Hybrid Optimization Methods for Parameter Estimation of Reactive Transport Systems
Optimization and Control
2025-12-16 v1
Abstract
This paper presents a hybrid optimization methodology for parameter estimation of reactive transport systems. Using reduced-order advection-diffusion-reaction (ADR) models, the computational requirements of global optimization with dynamic PDE constraints are addressed by combining metaheuristics with gradient-based optimizers. A case study in preparative liquid chromatography shows that the method achieves superior computational efficiency compared to traditional multi-start methods, demonstrating the potential of hybrid strategies to advance parameter estimation in large-scale, dynamic chemical engineering applications.
Cite
@article{arxiv.2503.12469,
title = {Hybrid Optimization Methods for Parameter Estimation of Reactive Transport Systems},
author = {Marcus Johan Schytt and Halldór Gauti Pétursson and John Bagterp Jørgensen},
journal= {arXiv preprint arXiv:2503.12469},
year = {2025}
}
Comments
7 pages, 3 figures