Yarahmadi Ali, Rashedi Ali, Bemani Amin
Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran.
Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Sci Rep. 2025 Mar 12;15(1):8469. doi: 10.1038/s41598-025-92608-2.
Given the application of cycloalkanes in surrogate blends for aviation fuels, their determination of critical characteristics pertinent to fuel transportation and combustion becomes imperative. In this study, we aim to construct intelligent models based on machine learning methods of random forest (RF), adaptive boosting, decision tree (DT), ensemble learning, K-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP) artificial neural network and convolutional neural network (CNN) to predict the density of binary blends of ethylcyclohexane or methylcyclohexane with n-hexadecane/n-dodecane/n-tetradecane in terms of operational conditions (pressure and temperature) and cycloalkane mole fractions in n-alkanes, utilizing laboratory data extracted from existing scholarly publications. The reliability of the data used is affirmed using an outlier detection algorithm, and the relevancy factor concept is utilized to find the relative effects of the input parameters on the output parameter. The preciseness of the developed models is checked and compared comprehensively via statistical and graphical methods. The obtained results indicate that temperature has the most effect on density, with a relevancy value of - 0.9619, and pressure has the least effective value, with a relevancy value of 0.041, which explains the straight relationship between pressure and density. The modeling results illustrate that DT and RF algorithms have the best performance in calculating density with R values of 0.9985 and 0.09982, respectively. The MLP and Adaboosting models exhibit the weakest performance in this field, with R values of 0.9455 and 0.9477, respectively. The current paper indicates robust tools for the accurate prediction of the density of binary blends of ethyl cyclohexane or methylcyclohexane with n-hexadecane/n-dodecane/n-tetradecane, which are required for fuel transportation and combustion studies.
鉴于环烷烃在航空燃料替代混合物中的应用，确定其与燃料运输和燃烧相关的关键特性变得至关重要。在本研究中，我们旨在基于随机森林（RF）、自适应提升、决策树（DT）、集成学习、K近邻（KNN）、支持向量机（SVM）、多层感知器（MLP）人工神经网络和卷积神经网络（CNN）等机器学习方法构建智能模型，以根据操作条件（压力和温度）以及正构烷烃中环烷烃的摩尔分数预测乙基环己烷或甲基环己烷与正十六烷/正十二烷/正十四烷二元混合物的密度，利用从现有学术出版物中提取的实验室数据。使用异常值检测算法确认所使用数据的可靠性，并利用相关因子概念来发现输入参数对输出参数的相对影响。通过统计和图形方法全面检查和比较所开发模型的精确性。所得结果表明，温度对密度的影响最大，相关值为 -0.9619，压力的影响最小，相关值为0.041，这解释了压力与密度之间的直接关系。建模结果表明，DT和RF算法在计算密度方面表现最佳，R值分别为0.9985和0.9982。MLP和Adaboosting模型在该领域表现最弱，R值分别为0.9455和0.9477。本文指出了用于准确预测乙基环己烷或甲基环己烷与正十六烷/正十二烷/正十四烷二元混合物密度的强大工具，这对于燃料运输和燃烧研究是必需的。
