Thermodynamic modeling and process simulation form the backbone of modern chemical and energy industries, enabling the design, optimization, and safe operation of complex processes. Despite rapid advances in computational power and theoretical frameworks, experimental data remain indispensable to the credibility and usefulness of these tools. This editorial discusses how experimental measurements have shaped the evolution of thermodynamic models and process simulators, why they continue to be essential in the era of digitalization and machine learning, and how future progress depends on a balanced integration of experiments, theory, and computation.
