In the present study, experimental and modeling investigations were performed and combined to implement trustworthy paradigms to predict the viscosity value under different circumstances and a wide variety of conditions. The experimental approach was conducted on a considerable number of Iranian crude samples using a Rolling Ball viscometer. Accordingly, more than 1000 experimental points were gained. These latter were utilized as a databank in the modeling approach which included many advanced soft computing techniques, namely radial basis function (RBF) neural network, multilayer perceptron (MLP), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), decision trees (DTs) and random forest (RF). When performing the modeling tasks using these techniques, two distinct cases were considered: the first includes all available parameters as inputs such as pressure, temperature, API°, Mw of C₁₂₊ and the mole fractions till C11; whereas in the second case, a grouping scheme was considered to reduce the number of fractions. The obtained results revealed that DTs for the first case is the best implemented model with an overall average absolute relative deviation (AARD) of 3.379%. In addition, the comparison results with the preexisting approaches showed the superiority of the newly proposed model.

在本研究中，进行了实验和模型研究，并进行了组合，以实现可信赖的范例，以预测在不同情况和多种条件下的粘度值。实验方法是使用滚球粘度计对大量伊朗原油样品进行的。因此，获得了1000多个实验点。后者被用作建模方法中的数据库，其中包括许多先进的软计算技术，即径向基函数（RBF）神经网络，多层感知器（MLP），支持向量回归（SVR），自适应神经模糊推理系统（ANFIS） ），决策树（DT）和随机森林（RF）。使用这些技术执行建模任务时，考虑了两种不同的情况：₁₂₊和摩尔分数直到C11; 而在第二种情况下，考虑采用分组方案以减少分数的数量。获得的结果表明，第一种情况的DT是最佳实施的模型，总体平均绝对相对偏差（AARD）为3.379％。另外，与现有方法的比较结果显示了新提出的模型的优越性。