用符号机器学习提升烃氮混合物相平衡预测精度
Symbolic Machine Learning for Vapor-Liquid Equilibrium Prediction in Cx-N2 Binary Mixtures
- 从实验数据中发现可解释的符号修正项，增强彭-罗宾逊方程
- 对不同碳数烃类均实现更高预测精度，覆盖宽范围组成
- 适合需要可解释性与高精度的化工相平衡建模场景
烃类-氮气混合物的汽液相平衡（VLE）精确预测对立方型状态方程仍是挑战，尤其在广泛组分和烃链长度范围内。尽管深度学习模型可提供高精度预测，但缺乏可解释性与显式解析表达式。本文提出一种符号机器学习方法，从实验数据中发现可解释的符号修正项，用于改进彭-罗宾逊状态方程（PR-EOS）的预测。该方法采用两级策略：首先为单一烃类体系识别符号表达式，再将系数表示为碳数的函数，从而实现跨不同烃类体系的准确预测。结果表明，该方法在所有烃-氮系统中均显著优于原始PR-EOS。整体上，该框架为提升烃-氮VLE的PR-EOS预测提供了可解释的符号修正方案。
原文摘要 · Abstract (English)
Accurate prediction of vapor--liquid equilibrium (VLE) for hydrocarbon-nitrogen mixtures remains challenging for cubic equations of state, particularly across broad ranges of composition and hydrocarbon chain length. While deep learning models can provide accurate predictions, they often lack interpretability and explicit analytical expressions. In this work, we propose a symbolic machine learning approach to discover interpretable symbolic corrections to Peng-Robinson equation-of-state (PR-EOS) predictions from experimental data. The proposed approach adopts a two-level strategy: symbolic expressions are first identified for individual hydrocarbon systems, after which their coefficients are represented as functions of carbon number to enable accurate prediction across different hydrocarbon systems. The results demonstrate significantly improved prediction accuracy over the original PR-EOS across all hydrocarbon-nitrogen systems. Overall, the proposed approach provides an interpretable symbolic correction framework for improving PR-EOS predictions of hydrocarbon-nitrogen VLE.
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