This paper investigates the optimal control problem of vacuum gas oil catalytic cracking, a key process in petroleum refining for producing valuable fuel fractions. The reaction is highly complex and nonlinear, requiring precise control of key parameters such as reaction temperature and time. The optimal control task is reformulated as a nonlinear global optimization problem and solved using the modified Particle Swarm Optimization (PSO) algorithm. The proposed modifications enhance solution feasibility and practical implementation. Using this approach, we analyze the impact of temperature and reaction time on process efficiency under both fixed and optimized conditions. The results demonstrate that optimal parameter selection significantly improves conversion rates and product yield. The paper presents all numerical experiment results and implementation details.

This paper studies the gains and losses of parallelization of evolutionary algorithms. It is known that the computing system’s parallel architecture should be taken into account to successfully parallelize an evolutionary optimization algorithm. However, it can lead to a modification of the sequential version of the algorithm, which results in poorer yet faster performance. We study an optimal control problem for a complex chemical reaction, namely, the hydrogenation of polycyclic aromatic hydrocarbons in the presence of catalysts. This optimal control problem was transformed into a global nonlinear optimization problem, which was solved by the Mind Evolutionary Computation (MEC) algorithm. We propose a few parallel modifications of the MEC algorithm which utilize different task parallelization techniques and algorithm parallelization approaches. Those modifications were used to solve the optimal control problem of hydrogenation of hydrocarbons in the presence of the nickel-kieselguhr catalyst. For the efficiency analysis, several metrics were used to evaluate a solution’s speed and quality. The results of all numerical experiments are presented in the paper with the obtained optimal control for the chemical reaction under investigation.

In this paper, the optimal control problem of hydrocarbons’ hydrogenation was investigated in the presence of two catalysts—Nickel–Kieselguhr and Raney Nickel. This multistage chemical reaction holds significant practical importance, particularly in the production of high-density fuels. The optimal control problem was reformulated as a nonlinear global optimization problem and addressed using a modified Mind Evolutionary Computation algorithm. The proposed modifications include methods designed to ensure solution feasibility and ease of practical implementation. Using the proposed method, the performance of the two catalysts was compared under constant temperature conditions and with optimal control strategies. The results demonstrate that selecting an effective catalyst has a greater impact on the reaction’s efficiency than temperature control alone, with the Raney Nickel catalyst consistently outperforming the Nickel–Kieselguhr catalyst by at least 17%. Additionally, the optimization approach was applied to identify a new set of catalyst parameters. The newly obtained catalyst parameters allowed for the improvement of the results of the Raney Nickel catalyst by 18%. The results of all numerical experiments and implementation details are described in the paper.

The catalytic properties of mesoporous amorphous titanosilicate Ti–Si in acetone homocondensation at 250–350°С and mass feed space velocity of 0.5–10 h–1 were studied. The acetone conversion was 13–52 wt

The kinetic model of the synthesis of methyl-tert-butyl ethers by intermolecular dehydration of tert-butanol with methanol using HY zeolite with a hierarchical structure as a catalyst (HYmmm), as well as a catalyst system based on CuBr2 deposited on HYmmm, is considered. A kinetic model was constructed and a multistage scheme of chemical transformations was developed based on the experimental data. The kinetic model is based on the law of acting surfaces with allowance for adsorption and desorption processes on the catalyst surface. The inverse problem was solved in the form of a global optimization problem, which made it possible to determine the parameters of the kinetic model—the kinetic constants and activation energies.

Предложен подход к численному моделированию неизотермической задачи фильтрации в трещиновато-пористой среде, основанный на методе расщепления по физическим процессам. В задаче учитывается наличие двухфазной жидкости и двойной пористости у коллектора. Применение метода расщепления по физическим процессам позволяет упростить алгоритм решения, при этом сохранив эквивалентность к консервативной разностной аппроксимации исходных уравнений и обеспечив устойчивость решения задачи. При численном решении используются аппроксимации дифференциальных операторов, полученные в рамках метода конечных разностей. Реализация численного алгоритма основывается на методе матричной прогонки. Апробация метода и его верификация выполнена в серии вычислительных экспериментов, исходные данные для которых взяты из исследований промысловых скважин на российских нефтяных месторождениях. The work proposes an approach to numerical modeling based on splitting by physical processes for a non-isothermal problem of filtration in a fractured-porous medium. The task is complicated by the presence of a two-phase fluid and dual porosity of the reservoir. The system of equations defining the model is complex and is described by a system of strongly non-linear partial differential equations. The use of the splitting method according to physical processes makes it possible to simplify the solution algorithm while maintaining the equivalence to the conservative difference approximation of the original equations and ensuring the stability of the problem solution. In the numerical solution, the approximations of differential operators obtained in the framework of the finite difference method are used. The implementation of the numerical algorithm is based on the matrix sweep method. To test the method, a series of computational experiments were carried out. Calculations have shown that the developed methodology is correct and allows one to simulate the dynamic operating conditions of wells.

An algorithm for determining the permissible interval values of variable parameters in solving the problem of multi-criteria optimization of conditions for a multi-stage chemical process is considered. The mathematical model of kinetics is given in the form of an interval problem, according to the intervals of kinetic parameters, and its solution is carried out by a two-line method. This approach makes it possible to take into account possible fluctuations in the reaction conditions and their influence on the values of the optimality criteria. This is especially important for industrial processes and must be taken into account when modeling laboratory reactions with subsequent introduction into production. The object of research in this work is the catalytic reaction of hydroalumination of olefins in the presence of triisobutyl aluminum, the product of which are higher aluminogranic compounds. The developed kinetic model of the process takes into account possible parameter intervals, due to fluctuations in the reaction temperature. And the solution of the multi-criteria optimization problem makes it possible to take into account the optimal conversion interval of the initial olefin. To assess the adequacy of the solution, a comparison is made with experimental data and according to the criteria for evaluating the quality of Pareto approximation. The algorithm of parallelization of calculations for the task of multi-criteria optimization using interval analysis is proposed.

This paper deals with a problem of optimal control of multi-stage chemical reactions which often is considered as a non-linear global optimization task. One of the distinct features of this type of problems is the variety of control parameters in scale, type, and units. For example, control temperature can vary within tens of degrees and control time can be measured in thousands of seconds. Such diversity eventually can affect the quality of obtained control and the convergence of the algorithm. In this paper we propose a modification of the parallel memetic algorithm to study the influence of the parameters scaling on the solution’s quality and algorithm’s efficiency. The study was performed with a use of a non-isothermal industrial reaction of the gasoline’s catalytic reforming over a bimetallic catalyst. The output of such a reaction is commercial gasoline for daily usage. The paper contains the description of the proposed parallel memetic algorithm and the results of all computational experiments along with the conclusions.

Проведен математический анализ процесса получения высокоплотных реактивных топлив марок Т-6 и Т-8В, основанный на гидрировании полициклических ароматических (по большей части бициклических) углеводородов. Процесс проводился на пилотной лабораторной установке с использованием двух никелевых катализаторов – никеля Ренея и никеля на кизельгуре. Экспериментальные данные, полученные для температур 200–400°С, разных объемных скоростей подачи сырья, использованы для построения математической модели процесса каталитического гидрирования углеводородов, учитывающей изменение объема реакционной смеси. Наблюдается хорошее согласие полученных в рамках математической модели и измеренных концентраций моно- и бициклических ароматических углеводородов (исходные и промежуточные соединения) и нафтенов (целевые продукты процесса). Решение обратной кинетической задачи позволило оценить кинетические параметры основных химических превращений процесса гидрирования ароматических углеводородов.

Introduction . Рolyarylene phthalides (PAF) are widely used in optoelectronics today. The reactions occurring during the synthesis of polyarylene phthalides have a complex character, which has not yet been described using mathematical models. In this regard, it is impossible to use PAF in many processes. Рolyarylene phthalides have luminescence, good optical and electrophysical properties. The elucidation of the mechanisms of the occurrence of luminescent states of PAF is of both fundamental and practical interest. The elucidation of the mechanisms of the occurrence of luminescent states of PAF is of both fundamental and practical interest. Due to the complexity of calculating the kinetics of the luminescence intensity of polyarylene phthalides using known mathematical models, the aim of the study was to build a system using machine learning methods that predicts luminescence values depending on temperature and heating time. Materials and methods . Experimental data have been prepared for calculations, the use of “random forest” and “gradient boosting” methods has been justified, a method for selecting hyperparameters of these models has been selected and the expediency of its use has been justified, optimal models have been constructed and predictions have been obtained. The results of the study . An algorithm for predicting the luminescence intensity of polyarylene phthalides has been developed. Using machine learning methods based on experimental data, the key hyperparameters of the system were determined and the average accuracy of predicting values was achieved — 80 %. Discussion and conclusions . High-accuracy forecasts will allow predicting how products containing polyarylene phthalides will react to external influences. The paper presents two methods for solving the problem, as they showed the best results.

Introduction. The formation of salt deposits and oilfield equipment’s corrosion in most oil fields has become particularly relevant due to the increase in the volume of oil produced and the increase in its water content in recent years. The deposition of salts in the formation and wells leads to a decrease in the permeability of the oil reservoir, the flow rate of wells. The aim of the work is to use machine learning algorithms to simulate the effects of an electromagnetic field on the processes of salt deposition and corrosion. Prediction of experimental results will allow faster and more accurate experiments to establish the influence of electromagnetic fields on the processes of corrosion and salt deposition. Materials and methods . Three groups of data were used, to train the models, differing in the composition of the studied initial model salt solution: the waters of the Vyngapurovsk’s and Priobsk’s deposits, as well as tap water. The following machine learning models were used: linear regression with Elastic-Net regularization, the k-nearest neighbors algorithm, the decision tree, the random forest and a fully connected neural network. Results. The processes of electromagnetic field influence on the formation of salt deposits and corrosion of oilfield equipment were simulated with the help of machine learning algorithms. Python program has been developed to predict the output results of experiments. Modeling with various models and their parameters is carried out. Discussion and conclusions . It was found that the decision tree and the random forest have the best accuracy of predictions, from the experiments conducted. This is due to the fact that there is too little data in the training samples. With the increase in the number of observations, it is worth using neural networks of various architectures.

Introduction . The basis for research, analysis and mathematical optimization of any chemical process is an adequate mathematical model that takes into account the kinetics of the object. Kinetic analysis is a challenge in chemical technology, since it allows for optimizing synthesis processes and predicting their efficiency. Numerous chemical processes involve several stage reactions. For successful design and optimization, a mathematical model that describes each stage is needed. Creating such a model manually can be time-consuming and costly, since it requires processing a large amount of information. The modern level of automation makes it possible to accelerate the obtaining of a mathematical formulation of the kinetics of multistage reactions. In this case, working with data is greatly simplified, and the probability of making mistakes is reduced. The resulting mathematical model can be applied for further analysis and optimization of the process. The paper considers the industrial reaction of catalytic reforming of gasoline, which occupies an important place in the modern scheme of oil refining, since it is a source of high-octane components of commercial gasolines and individual aromatic hydrocarbons. This process is characterized by the participation of a large number (up to 300) of various hydrocarbons, a change in the number of moles, and non-isothermality in it. Mathematical modeling of such processes involves detailing the stages to the required level. The detailing of up to 173 stages is considered. In this setting, automation of the formation of a mathematical formulation of kinetics for catalytic reforming of gasoline has not been carried out before. Therefore, the presented work aimed at implementing effective numerical methods and algorithms for automating the building of a mathematical model taking into account kinetics, thermodynamics, and changes in the number of moles. Materials and Methods . The mathematical formulation of the kinetics of multistage reactions was developed on the basis of the mass action law. The kinetic parameters values were taken from literary sources. The direct kinetics problem was solved using algorithms: the Gear method, the Runge-Kutta method of the 4th order, and the scipy.odeint() method of the Python language. The automation concept was implemented using the IDEF0 methodology. The software was written in the Python programming language. Results . A new software was created to automate the process of forming a mathematical model, taking into account the kinetics, thermodynamics, and the volume of the reaction mixture. The program results were presented by the example of catalytic reforming of gasoline. The model implemented the possibility of taking into account the intermediate heating of the mixture in the reactor cascade. Numerical values of temperature changes corresponding to industrial data were obtained. Discussion and Conclusion . The results obtained through modeling chemical transformations in the cascade of gasoline catalytic reforming reactors confirmed the exothermic nature of the reaction. The developed software product provides displaying changes in the concentrations of reactants, as well as temperature variations in the reactor, and it can be used in scientific research organizations for the analysis of multistage catalytic processes. The results of the reaction kinetics modeling will be used in the subsequent optimization of the process conditions in production.

The problem of multicriteria interval optimization of the conditions for complex chemical reactions is formulated based on an interval kinetic model. A solution method is proposed, based on evolutionary optimization algorithms, in the form of an interval Pareto front. An interval kinetic model is developed for the reaction of dimethyl carbonate with alcohols in the presence of a Co 2 (CO) 8 metal complex catalyst, and two-sided limits on component concentrations and kinetic parameters have been determined. For this process, the effect of temperature and its possible disturbance on the values of the optimality criteria is calculated: the yield of the target product and productivity, with appropriate restrictions on changing the width of the interval.

A mathematical analysis of the process for the preparation of high-density jet fuels of T-6 and T‑8V grades, based on hydrogenation of polycyclic aromatic (mostly bicyclic) hydrocarbons, has been performed. The process was carried out on a pilot laboratory plant using two nickel catalysts: Raney nickel and nickel on kieselguhr. The experimental data obtained for temperatures of 200–400°C and different feed space velocities were used to construct a mathematical model for catalytic hydrogenation of hydrocarbons that allows for changes in the volume of the reaction mixture. The concentrations of mono- and bicyclic aromatic hydrocarbons (initial and intermediate compounds) and naphthenes (target products) obtained within the framework of the mathematical model are in good agreement with the measured concentrations. The solution of the inverse kinetic problem made it possible to estimate the kinetic parameters of the main chemical transformations in the hydrogenation of aromatic hydrocarbons.

This paper deals with a problem of optimal control of complex multi-stage chemical reactions which often impose complicated restrictions on control variables, such as temperature or time. Without taking those restrictions into account, the obtained optimal control sometimes can be useless as it would not be possible to implement such a control strategy in practice. In this work we propose a novel parallel memetic algorithm that allows obtaining feasible control strategies by monitoring the restrictions on control variables. The proposed algorithm and its software implementation were utilized to find feasible controls for several industrial chemical processes including the synthesis of the benzyl butyl ether, the hydroalumination of olefins with diisobutylaluminium hydride, and the catalytic reforming of gasoline. In addition, the obtained results were compared with the ones obtained by several other methods. The paper presents the results of conducted numerical experiments and the obtained controls for the specified chemical reactions.

The solution of the multiobjective optimization problem was performed with the help of the Pareto approximation algorithm. The problem of multiobjective optimization of the reaction process conditions for the olefin hydroalumination catalytic reaction, with the presence of organoaluminum compounds diisobutylaluminiumchloride, diisobutylaluminiumhydrate, and triisobutylaluminum, was solved. The optimality criteria are the yield of the reaction resultants. The largest yield of the high-order organoaluminum compound Bu2AlR was observed for the reactions with diisobutylaluminiumhydrate and triisobutylaluminum. Such results were obtained due to the fact that in the case of diisobutylaluminiumchloride, Bu2AlR was used for the formation of ClBuAlR. The yield of the Schwartz reagent Cp2ZrHCl was higher by a third in the reaction in the presence of diisobutylaluminiumchloride. Unlike the experimental isothermal conditions, the temperature optimal control showed the sufficiency of the gradual growth temperature for achieving the same or higher values of optimality criteria. For computational experiments, the algorithm for solving the multi-criteria optimization problem was parallelized using an island model.

In this paper, we investigate the problem of optimal control of complex multistage chemical reactions, which is considered a nonlinear global constrained optimization problem. This class of problems is computationally expensive due to the inclusion of multiple parameters and requires parallel computing systems and algorithms to obtain a solution within a reasonable time. However, the efficiency of parallel algorithms can differ depending on the architecture of the computing system. One available approach to deal with this is the development of specialized optimization algorithms that consider not only problem-specific features but also peculiarities of a computing system in which the algorithms are launched. In this work, we developed a novel parallel population algorithm based on the mind evolutionary computation method. This algorithm is designed for desktop girds and works in synchronous and asynchronous modes. The algorithm and its software implementation were used to solve the problem of the catalytic reforming of gasoline and to study the parallelization efficiency. Results of the numerical experiments are presented in this paper.