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

TL;DR

This paper presents a bio-inspired ant colony optimization approach to design hierarchical control system architectures in the chemical industry, aiming to optimize distributed process control systems for safety, quality, and cost efficiency.

Contribution

It introduces a formal optimization model for hierarchical control system design and applies a metaheuristic algorithm to find optimal configurations, demonstrating its effectiveness through numerical examples.

Findings

The proposed method effectively optimizes system architecture.

Experimental results show improved solution quality.

The approach is adaptable with potential for further enhancements.

Abstract

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.