arXiv subjects

Ruslan Zakirzyanov

Publications and source records attributed to Ruslan Zakirzyanov.

5 recordsLinked to original sources

arXiv2026

Improving a Hybrid Algorithm for APCS Hierarchical Architecture Design via Hyperparameter Optimization

Automated process control systems, widely used in industry, have a hierarchical multi-level tree-based architecture and are built using specialized hardware and software. Constructing such a control system from off-the-shelf industrial components with predetermined characteristics is a complex combinatorial optimization problem with multiple constraints. A hybrid method for constructing a tree-based structure is considered, combining a deterministic construction algorithm and an ant colony metaheuristic for device selection. The effectiveness of the metaheuristic algorithm depends on its hyperparameters, which are proposed to be optimized. This paper presents a general problem formulation, defines optimization criteria, describes the solution method, and provides several numerical examples.

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arXiv2026

Hybrid Metaheuristic Optimization of Distributed Control System Hardware Architecture with Model-Based Verification

Large-scale chemical plants rely on distributed process control systems (PCS) comprising numerous processing units, communication modules, and I/O devices interconnected via industrial networks. The design of a cost-efficient and reliable hardware architecture under partial uncertainty in plant parameters remains a challenging combinatorial optimization problem. This paper proposes a formal model for distributed control system hardware architecture synthesis. A hybrid ant colony-based metaheuristic framework is developed to construct feasible hierarchical architectures. The proposed approach is validated on a large-scale sulfuric acid plant control system case study. Plant parameters are identified from operational data, system stability is analyzed, and a controller synthesis is performed based on the optimized architecture. The results demonstrate the feasibility of the approach and confirm that the obtained architecture satisfies structural and dynamic performance requirements.

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arXiv2026

Bio-inspired metaheuristic optimization for hierarchical architecture design of industrial control systems

Automated process control systems (APCS) are widely used in modern industrial enterprises. They address three key objectives: ensuring the required quality of manufactured products, ensuring process safety for people and the environment, and reducing capital and operating costs. At large industrial enterprises, APCSs are typically geographically distributed and characterized by a large number of monitored parameters. Such systems often consist of several subsystems built using various technical means and serving different functional purposes. APCSs usually have a hierarchical structure consisting of several levels, where each level hosts commercially available technical devices with predetermined characteristics. This article examines the engineering problem of selecting an optimal software and hardware structure for a distributed process control system applied to a continuous process in the chemical industry. A formal formulation of the optimization problem is presented, in which the hierarchical structure of the system is represented as an acyclic graph. Optimization criteria and constraints are defined. A solution method based on a metaheuristic ant colony optimization algorithm, widely used for this class of problems, is proposed. A brief overview of the developed software tool used to solve a number of numerical examples is provided. The experimental results are discussed, along with parameter selection and possible algorithm modifications aimed at improving solution quality. Information on the verification of the control system implemented using the selected software and hardware structure is presented, and directions for further research are outlined.

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arXiv2026

Metaheuristic algorithm parameters selection for building an optimal hierarchical structure of a control system: a case study

Metaheuristic algorithms are currently widely used to solve a variety of optimization problems across various industries. This article discusses the application of a metaheuristic algorithm to optimize the hierarchical architecture of an industrial distributed control system. The success of the algorithm depends largely on the choice of starting conditions and algorithm parameters. We examine the impact of parameter selection on the convergence of a modified ant colony algorithm and provide recommendations for tuning the algorithm to achieve optimal results for a specific industrial problem. The findings presented in this article can also be applied to other combinatorial optimization problems.

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arXiv2026

A method for optimizing the structure of the software and hardware complex of a distributed process control system for large industrial enterprises

The article proposes a method for optimizing the structure of the software and hardware complex of an automated control system for continuous technological processes for large industrial enterprises. General information is given on the relevance of the problem of choosing the structure of a system built on the basis of serially produced components, a formal description of the optimization problem is given, the criterion and limitations are highlighted. A solution method using the metaheuristic algorithm of ant colonies is described. A numerical example of the solution is given, the results of the algorithm are analyzed, and directions for further research are determined.

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