Hydrotreating of diesel fuel aims to reduce the sulfur content in the fuel to 10 ppm to meet environmental standards. However, this deep purification of diesel requires the use of expensive catalysts at hydrotreating plants with giant reactors with a capacity of 200-600 cubic meters. Such large volumes of reactors are associated with classical kinetic methods for chemical reactions, where the feedstock is in the reactor until the required conversion depth is reached. All known mathematical models for diesel hydrotreatment have a common drawback: they rely on approximations about the composition of multicomponent raw materials containing dozens of different organic sulfur compounds that react differently in hydrogenation reactions. This raw material is often presented in a mathematical model as a combination of two to six pseudo-components or lumps combining organosulfur impurities from one or more homologous groups. This theoretical basis allows us to simulate the current state of hydrotreating technology, but does not develop and promote it. We propose a new approach to mathematical modelling of diesel fuel hydrotreating, in which the structure of the mathematical model considers the composition of raw material as a set of 10-20 narrow fractions. The set of hydrogenated organosulfuric impurities within each fraction is treated as a single pseudocomponent. This allows us to integrate the system of differential equations of the model and adapt the rate constant to the concentration of hydrogenated organosulfur impurities at any given time during the process. The developed model has also allowed us to propose a new technology, hydrotreatment: separating the feedstock into two or three wide fractions, combining the corresponding narrow fractions, and then subjecting them to individual hydrogenation processes. As a new approach, this differential hydrotreatment technique will reduce the catalyst load in the hydrotreatment unit by approximately 50%, while maintaining efficiency of processing, or double efficiency while maintaining a similar catalyst load using traditional technology.
Ethics Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data supporting the conclusions of this study are available from author upon reasonable request.
Funding
Not funding.
Declaration of Competing Interest
The author declare that have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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