of smart contracts tailored to an enterprise's business objectives
• End-to-end development of decentralized applications
• Logistics and document flow
• Corporate governance
• Control, dosing and accounting of petroleum products
• Application of distributed ledgers in the system of petrol/hydrogen filling stations
• The use of blockchain technologies in the field of machine learning, Deep Learning and Big Data
A set of theoretical, experimental and numerical techniques to simulate the flow of liquids and gases, as well as heat and mass transfer processes, reactant flows, etc.
We have developed a CFD model of a hydrodynamic mode of a solid catalyzed isobutane-olefin alkylation reactor and analyzed the design of a distributed residence time section reactor. CFD modeling results lead to a conclusion that the process is performed at low linear flow rates.
The ultimate differential pressure estimates are consistent with the experimental data and calculations from Python-developed mathematical model software.
The results reveal no stagnant zones or phenomena in the velocity or pressure fields of the reactor, which will favorably affect the catalyst performance and durability.
We have developed a CFD model of a petroleum fraction hydrotreatment reactor and analyzed the design of a vertical fixed-bed catalytic quenching reactor.
CFD modeling results lead to a conclusion that the process is performed at permissible linear flow rates. The ultimate differential pressure estimates are consistent with the experimental data and calculations from Python-developed mathematical model software. During the CFD modeling, we have further studied the designs of distribution plates to assess the distribution of the flow at the inlet to the catalyst bed.
We have studied the design of a fluidized bed catalyst reactor. The model was built based on kinetic and thermal effects.
The results reveal no stagnant zones (thus, no overheating) in the velocity, temperature or pressure fields of the reactor, which will favorably affect the catalyst performance and durability. The ultimate resultant ant temperature estimates are consistent with the industrial data and calculation from kinetic model software.
During the CFD modeling, we have further studied the cyclone filter design. Throughout the simulation no catalyst particles were found in the reactor effluent gas which indicates satisfactory operation of the built-in cyclone filter and the absence of catalyst carry-overs at a given process mode. As a result, the key goal has been achieved – to create a valid model of a new petrochemical process conducted in a complex reactor.
AR technology helps to significantly reduce transaction costs and facilitate more accurate decision-making for companies operating in a less well-defined industries, such as the oil & gas industry.