Vol 57, No 2 (2023)

The kinetics of the reactions of selective hydrogenation of acetylenic hydrocarbons in ethane–ethylene and propane–propylene fractions (EEF and PPF) of pyrogas on a polymetallic nickel catalyst promoted with group I, III, IV, and VI metals is studied. The dimensions of the cylindrical granules used in the catalytic studies are 2 mm diameter and 10 mm length. The nickel content in the catalyst is less than 32 wt %. The kinetic experiments are carried out in a laboratory flow reactor 2 cm in diameter with a reaction-zone length of 20 cm and a bench reactor with an inner tube diameter of 3.2 cm and a reaction-zone length of 600 cm. Experimental conditions were varied as: feed flow rate of 2000–42000 h–1, reaction-zone temperature 330–410 K, pressure 1–30 atm, and hydrogen : acetylenic hydrocarbon molar ratio 2–10. A staged two-route mechanism for the reaction of hydrogenation of acetylenic hydrocarbons is proposed and a kinetic model corresponding to it is derived. A total of 80 experiments are carried out on laboratory and bench installations. The kinetic model constants and the macrokinetic constants of the bench reactor model are estimated by the nonlinear least-squares method. The correspondence of the proposed models to the experimental results is shown. The possibility of joint purification of EEF and PPF pyrogas in a single reactor with an increase in olefins in the product stream compared to the feed stream is demonstrated.

The extractive distillation of three binary mixtures (acetone–chloroform, acetone–methanol, and allyl alcohol–allyl acetate) in traditional two-column schemes and in sequence with partially coupled heat and material flows is considered. The optimal scheme parameters according to the criterion of total energy costs in column boilers are determined. It is shown that the use of sequence with partially coupled heat and material flows provides energy cost savings of 4.4–29.2% compared to the traditional scheme. It is found that during the extractive distillation of the allyl alcohol–allyl acetate mixture, the optimal values of the extractive agent flowrate for the two-column scheme and the scheme with partially coupled heat and material flows coincide; the optimal flowrate of the extractive agent in the scheme with partially coupled heat and material flows in the separation of an acetone–chloroform mixture is 1.28 times lower, and in the separation of an acetone–methanol mixture, it is 1.27 times higher than in the two-column scheme. The reasons for these differences are established.

The problem of extraction a target component from a cylindrical pore is studied. The solution of this problem is used as a basis for the mathematical model of mass transfer in a porous medium. The proposed model is applicable for the description of a number of processes, in particular, extraction and sediment washing. Some graphical illustrations explaining the process of washing within the proposed model are given.

The paper presents a data based surrogate model of the chemical kinetics of hydrogen oxidation by air using recurrent and feed-forward neural networks. The work aims at the application of surrogate models in computational fluid dynamics simulators, which are ubiquitous in the development and optimization of modern chemical technologies. The sensitivity of the results to the size of the data set and network parameters is analyzed. For a seven-component reaction mechanism at adiabatic conditions, a model trained on a sample of one million sets of initial conditions enables prediction of the dependence of concentrations and temperature on time with a standard deviation below 2% over 20 microsecond range. However, points with large deviations reaching 10% are also observed, mostly for minor components with low concentrations. The surrogate model is several times faster compared to the direct numerical solution of kinetic equations on the temporal grid. The computational performance strongly depends on the batch size and is sensitive to the hardware. The results of the work demonstrate a significant potential of machine learning methods for modeling chemical transformations in computational fluid dynamics solvers. Further improvement of the accuracy with a similar computational performance can be expected from: (a) separate models for short-time (that is, strongly non-equilibrium) and long-time (closer to the equilibrium) ranges; (b) repeated optimization of network parameters even with minor modifications of the reaction mechanism; (c) more versatile approaches to complying with the conservation laws (d) application of physics informed machine learning (e.g. of the models with additional physical and chemical constraints such as mass conservation).

Mixing is one of the most common processes in various branches of the chemical industry: food, construction, oil refining, pharmaceuticals, and others. Increasing the intensity of mixing can be achieved by changing the speed or direction of movement of the impeller, which is confirmed by studies of domestic and foreign authors. In this paper, a planetary drive of a stirred tank is considered, which makes it possible to implement various types of uneven movement of the impeller. The advantages of the proposed mechanism over existing analogues are compactness, reliability, and ease of setting the coefficient of uneven rotation of the agitator. On the basis of the IKA laboratory reactor, an experimental setup is constructed, with the help of which comparative tests of the heat-transfer intensity in a traditional mixing device with a constant rotational speed and using the proposed planetary mechanism are carried out. As a result of the experiment, it is found that the transmission to the impeller of uneven movement can reduce the mixing time, as well as increase the energy efficiency of the apparatus.