论文标题
使用神经网络的N-烷烃/氮混合物的蒸气液平衡预测
Vapor-liquid equilibrium predictions of n-alkane/nitrogen mixtures using neural networks
论文作者
论文摘要
了解高压和高温条件下的流体相行为对于在液体燃料燃烧系统中对化学反应流的高保真模拟至关重要。蒸气液平衡（VLE）曲线的研究也构成了化学和油气工业控制过程的设计和建模的组成部分。这项研究的主要目的是开发数据驱动的模型，以预测涉及长链N-烷烃和氮的III型二元混合物的VLE。在这项研究中提出了两个数据驱动的模型，每个模型都在估算C10/N2和C12/N2的二元系统的VLE时，最大为50-60 MPa。与使用Peng-Robinson方程（PR-EOS）相比，这两个模型在预测二进制混合物的平衡压力方面均显示出更好的性能（较低的平均绝对百分比误差）。数据驱动的模型还能够正确地追踪蒸气相组成的曲率变化，靠近混合物临界点，在高压和温度下 - Pr-eos模型未能捕获的N-Alkane/Nitrogen System VLE的特征。
Understanding fluid phase behavior in high pressure and high temperature conditions is crucial for developing high-fidelity simulations of chemically reacting flows in liquid-fueled combustion systems. The study of vapor-liquid equilibrium (VLE) curves also forms an integral part of the design and modeling of the control processes in chemical and oil-gas industries. The main objective of this study was to develop data-driven models to predict VLE of Type III binary mixtures involving long-chained n-alkanes and nitrogen. Two data-driven models have been proposed in this study, each of which was competent in estimating VLE for the binary systems of C10/N2 and C12/N2, at pressures ranging up to 50-60 MPa. Both the models showed better performance (less average absolute percentage error) in predicting equilibrium pressure of the binary mixtures as compared to the VLE modeled using Peng-Robinson equation of state (PR-EOS). The data-driven models were also able to correctly trace the change in curvature of the vapor phase composition, close to the mixture critical point, at high pressures and temperatures-a feature of the n-alkane/nitrogen system VLE which the PR-EOS model fails to capture.
