用于Cx-N2二元混合物汽液平衡预测的符号机器学习

中文摘要 AI 辅助

烃-氮混合物的汽液平衡（VLE）预测对立方状态方程而言仍具挑战性，尤其在宽组成范围和烃链长度变化时。深度学习模型虽能提供准确预测，但常缺乏可解释性和显式解析表达式。本研究提出符号机器学习方法，从实验数据中发现对Peng-Robinson状态方程（PR-EOS）预测的可解释符号校正项，采用两级策略：先为单个烃系统确定符号表达式，再将其系数表示为碳数的函数，以实现对不同烃系统的准确预测。结果显示，在所有烃-氮系统中，该方法较原始PR-EOS的预测精度显著提升，为改进PR-EOS对烃-氮VLE的预测提供了可解释的符号校正框架。

英文摘要

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.

发表机构

• Purdue University（普渡大学）

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