Problems Of Chemical Kinetics Research Articles

Fluidized-bed gasification kinetics model development using genetic algorithm for biomass, coal, municipal plastic waste, and their blends

This study aims to solve the inverse problem of chemical kinetics with the genetic algorithm (GA) as an optimization tool to develop robust gasification kinetic models that target a variety of feedstocks, such as southern pine biomass, lignite coal, municipal plastic wastes, and their blends. A dataset (syngas compositions, tar compositions, and char yield) obtained by conducting oxy-steam fluidized-bed gasification experiments of various feedstocks, including southern pine biomass, lignite coal, municipal plastic waste, and their twelve different blends have been utilized to develop and validate gasification kinetic model. The results generated by the developed GA-based gasification model surpassed the performance of the conventional model (developed using a trial and error approach), with the least average absolute errors of 0.9 % for H2, 1 % for CO, 0.65 % for CO2, and 3 % for CH4 in the syngas prediction. The study contributed by developing individual gasification kinetics models for various feedstocks, including biomass, coal, plastics, and their blends. Furthermore, the model was found to be suitable for a range of gasification operating conditions, such as steam-to-carbon ratios of 1–3, equivalence ratios of 0–0.2, and gasifier temperatures of 750–950 °C.

Extended Fourier Neural Operators to learn stiff chemical kinetics under unseen conditions

The solution of stiff chemical kinetics is recognized as the computational bottleneck for direct simulations of reacting flows. In this study, we extend the concept of Fourier Neural Operator (FNO) to learn stiff chemical kinetics. Specifically, element and mass conservation are introduced as the physical constraints in the extended FNO (EFNO). In addition, the training data are transformed by a Box–Cox strategy to rectify the skewed distribution of the species in the stiff problems. Finally, balanced loss functions are formulated to address the unbalanced sampling data points in complex reacting flow problems. The EFNO model is leveraged to forecast the temporal evolution of chemical species, utilizing an iterative approach wherein the prediction outcome from the previous time step is employed as a new input for subsequent time step prediction. The results in this work demonstrate the significant use of an EFNO approach to solving stiff chemical dynamics in reacting flow simulations, with a time step size comparable to the typical flow time step size. Its prediction accuracy and generalization ability are evaluated by comparing with the original FNO, Deep Nueral Network (DNN) and DeepONet models, in solving toy problems, zero-dimensional hydrogen autoignition, and a three-dimensional hydrogen/ammonia turbulent jet flame. The EFNO is shown to be highly accurate. More importantly, compared with other deep learning models, it can be generalized to stiff chemical kinetic states under unseen conditions, which the model has never trained for. The great performance of EFNO in terms of accuracy and generalization ability suggests that EFNO is a promising solution algorithm for stiff chemical kinetics problems in reacting flows.Novelty and Significance Statement: The novelty of this work lies in the newly developed extended Fourier neural operators (EFNO) to learn stiff chemical kinetics. Specifically, we for the first time evaluated and tested the performance of Fourier neural operators in solving stiff chemical kinetics. More importantly, we extended the original Fourier neural operators to accurately solve for stiff chemical kinetics problems under unseen conditions, which was a very challenging problem for deep learning methods in the literature. Our results demonstrated that the EFNO model solves chemical kinetics in both simple 0D autoignition and complex 3D turbulent jet flames with great accuracy and generalization ability, even for conditions which the training dataset has never encompassed. This work is significant because it developed a neural operator-based algorithm that can significantly accelerate the stiff chemical kinetic solution process in reacting flow simulations with great accuracy even for unseen initial conditions.

SOLUTION OF THE INVERSE PROBLEM OF CHEMICAL KINETICS BASED ON FUZZY LOGIC MODELS

The possibility of using “soft” calculation methods to solve the inverse problem of chemical kinetics to identify fuzzy kinetic laws based on the data of stationary experiments without a priori specifying the type of the kinetic law has been investigated. “Soft” computing combines artificial intelligence methods such as fuzzy logic, neural networks, genetic algorithms, etc. (F. Rosenblatt, L. Zadeh, J. Holland, etc.). Combinations of these methods will allow the creation of various hybrid systems. Dynamic kinetic models of chemical reactions are described by systems of ordinary differential equations (clear deterministic models), on the basis of which, using various optimization methods (least squares, etc.), the inverse problem of chemical kinetics is solved. B. Kosko proved that any mathematical system can be approximated as accurately as desired by a non-deterministic or weakly deterministic model based on fuzzy logic using simple algorithmic constructions of the “if ... then” type (conditional operators) without direct quantitative or qualitative analysis of systems of differential equations. A fuzzy kinetic model is an algorithmic design based on fuzzy algebra, which allows one to calculate the reaction rate for any values of reagent concentrations of interest to the experimenter. This model allows solving the inverse problem without a priori specifying the type of kinetic law, does not have an unambiguous formulaic representation and can be described by regression (clear) equations. In this article, based on fuzzy logic methods, regression kinetic models are obtained and studied to solve the inverse problem for a simple model reaction and the acetylene hydrochlorination reaction. It has been shown that clear regression models demonstrate good accuracy and can be used to establish alternative kinetic laws and mechanisms for chemical reactions. For citation: Kol'tsov N.I. Solution of the inverse problem of chemical kinetics based on fuzzy logic models. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2024. V. 67. N 12. P. 54-63. DOI: 10.6060/ivkkt.20246712.7067.

DEVELOPMENT OF THE THEORY OF GAS FUEL COMBUSTION TAKING INTO ACCOUNT MODERN KINETIC MECHANISMS OF COMBUSTION

An actuality of modernizing advancement the modern adopted combustion theory is due to a significant change in the composition of fuels at the market of developed countries by rejection of basic mineral (organic) fuels (fossil fuels), which have been used in the world for 250 years. The transformation of traditional fuels’ orientation towards an application of carbon-free fuels, primarily to hydrogen and its mixtures with natural gas. Each way of mentioned activity is connected with to ensure an environmental decarbonisation. Criteria for evaluating the CO2 emissions by fuels’ choice and selection have been proposed while numerical calculations of the specific CO2 content in the combustion products of the hydrocarbon-hydrogen mixture jʹ and j have been evaluated. It is possible to reduce these values when adding the shared H2 in the fuels (m3 CO2/ MJ; kg CO2/ MJ). Considering the combustion process in combustion systems (combustion chambers, boilers, furnaces) at constant pressure (p =const, dp =0 — isobaric process), the enthalpy of the reactants’ flow of the mixture (total (chemical) enthalpy I) can serve as a measure of the specific energy of the reacting mixture. An approach is proposed, according to which the main equations of heat and mass transfer in the torch (premixed flame) are composed basing upon the principle of conservation the total (chemical) enthalpy representing energy as basic function. The basic approach to the development of a modern combustion theory is focused on the concept of T. von Karman, according to which the combustion process is considered as “aerothermochemistry”, i.e. the description of physical and chemical processes is based on the combination of heat and mass transfer equations with chemical kinetics’ equations. At the current period, the problem of chemical kinetics (burning process) is considered through the combustion mechanisms of basic fuels, which consist of a large number of equations for molecules and particles involved in process at various stages of the combustion — from the preheating of the reaction mixture and through the stage of ignition (or self-ignition) — to the final temperature of flue gases. The laminar combustion velocity SL is a determinative characteristic of the combustible properties of gas fuels and, accordingly, of the safety restrictions by the fuel choice. Quantitative values of SL for the separate basic fuels, including the hydrogen, both the organic (fossil fuels) and alternative ones could be differed by an order of magnitude and more. The problem of estimable forecasting the SL value provides an option of the composition of the fuel-oxidant mixture in the industry, power and municipal energetics. The SL value’s prediction makes an especially important action at the current stage of modelling completion with an account of the global warming and by preventing an excess of CO2 emissions, including involvement of hydrogen-containing gases by substitution the carbon-friendly fuels, firstly the fossil fuels. The contemporary theory of combustion has been developed by extensive use the adequate kinetic mechanism GRI-Mech 3.0, the universality of which has been confirmed by our calculations of SL values for a wide range of the gas fuels — from methane CH4 to hydrogen H2 and their mixtures. An advanced modern theory of combustion using the adequate and approved GRI-Mech 3.0 combustion kinetic mechanism has been developed. The universality of this mechanism is confirmed by our calculations of SL values for a wide range of gaseous fuels — from methane CH4 to hydrogen H2 and their mixtures (mixed gases MG). For further analysis and development, the model of combustion in the reacting flow by Y.B. Zeldovich and D.A. Frank-Kamenetsky was used, considering the transfer processes of total enthalpy I as the energy characteristic of the combustible mixture. The kinetic component (on the example of natural gas) is taken into account using the combustion mechanism for GRI-Mech 3.0. The latter summarizes the combustion process of through 325 constituent reactions for 53 components. The adequacy of the combustion model proposed in the paper is confirmed by numerical examples of the coincidence of numerical SL values: ours — calculated; literary — experimental. The validity of experimental values of SL is provided by the use of different methods of measuring the SL. Calculations of the specific total enthalpy I of the reacting mixture along the length x º l (thickness of the front for a homogeneous flame in a one-dimensional formulation) have been performed as a result of researches confirmed the initial position: I(x) = const with deviations for CH4 +7 % / –2 %; for H2 +9,2 % / –2,8 % in relation to the heat of combustion. Bibl. 52, Fig. 9, Tab. 3.

Two Numerical Approaches for Solving Fractional Model of Chemical Kinetics Problem via Chebyshev Polynomials

Two Numerical Approaches for Solving Fractional Model of Chemical Kinetics Problem via Chebyshev Polynomials

CONSERVATION LAWS OF CHEMICAL REACTIONS IN A CLOSED NONISOTHRUM INCOMPLETE MIXING REACTOR

In chemical reactions occurring stationary or non-stationary in closed isothermal ideal mixing reactors (CSTR), the classical linear stoichiometric laws of conservation are always satisfied, reflecting the constancy of atoms of one type or another during the entire reaction. In real conditions, taking into account possible deviations from ideal mixing conditions or violations of the constancy of temperature conditions, various macroscopic factors (mass transfer, heat transfer) can have a noticeable influence on the course of the reaction. To study the kinetics of such processes, a more general mathematical model of an incomplete mixing reactor (IMR) is used. It is of interest to study the influence of diffusion and convection on the patterns of stationary modes of non-isothermal chemical reactions associated with the existence of more general (non-stoichiometric) conservation laws (kinetic invariants). It is more difficult to detect such invariants (especially temperature ones) in IMRs than in CSTR, and at present they are practically not studied. In this work, for chemical reactions occurring in a closed non-isothermal IMR according to the kinetic law of mass action, linear concentration and temperature conservation laws are established, which take into account the complicating processes of diffusion and convection. These conservation laws relate the stoichiometry of the reaction mechanism to the concentrations of reagents, the temperature of the reaction medium and the intensity of macrokinetic processes, which allows them to be used for a more reasonable identification of linear and nonlinear mechanisms of chemical reactions when solving the inverse problem of chemical kinetics. The new kinetic conservation laws presented in the work can be discovered a priori and verified experimentally. For citation: Kol'tsov N.I. Conservation laws of chemical reactions in a closed nonisothrum incomplete mixing reactor. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2024. V. 67. N 7. P. 111-118. DOI: 10.6060/ivkkt.20246707.6976.

Simulation of exchange processes in multi-component environments with account of data uncertainty

The goal of the work. Proposals for methods of solving systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients that defined in interval form and intended for modeling exchange processes in multicomponent environments. Research subject: systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients defined in interval form. Research method: interval analysis. The obtained results. Systems of linear homogeneous and non-homogeneous differential equations, which are used in modeling exchange processes in multicomponent environments, are considered. Such systems can be considered, for example, in problems of chemical kinetics, materials science, and the theory of Markov processes. To obtain the solution of these equations, specialized calculators of analytical transformations were used and tested. The Matlab system (ode15s solver) was used for numerical analysis of systems of differential equations. It is shown that the application of interval methods of numerical analysis at the initial stage of system modeling has some advantages over probabilistic methods because they do not require knowledge of the laws of distribution of the results of the system state parameter measurements and their errors. It is shown that existing methods of solving systems of linear differential equations can be divided into two groups. Common to these groups is the use of interval expansion of classical methods for solving differential equations given in interval form. The difference between these two groups of methods is as follows. The methods of the first group can be used for all types of differential equations but require the creation of special software. The peculiarity of the methods of the second group is that they can be used to solve equations analytically or using numerical analysis packages. The application of the methods of the second group is shown on the example of solving a system of differential equations, the coefficients of which are determined in interval form. The system of these equations is intended for modeling the processes of exchange with the external environment of the elements of the model of a specific physical system. In the case when the coefficients of these equations are variables, their piecewise-constant approximation is applied and a criterion that determines the possibility of its application is given. The technique proposed in the paper can be applied to solve systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients if they are given by slowly varying functions. In the case when the coefficients of the equations are determined in the interval form, the technique allows obtaining their solution also in the interval form and does not require the creation of special software.

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview.

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

Advanced Fitting Method for the Kinetic Analysis of Thermogravimetric Data.

The article considered the solution of the inverse problem of chemical kinetics of the analysis of experimental data of a thermogravimetric experiment at a constant sample heating rate. The fitting method for identifying the parameters of a kinetic triplet using the integral method for a model of a solid-state reaction based on the modified Arrhenius equation is described. The effectiveness of the proposed approach was confirmed by solving test cases for low, medium, and high rates of material conversion. Unlike other methods, setting the parameters of the reaction mechanism is not required, as they are determined by the solution. Solutions for real data of TGA studies with high and low sample heating rates were compared with the results obtained by other authors and experimental data. A description of the full cycle of calculations used to identify kinetic parameters from thermogravimetric experimental data is given, from the derivation of calculated relationships to the implementation of a short (three to five formulas) program code for MS Excel spreadsheets. The presented code is easy to verify and reproduce and can be modified to solve various problems.

A new one-step method with three intermediate points in a variable step-size mode for stiff differential systems

This work introduces a new one-step method with three intermediate points for solving stiff differential systems. These types of problems appear in different disciplines and, in particular, in problems derived from chemical reactions. In fact, the term “stiff”’ was coined by Curtiss and Hirschfelder in an article on problems of chemical kinetics (Hirschfelder, Proc Natl Acad Sci USA 38:235–243, 1952). The techniques of interpolation and collocation are used in the construction of the scheme. We consider a suitable polynomial to approximate the theoretical solution of the problem under consideration. The basic properties of the new scheme are analyzed. An embedded strategy is adopted to formulate the proposed scheme in a variable stepsize mode to get better performance. Finally, some models of initial-value problems, including ordinary and time-dependent partial differential equations, are solved numerically to assess the performance and efficiency of the proposed technique, with applications to real-world problems.

Multiscale Physics-Informed Neural Networks for Stiff Chemical Kinetics.

In this paper, a multiscale physics-informed neural network (MPINN) approach is proposed based on the regular physics-informed neural network (PINN) for solving stiff chemical kinetic problems with governing equations of stiff ordinary differential equations (ODEs). In MPINNs, chemical species with different time scales are grouped and trained by multiple corresponding neural networks with the same structure. The adaptive weight based on a key performance indicator is assigned to each loss term when calculating the summation of loss residues. With this structure, MPINNs provide a framework to solve challenging stiff chemical kinetic problems without any stiffness-removal artifacts before training. In addition, by introducing a small number of ground truth data (GTD) points (less than 10% of the number required for residual loss calculation) and adding data loss terms into loss functions, MPINNs show superior ability to represent stiff ODE solutions at any desired time. The accuracy of MPINNs is tested with classical chemical kinetic problems, by comparing with the regular PINN and other state-of-the-art methods with special consideration for solving stiff chemical kinetic problems with PINNs. The validation results show that MPINNs can effectively avoid the influence of stiffness on neural network optimization. Compared with the traditional deep neural network only trained by GTD, MPINNs can use no data or a relatively small amount of data to achieve high-precision prediction of stiff chemical ODEs. The proposed approach is very promising for solving stiff chemical kinetics, opening up possibilities of MPINN application in different fields involving stiff chemical dynamics.

Исследование гидродинамического влияния на молекулярные параметры продукта полимеризации изопрена в присутствии неодимсодержащего каталитического комплекса

The results of studying kinetic regularities in the process of obtaining polyisoprene in the presence of neodymium-containing catalytic systems for various technological modes of industrial production are presented. The process kinetic scheme is provided under conditions of the used catalyst monocenter, according to which a mathematical model of the process of isoprene polymerization for the process periodic and continuous modes is compiled. Kinetic approach to solving problems of chemical kinetics in combination with the method of moments was applied in the process mathematical description. To evaluate hydrodynamic influence exerted by the process implementation in a reactor cascade, the constructed kinetic model was supplemented with a macro-kinetic module that takes into account the relevant patterns. Features of the starting and static modes calculation in continuous production were determined. Numerical calculation methods were introduced to obtain dependences of alterations in the molecular parameters of the resulting product for a different number of reactors used in the continuous production system under static conditions. The hydrodynamic mode influence was shown in the reaction zone on the molecular weight distribution of the resulting product. As a result of computational experiments aimed at increasing the length of the reactor cascade, increase in the average molecular weights and approximation of the obtained values to the those characterizing the process periodic mode were noted

Chemistry students’ conceptual difficulties and problem solving behavior in chemical kinetics, as a component of an introductory physical chemistry course

Abstract The identification of undergraduate chemistry students’ conceptual difficulties and common mistakes with basic concepts and problems in chemical kinetics provided the aim for this study, which involved 2nd-year/4th semester students who had passed the chemical kinetics component of a physical chemistry course. The study involved the analysis, evaluation and interpretation of students’ answers to the final examination in chemical kinetics. Three achievement groups, for the various topics, were identified: Group A, high achievement (mean ≈ 85%): (a) the steps in a chain-reaction mechanism, (b) integrated 1st- and 2nd-order rate laws; and (c) the Lindemann–Hinshelwood mechanism. Group B, intermediate achievement (mean ≈ 74%): (a) half-life, (b) instantaneous rate and the extent of reaction variable (ξ), (c) the Michaelis–Menten mechanism, and (d) theoretical rate law not asking for a final formula. Group C, low achievement (mean ≈ 54%): (a) experimental rate law and the reaction rate constant on the basis of an experimental-data table, (b) extracting the theoretical rate law, and (c) the Arrhenius equation. Students’ errors and misconceptions have also been identified. Successful students tended to respond well to straightforward questions on the theory of the subject, but had difficulties when solving problems. It is essential that teachers understand the potential of their students, especially possible misconceptions they may hold, and the teaching approaches that may contribute to overcoming the student difficulties. Problems in chemical kinetics can be very demanding both in terms of algebraic manipulations and conceptually. Teaching should focus on problem solving, with the emphasis on students themselves trying to solve the problems.

Physics-informed neural networks and functional interpolation for stiff chemical kinetics

This work presents a recently developed approach based on physics-informed neural networks (PINNs) for the solution of initial value problems (IVPs), focusing on stiff chemical kinetic problems with governing equations of stiff ordinary differential equations (ODEs). The framework developed by the authors combines PINNs with the theory of functional connections and extreme learning machines in the so-called extreme theory of functional connections (X-TFC). While regular PINN methodologies appear to fail in solving stiff systems of ODEs easily, we show how our method, with a single-layer neural network (NN) is efficient and robust to solve such challenging problems without using artifacts to reduce the stiffness of problems. The accuracy of X-TFC is tested against several state-of-the-art methods, showing its performance both in terms of computational time and accuracy. A rigorous upper bound on the generalization error of X-TFC frameworks in learning the solutions of IVPs for ODEs is provided here for the first time. A significant advantage of this framework is its flexibility to adapt to various problems with minimal changes in coding. Also, once the NN is trained, it gives us an analytical representation of the solution at any desired instant in time outside the initial discretization. Learning stiff ODEs opens up possibilities of using X-TFC in applications with large time ranges, such as chemical dynamics in energy conversion, nuclear dynamics systems, life sciences, and environmental engineering.

Solving inverse problems of chemical kinetics by metaheuristic methods

Solving inverse problems of chemical kinetics by metaheuristic methods

New Solitary Wave Solutions of the Fisher Equation

In this paper, we use Riccati equation to find new solitary wave solutions of Fisher equation, which describes the process of interaction between diffusion and reaction. It is of great importance to comprehend the equation to solve the problems in chemical kinetics and population dynamics. We resolve the Ricatti equation through diverse function transformation and many types of exact solutions are obtained. Then it is used as an auxiliary equation to solve Fisher equation. In the process, we select different coefficients in the Racatti equation, as a result, abundant solitary wave solutions are obtained, some of which haven’t been found in other documents yet. Moreover, these solutions we got in this paper will be favorable for understanding the Fisher equation.

INFLUENCE OF ADDITIONAL SLOW STAGES ON KINETIC REGULARITIES OF CHEMICAL REACTIONS

It is known that mechanisms with a different set of elementary stages are alternatively used to describethe kinetic patterns of chemical reactions involving the same reagents. In this regard, the article introduces two concepts–the "initial" reaction mechanism, which is obtained as a result N.I. Kol’tsovChemChemTech. 2022. V. 65. N 833of experimental studies, and alternative reaction mechanisms thatcan demonstrate dynamic be-havior close to the original mechanism. Alternative mechanisms include all stages of the original mechanism and additional slow stages satisfying certain stoichiometric constraints. The influence of additional dependent and independent slow stages on the patterns of chemical reactions in closed and open reactors of ideal mixing is investigated. It is established that the introduction of slow linearly dependent stages into the reaction mechanism does not significantly affect its kinetic pat-terns. The addition of the reaction mechanism by slow linearly independent stages leads to a change in its dynamics, which does not allow them to be used as part of alternative mechanisms. The article describes an approach that allows us to detectdependent stages, check the possibility of their im-plementation by comparing stationary and non-stationary characteristics (coordinates of stationary states, linear and nonlinear relaxation times, etc.), analyzing the properties of the matrix of stoi-chiometric coefficients (the number of rows and columns) and its rank (the maximum number of independent rows or columns). In this case, the main role is played by stoichiometric conservation laws (stationary and non-stationary), taking into account the peculiarities of their implementation in closed, quasi-open and fully open reactors of ideal mixing. Examples are given demonstrating the influence of additional dependent and independent slow stages on the dynamics of reactions. The results obtained can be useful in practice when solving inverse problems of chemical kinetics to clarify the mechanisms of specific complex reactions.

Academician G. K. Boreskov: Catalysis as Destiny

The article is a biographical outline of the life and achivements of an outstanding chemist Georgy Konstantinovich Boreskov (1907–1984). G. K. Boreskov entered the history of domestic and world science as an outstanding scientist, engineer and teacher. The bright life of the scientist-chemist Boreskov was devoted to the study of catalysis and the closely related problems of chemical kinetics and chemical technology. In his work, fundamental research on catalysis as an interesting chemical phenomenon were harmoniously combined with the successful solution of the most important problems of the chemical industry. With the development of a new catalyst for one of the main industrial processes – the production of sulfuric acid – that G. K. Boreskov began his career in science. Thanks to the introduction of technologies based on his developments, a real breakthrough occurred in some areas of industrial production related to catalysis. In Novosibirsk, Academician Boreskov played the main role in the creation of the country’s first Institute of Catalysis of the Siberian Branch of the Academy of Sciences of the USSR, which now bears his name. No less important is the merit of Academician Boreskov as the founder of the scientific school in the field of heterogeneous catalysis.

Study of stiff differential equations of mathematical description of izomerization of pentane-hexane cut process

This paper provides a brief overview of the existing definitions of a stiff system of differential equations. Further, on the example of the accepted scheme of chemical transformations of the catalytic isomerization process of the pentane-hexane fraction, the stiffness of the system of differential equations was studied. In the course of the work, a method for studying the direct kinetic problem for stiffness is presented. In the Matlab software, the results of solving a system of differential equations by five methods (solvers) were compared. The given method can be tried for solving other problems of chemical kinetics.

Application of the genetic algorithm method in the problem of chemical kinetics of the oxidative regeneration process

Annotation. One of the main problems in chemical kinetics is the establishment of the mechanisms of complex chemical reactions. The inverse problem of chemical kinetics is understood as the determination of the dependence of the concentration of the participating components on the basis of experimental data obtained from a laboratory installation for the oxidative regeneration of coked catalysts. One of the main methods used in inverse problems the genetic algorithm. The algorithms considered in the article make it possible to determine the values of the rate constants of the considered chemical stages.