Abstract

In this paper, two types of machine learning, namely neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP), have been studied to model light hydrocarbons’ solubility solvent in bitumen. The 268 number of experimental data is used in this work from different articles. The input parameters are Temperature (T), pressure (P), and molecular weight (MW) of hydrocarbons. The result shows the high performance of the MLP model with a two layers to predict the experimental values. The estimated values were investigated by statistical parameters such as R ², MSE, and MARD%. According to, statistical parameters, the values of 0.99, 0.00081, and 0.68 for MLP, and 0.96, 0.0029, and 0.78 for ANFIS indicate the high performance of the MLP model. Comparison between established models and previous work indicates that the developed model can be a suitable technique for solubility modeling

摘要

本文研究了两种类型的机器学习，即神经模糊推理系统（ANFIS）和多层感知器（MLP），以模拟轻质烃在沥青中的溶解性溶剂。这项工作使用了268个实验数据，来自不同的文章。输入参数是碳氢化合物的温度（T），压力（P）和分子量（MW）。结果显示了具有两层的MLP模型的高性能，可以预测实验值。通过统计参数（例如R ²，MSE和MARD）调查估计值％。根据统计参数，MLP的值为0.99、0.00081和0.68，ANFIS的值为0.96、0.0029和0.78表示MLP模型的高性能。已建立的模型与先前的工作之间的比较表明，所建立的模型可能是用于溶解度建模的合适技术