The complexity of lipid feedstocks and the lack of data on physical properties hinder the simulation of oleochemical processing units. In this work, an iterative lumping approach is proposed to define an adequate number of key compounds such that diversification between lipid feedstocks becomes possible, while keeping the determination of physical properties as required for process modeling manageable. As a case study, the iterative lumping approach is used for simulation and optimization of a fatty acid distillation plant. For predicting vapor–liquid equilibria of fatty acids, the best results were acquired using the property method universal quasichemical-Hayden–O'Conell. Using the iterative lumping approach, 11 key compounds were selected to represent the feedstock. The process model properly predicts the product composition, yield, purity and heat duty. The most important process parameters are found to be side-reflux-ratio, reboiler-outlet-temperature, and heat-duty of the pitch-distiller. For optimization, an increase of the side-reflux-ratio and reboiler-outlet-temperature is recommended.

中文翻译：

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脂肪酸蒸馏模拟和优化中表示脂质原料的迭代集总方法

脂质原料的复杂性和缺乏物理性质的数据阻碍了油脂化学加工单元的模拟。在这项工作中，提出了一种迭代集总方法来定义足够数量的关键化合物，从而使脂类原料之间的多样化成为可能，同时保持对过程建模所需的物理性质的确定是可管理的。作为案例研究，迭代集总方法用于脂肪酸蒸馏厂的模拟和优化。为了预测脂肪酸的汽-液平衡，使用通用方法准通用化学-Hayden-O'Conell获得了最佳结果。使用迭代集总方法，选择了11种关键化合物来代表原料。流程模型可以正确预测产品组成，产量，纯度和热负荷。发现最重要的工艺参数是侧回流比，再沸器出口温度和沥青蒸馏器的热负荷。为了优化，建议增加侧面回流比和再沸器出口温度。