Abstract
Reliable kinetic models are essential for the design, optimisation and operation of methanol synthesis reactors in power-to-X applications. However, parameter estimation is frequently performed without prior assessment of parametric identifiability or the information content of experimental conditions, often resulting in poorly constrained parameters and inefficient experimental campaigns. This study introduces a systematic, pre-calibration, information-driven framework for identifiability analysis and experimental design. The framework integrates local sensitivity analysis, structural and practical identifiability metrics and a Sequential Information-Driven Experimental Selection (SIDeS) strategy to guide experiment selection prior to parameter estimation. The methodology is applied to four literature kinetic models for methanol synthesis, spanning varying levels of mechanistic detail. A Sobol-sampled design space is first screened for feasibility and local information content, followed by cumulative Fisher information analysis. Results demonstrate that conventional screening campaigns provide limited gains in parameter precision, whereas SIDeS identify compact experimental sequences that significantly improve practical identifiability within a constrained experimental budget. Cross-model comparison reveals that increased mechanistic description does not necessarily translate to improved identifiability under fixed experimental resources. The proposed framework supports informed model selection, reduces unnecessary calibration effort, and provides actionable design-of-experiments recommendations for methanol synthesis systems.