Bitumen is extracted from oil sands using warm water and additives. The resulting bitumen froth is diluted with naphtha in a froth treatment process. Residual naphtha in the aqueous tailings of the froth treatment unit is recovered in a naphtha recovery unit (NRU). It is imperative to maximize the naphtha recovery process to minimize the plant's environmental and economic impact. It is, in this respect, that NRU vapour–liquid–liquid equilibrium data is of significant value. In this work, a paraffinic-aromatic synthetic naphtha (PASN) with a true boiling point (TBP) similar to that of froth treatment naphtha is used. Water/PASN mixtures are studied using the Soave-Redlich-Kwong equation of state with a Kabadi-Danner modification. The tangent plane distance (TPD) is evaluated as a possible criterion to calculate the number of phases, with its significant shortcomings being established. As well, experimental data obtained in a CREC-VL-Cell is observed to display higher solubilities of PASN in water than the ones obtained by HYSYS-Aspen Plus V9 simulation. To address these issues, a machine learning (ML)-based phase classification methodology was considered, predicting the number of phases with a 99% recall. This anticipates that ML will be of significant value for faster convergence of the flash split calculations for the naphtha hydrocarbon-water systems under consideration.

中文翻译：

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链烷烃芳烃合成石脑油/水混合物的汽-液-液和汽-液平衡：相数的预测

使用温水和添加剂从油砂中提取沥青。所得沥青泡沫在泡沫处理过程中用石脑油稀释。泡沫处理装置尾矿中的残留石脑油在石脑油回收装置 (NRU) 中回收。必须最大限度地提高石脑油回收过程，以尽量减少工厂对环境和经济的影响。在这方面，NRU 气-液-液平衡数据具有重要价值。在这项工作中，使用了真沸点 (TBP) 与泡沫处理石脑油相似的链烷烃-芳烃合成石脑油 (PASN)。使用具有 Kabadi-Danner 修正的 Soave-Redlich-Kwong 状态方程研究水/PASN 混合物。切面距离 (TPD) 被评估为计算相数的可能标准，其重大缺陷已被确立。同样，观察到在 CREC-VL-Cell 中获得的实验数据显示 PASN 在水中的溶解度高于通过 HYSYS-Aspen Plus V9 模拟获得的数据。为了解决这些问题，我们考虑了一种基于机器学习 (ML) 的阶段分类方法，以 99% 的召回率预测阶段的数量。这预计 ML 对于正在考虑的石脑油烃-水系统的闪蒸分裂计算的更快收敛将具有重要价值。考虑了基于机器学习 (ML) 的阶段分类方法，预测具有 99% 召回率的阶段数。这预计 ML 对于正在考虑的石脑油烃-水系统的闪蒸分裂计算的更快收敛将具有重要价值。考虑了基于机器学习 (ML) 的阶段分类方法，预测具有 99% 召回率的阶段数。这预计 ML 对于正在考虑的石脑油烃-水系统的闪蒸分裂计算的更快收敛将具有重要价值。