Оптимальный выбор начальных условий задачи Коши и его применение для расчета начального состава реакционной смеси

A new global optimization method combining geneticalgorithm and Hooke-Jeeves method to solve a class of constrainedoptimization problems is studied in this paper. We first introducethe quadratic penalty function method and the exact penalty functionmethod to transform the original constrained optimization problemwith general equality and inequality constraints into a sequenceof optimization problems only with box constraints. Then, thecombination of genetic algorithm and Hooke-Jeeves method isapplied to solve the transformed optimization problems. SinceHooke-Jeeves method is good at local search, our proposed methoddramatically improves the accuracy and convergence rate of geneticalgorithm. In view of the derivative-free of Hooke-Jeeves method,our method only requires information of objective function valuewhich not only can overcome the computational difficulties causedby the ill-condition of the square penalty function, but also canhandle the non-differentiability by the exact penalty function.Some well-known test problems are investigated. The numericalresults show that our proposed method is efficient and robust.

The problem of determination of optimal initial concentrations of catalytic reaction reagents is considered. The mathematical description of the reaction is a system of ordinary differential equations. Restrictions are imposed on the values of concentrations of substances at the initial moment of time. To find a solution to the problem, a genetic algorithm with real coding has been developed, taking into account the physical and chemical meaning of the problem. The algorithm is modified at the stage of initial population filling, crossover and mutation. The developed algorithm takes into account the constraints set on the values of initial concentrations of reagents. The work of the algorithm is tested on the example of the catalytic reaction of benzyl butyl ether synthesis. As a result of numerical experiments the initial concentrations of reagents, at which the highest values of concentrations of target products of the reaction -- benzylbutyl ether and dibenzyl ether -- are provided, have been calculated. The values of concentrations of target products of reaction at other initial compositions of reaction mixture have been calculated. It is shown that the set of initial concentrations of reagents calculated with the help of the algorithm provides the highest values of concentrations of target reaction products.

A numerical algorithm has been developed to search for the optimal values of the concentrations of chemical reaction reagents. The statement of the problem of determining the optimal initial concentrations of the initial substances on the basis of the kinetic model of the reaction, which is a system of ordinary differential equations, is formulated. To solve the problem, a genetic algorithm with real coding is given. The algorithm is modified taking into account the restriction on the values of the initial concentrations and the condition imposed on the total initial concentration of the reagents. A computational experiment was carried out for the reaction of the synthesis of benzyl butyl ether under the action of copper-containing catalysts. The initial concentrations of the initial substances are determined at which the highest values of the concentrations of the target reaction products are achieved.

In this paper, we have reviewed some penalty function methods for solving constrained optimization problems in the literature and proposed a continuously differentiable logarithmic penalty function which consists of the proposed logarithmic penalty function and modified Courant-Beltrami penalty function for equality and inequality constraints, respectively. Furthermore, we hybridized the two and came up with the general form of both (equality and inequality) constraints. However, in the first part, the equivalence between the sets of optimal solutions in the original optimization problem and its associated penalized logarithmic optimization problem constituted by invex functions with equality and inequality constraints has been established. In the second part, we have validated the general form of the logarithmic penalty function and compared the results with absolute value penalty function results by solving nine small problems from Hock-Schittkowski collections of test problems with different classifications. The experiments were carried out via quasi-newton algorithm using a fminunc routine function in matlab2018a. The general form yields a better objective value compared to absolute value penalty function.

We address an optimal portfolio selection problem of maximizing so we call CVaR (Conditional Value-at-Risk) based Sharpe ratio of return rate of portfolio, which is defined as the ratio of the expected excess return to CVaR. The Sharpe ratio defined as the ratio of expected excess return to standard deviation, the most common traditional performance measure, takes standard deviation as a risk measure, however, its has been received a lot of criticisms. In our CVaR based Sharpe ratio, the standard deviation is replaced by CVaR, which is a remarkable coherent risk measure which overcomes essential defects of standard deviation. Although our new performance measure is expected to enlarge the applicable area of practical investment problems for which the original Sharpe ratio is not suitable, however, we should device effective computational methods to solve optimal portfolio selection problems with very large number of investment opportunities. In order to deal with rather complicated non-concave objective function, which comes from the introduction of CVaR, in this paper, we propose a Genetic Algorithm (GA) approach. The paper briefly reviews the literature in the area of application of Genetic Algorithms to financial problems, and then details the development of Genetic Algorithm for portfolio selection. In order to evaluate CVaR for each portfolio, by utilizing the results of Rockafellar and Uryasev (2000), we introduce an auxiliary decision variable to obtain a tractable concave maximization problem. Furthermore, if we estimate or approximate required expected values by sampling methods or historical data, we can reduce this concave maximization problem to an LP (Linear Programming) problem. Therefore, our problem could be solved by GA which incorporates LP for evaluating values of CVaR. Numerical experiments from real Japanese financial data are conducted to test our approach to conclude that, by suitable choice of probability parameter of three evolution operation, we could effectively solve optimal selection problems with practical sizes.

The work is devoted to the development and verification of a kinetic model for the synthesis of isophorone (CAS 78-59-1) from acetone in the presence of a homogeneous catalyst and alkali. The proposed model is aimed at accurately predicting changes in the concentrations of reagents and products during the reaction process, which is important for optimizing production. The model allows us to calculate the behavior of the reaction system in flow-through and ring-type chemical reactors, which makes it a universal tool for various types of industrial installations. Experimental data were used to create the model, which made it possible to minimize deviations between calculated and actual concentrations of reagents and products. The type and parameters of the model were obtained by minimizing deviations of experimental and calculated values of concentrations of reagents and products, while the model showed its adequacy to the experiment. The limits of applicability of the model were checked through computational experiments at various acetone-alkali ratios and temperature conditions. The results obtained demonstrated the high accuracy of the model's predictions and its adequacy in real conditions. The application of the model has shown that it is able to predict the behavior of the system under various conditions, which is especially important for practical chemical engineering. This makes it possible to reduce the number of experiments at the reactor design stage and reduce the cost of producing isoforone. The model also demonstrated good agreement with experimental data at various temperature conditions, which confirms its reliability and applicability in a wide range of conditions. The kinetic model of isophorone synthesis is an important tool for predicting and optimizing technological processes in industry, providing precise control of reaction parameters to produce isophorone on a large scale with minimal deviations. For citation: Shishanov M.V., Kuk C.G., Gevorkyan E.L. Kinetic modeling of the isophorone synthesis reaction. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2025. V. 68. N 6. P. 134-139. DOI: 10.6060/ivkkt.20256806.7172.

Auto-CASH: A meta-learning embedding approach for autonomous classification algorithm selection

The technique of calibration in survey sampling is a widely used technique in the field of official statistics. The main element of the calibration process is an optimization procedure, for which a variety of penalty functions can be used. In this chapter, we consider three of the classical penalty functions that are implemented in many of the standard calibration software packages. We present two algorithms used by many of these software packages, and explore the properties of the calibrated weights and the corresponding estimates when using these two algorithms with the classical calibration penalty functions.

In recent years, evolutionary algorithms have shown great advantages in the field of feature selection because of their simplicity and potential global search capability. However, most of the existing feature selection algorithms based on evolutionary computation are wrapper methods, which are computationally expensive, especially for high-dimensional biomedical data. To significantly reduce the computational cost, it is essential to study an effective evaluation method. In this paper, a two-stage improved gray wolf optimization (IGWO) algorithm for feature selection on high-dimensional data is proposed. In the first stage, a multilayer perceptron (MLP) network with group lasso regularization terms is first trained to construct an integer optimization problem using the proposed algorithm for pre-selection of features and optimization of the hidden layer structure. The dataset is compressed using the feature subset obtained in the first stage. In the second stage, a multilayer perceptron network with group lasso regularization terms is retrained using the compressed dataset, and the proposed algorithm is employed to construct the discrete optimization problem for feature selection. Meanwhile, a rapid evaluation strategy is constructed to mitigate the evaluation cost and improve the evaluation efficiency in the feature selection process. The effectiveness of the algorithm was analyzed on ten gene expression datasets. The experimental results show that the proposed algorithm not only removes almost more than 95.7% of the features in all datasets, but also has better classification accuracy on the test set. In addition, the advantages of the proposed algorithm in terms of time consumption, classification accuracy and feature subset size become more and more prominent as the dimensionality of the feature selection problem increases. This indicates that the proposed algorithm is particularly suitable for solving high-dimensional feature selection problems.

To evaluate the rationality and separation conditions of alkyl esters of phosphorous P(III) and P(V) acids using reversed phase HPLC with mass spectrometric detection, the analyses of reaction mixtures of two 1-alkanols (1-butanol and 1-heptanol) with PCl3 in the presence of N,N-dimethyl aniline (for pH correction) have been carried out. In accordance with the previous results, these mixtures preferably contain trialkyl phosphites (RO)3P and dialkyl phosphonates (RO)2PH=O. However, the results of HPLC-MS analyses indicate the prevalence of oxidation products P(III) ® P(V) in the composition of these mixtures, namely trialkyl phosphates (RO)3OP. In accordance with this scheme, the appearance of diheptyl phosphate (C7H15O)2P(=O)-OH is caused by the oxidation of diheptyl phosphonate. Possible reasons of such discrepancies in the composition of reaction mixtures are discussed. One of them may be oxidation of P(III) derivatives by air oxygen during storage, or, tentatively, due to electrochemical reactions during electrospray ionization. The reaction mixture of 1-heptanol with PCl3 contains two components with the same molecular weights ( M = 294) and identical mass spectra, but strongly different retention parameters ( t R 5.4 and 16.0 min). The second of them is diheptyl phosphonate. To explain the appearance the first of them, existence of more hydrophilic prototropic tautomer (C7H15O)P-OH (contains hydroxyl group in the molecule) was proposed. Comparing the analytical results obtained in positive and negative modes of detection indicates that the first of them seems to be more effective for detection of alkyl esters of P(III) and P(V) phosphorous acids.

Genetic algorithm for robot selection and work station assignment problem

This article describes an algorithm for the automatic selection of hydropneumatic capacitive vessels used for household and industrial purposes. The suggested informational system makes it possible to reduce the working time spent on the selection of equipment and increase the relevance of the analyzed information. The article discusses a detailed description of the design of pneumohydroaccumulators, as well as provides examples of calculations of their main characteristics. The main task we needed to solve was a multi-criteria choice, and the article provides an overview of methods for solving this problem. In addition to these methods, we analyzed information systems that allow one to select suitable equipment among various alternatives. Based on the analysis of methods and similar software solutions, the authors suggested an information system and an algorithm for automatic selection of hydraulic accumulators by searching for options that meet the parameters specified by the system user. Also, the article discusses the process of purchasing capacitive equipment. There was demonstrated a logical model of the database structure storing the technical characteristics of the equipment which reflects objects of the information system and their connections. In this article we presented and argued the software for development of a specified information system. The development of the information system was based on the "1С platform: The enterprise". The task of selecting hydropneumatic capacitive vessels was solved by a request that can be interacted with through a report in the information system. This report allows the user to search for equipment by quantitative and qualitative characteristics: volume, operating pressure, vessel material, flange material, installation type (vertical or horizontal), availability of legs, cost, manufacturer or supplier. The report options allow users to search for equipment both in accordance with the specified parameters and with a deviation from them but only if the parameter is quantitative. The results of the report are ordered by relevance to the criteria specified by the user. The operation of the report query aggregating data was reflected in the flowchart. The article presents a situation where the user solves the selection problem by using capabilities of the report. The problem of multi-criteria selection was formulated in a way to demonstrate how a clear parameter of the report becomes fuzzy, and our solution of it allows you to find more alternative options with sorting by relevance helping you to identify the most suitable pneumohydroaccumulator. This software package was registered in the state registry of computer programs.

The presented article is devoted to thermodynamic calculations of the N-methyl-D-glucosimine reversible formation reaction, an intermediate product for N-methyl-D-glucosamine synthesis, which is widely used in pharmaceutical practice as a ballast or counterion that improves the bioavailability of the main active substance. N-methyl-D-glucosimine is synthesized as a result of the interaction of D-glucose with methylamine in organic solvents, the reaction is reversible, and the yield of the target product depends entirely on the reaction conditions. The use of thermodynamic calculations makes it possible to evaluate the influence of the chemical process conditions on the yield of target products, which in turn contributes to a deeper understanding of the chemical reactions mechanisms. In chemical equilibrium, direct and reverse reactions proceed at equal rates, while the concentrations of products and reagents remain constant. When the reaction proceeds in a closed system, after a certain time, a state of equilibrium occurs, while the reaction does not proceed with a complete transformation of the reagents. This article presents the results of thermodynamic calculations of the reaction for the synthesis of N-methyl-D-glucosimine by the Van Kravlen – Cheremnov method. The Gibbs energy, equilibrium constants, and D-glucose conversion were calculated as activity function of reacting substances. It was shown that an increase in the temperature of the reaction mixture from 20 to 160 °C promotes an increase in the conversion of D-glucose from 3 to 32%, and therefore it is possible to recommend carrying out this reaction at elevated temperatures.

Bayesian methods have formed the core of the development of reconstruction algorithms for emission tomography. In particular, there has been considerable interest in edge-preserving prior models, which are associated with smoothing penalty functions that are nonquadratic functions of nearby pixel differences. In spite of several advantages of nonconvex prior models, their use in routine applications has been hindered by several factors, such as the computational expense due to the nonconvexity of penalty functions and the difficulty in the selection of hyperparameters. We note here that, by choosing a penalty function which is nonquadratic but is still convex, both the problem of nonconvexity involved in some nonquadratic priors and the edge-oversmoothing problem of conventional quadratic priors may be avoided. In this paper, we use a class of two-dimensional smoothing splines with low (first) and high (second) spatial derivatives applied to convex-nonquadratic (CNQ) penalty functions. To compare quantitative performance of our new priors, we use the quantitation of bias/variance and total squared error over noise trials. Our numerical results show that a linear combination of first- and second-order spatial derivatives applied to CNQ penalty functions improves reconstructions in terms of total squared error.

The plasma concentrations of two cardiovascular risk factors, total homocysteine (tHcy) and asymmetric dimethylarginine (ADMA), correlate with decreased levels of endothelium-derived nitric oxide and subsequent endothelial dysfunction. Homocysteine has been proposed to inhibit the catabolic enzyme of ADMA, dimethylarginine dimethylaminohydrolase (DDAH), but the mechanism of this inhibition has not been fully elucidated. Here, the human DDAH isoform-1 (DDAH-1) is heterologously expressed and purified. Cys(274) and His(173) are identified as active site residues and the pH rate dependence is described. Because oxidation of the active site Cys has been suggested as an inhibitory mechanism in patients with hyperhomocysteinemia, the sensitivity of DDAH-1 to inhibition by L-homocysteine, H(2)O(2), and S-nitroso-L-homocysteine is quantified. DDAH-1 is surprisingly insensitive to inactivation by the powerful oxidant, H(2)O(2) (0.088 M(-1) s(-1)), possibly because of a substrate-assisted mechanism that allows the active site cysteine to remain predominantly protonated and less reactive in the resting enzyme. In contrast, DDAH-1 is sensitive to inactivation by S-nitroso-L-homocysteine (3.79 M(-1) s(-1)). This work illustrates how a particular catalytic mechanism can result in selective redox regulation and has possible implications for hyperhomocysteinemia.