Abstract In thermal de-asphalting procedure, where asphaltene particles are aggregated through heating crude oil at an elevated temperature, predicting the asphaltene particles’ diameter is of major importance. In this study, thermal de-asphalting process is modeled using adaptive neuro fuzzy inference system by considering isolating temperature, crude oil API and asphaltene content as inputs to predict asphaltene particles’ average diameter. The experimental data measured in a custom built thermal de-asphalting set-up, are applied to construct neuro fuzzy model structure. The average relative error of proposed model, for all data points is 3.06%, indicating an excellent agreement between model predictions and experimental values.

中文翻译：

---

预测平均沥青质粒径的自适应神经模糊推理系统案例研究：在热脱沥青过程中

摘要 在热脱沥青过程中，沥青质颗粒通过在高温下加热原油而聚集，预测沥青质颗粒的直径非常重要。在这项研究中，通过考虑隔离温度、原油 API 和沥青质含量作为预测沥青质颗粒平均直径的输入，使用自适应神经模糊推理系统对热脱沥青过程进行建模。在定制的热脱沥青装置中测量的实验数据用于构建神经模糊模型结构。对于所有数据点，所提出模型的平均相对误差为 3.06%，表明模型预测与实验值之间非常吻合。