Department of Mathematics, HITEC University, Taxila 47080, Rawalpindi, Pakistan
Email: quaid.taimoor@gmail.com (M. T.)
*Corresponding author
Manuscript received February 20, 2026; accepted March 20, 2026; published April 21, 2026
Abstract—Advanced liquid chromatography remains the most effective method for separating, identifying, and quantifying intricate mixtures in the fields of chemistry, engineering, and life sciences. Modifications have been implemented in chromatographic modelling and optimization to meet demands for enhanced resolution, increased speed, and improved durability. For example, transitioning from single-dimensional separations to dual-dimensional workflows. This review thoroughly examines the methodologies based on parameters and moments used to define mass transfer, dispersion, retention, and reaction kinetics in liquid chromatography systems, emphasizing their significance in method development and performance enhancement. This article explores the latest advancements in data-centric and AI-driven approaches, focusing on retention forecasting, quantitative structure–retention correlations, and automated parameter fine-tuning. The synergistic integration of mechanistic models and machine-learning techniques is explored as a feasible strategy to address challenges in complex method development, multidimensional data management, and computational costs. Some of the current issues and new trends being examined include real-time adaptive control, generative method design, and environmentally friendly chromatographic practices, but they depend on reliable online sensing, low-latency inference, and validated digital twin frameworks. This review aims to guide future progress in HPLC modelling and optimization by synthesizing theoretical, computational, and practical perspectives.
Keywords—parametric analysis, reversible/irreversible, machine learning/artificial intelligence, method optimization, future perspectives
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Cite: Muhammad Tamoor, "Integration of Parametric, Mechanistic, and AI-Based Modelling in High-Performance Liquid Chromatography: Future Prospect," International Journal of Modeling and Optimization, vol. 16, no. 1, pp. 15-22, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
