3.Cp=f(T) : Accurate Prediction of Heat Capacity Cp(T) for Gaseous Compounds
Reliable predictions of ideal-gas molar heat capacities at constant pressure are a cornerstone of thermodynamic modeling. In the absence of a reliable model or correlation for the estimation of the ideal-gas heat capacity, an equation of state (EoS) alone cannot ensure thermodynamically consistent calculations of state-function variations, nor can it provide real-fluid heat capacities, since it only yields residual contributions. Changes in internal energy, enthalpy, entropy, exergy, and real-fluid heat capacities require the integration of the ideal-gas contribution which depends solely on the temperature-dependent ideal-gas heat capacity. Thus, coupling an EoS with an ideal-gas heat capacity model enables full thermodynamic property evaluation in process simulators. In this paper, we propose a deep-learning framework for continuous prediction of temperature-dependent ideal-gas molar heat capacities at constant pressure (Cp) via artificial neural networks (ANNs) trained using molecular descriptors computed with Mordred and a high-quality experimental database comprising 1471 compounds. The dataset was augmented using experimentally validated correlations, generating 100 Cp data points per molecule over their corresponding temperature range and several ANN models were trained in a bagging ensemble configuration. The resulting model achieves excellent predictive accuracy (R² > 0.999 for both training and test sets) while demonstrating strong generalization to external datasets, indicating that the framework captures transferable structure-thermodynamic relationships. By explicitly treating temperature as an input variable, the proposed model provides smooth and continuous ideal-gas CpT predictions while preserving physically consistent thermodynamic trends across temperature ranges relevant to industrial applications.
https://doi.org/10.1016/j.ceja.2026.101140