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

TL;DR

This paper introduces a hybrid ant colony metaheuristic for designing cost-effective, reliable distributed control system hardware architectures in large-scale chemical plants, validated through a sulfuric acid plant case study.

Contribution

It presents a formal model and a hybrid metaheuristic framework for optimizing distributed control system hardware architectures under uncertainty.

Findings

Validated on a large-scale sulfuric acid plant case study

Optimized architecture meets structural and dynamic performance requirements

Demonstrated feasibility of the proposed approach

Abstract

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.