Kooti Ghazal, Dabir Bahram, Butscher Christoph, Taherdangkoo Reza
Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Department of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran.
Sci Rep. 2024 May 15;14(1):11142. doi: 10.1038/s41598-024-61535-z.
Accurately describing the evolution of water droplet size distribution in crude oil is fundamental for evaluating the water separation efficiency in dehydration systems. Enhancing the separation of an aqueous phase dispersed in a dielectric oil phase, which has a significantly lower dielectric constant than the dispersed phase, can be achieved by increasing the water droplet size through the application of an electrostatic field in the pipeline. Mathematical models, while being accurate, are computationally expensive. Herein, we introduced a constrained machine learning (ML) surrogate model developed based on a population balance model. This model serves as a practical alternative, facilitating fast and accurate predictions. The constrained ML model, utilizing an extreme gradient boosting (XGBoost) algorithm tuned with a genetic algorithm (GA), incorporates the key parameters of the electrostatic dehydration process, including droplet diameter, voltage, crude oil properties, temperature, and residence time as input variables, with the output being the number of water droplets per unit volume. Furthermore, we modified the objective function of the XGBoost algorithm by incorporating two penalty terms to ensure the model's predictions adhere to physical principles. The constrained model demonstrated accuracy on the test set, with a mean squared error of 0.005 and a coefficient of determination of 0.998. The efficiency of the model was validated through comparison with the experimental data and the results of the population balance mathematical model. The analysis shows that the initial droplet diameter and voltage have the highest influence on the model, which aligns with the observed behaviour in the real-world process.
准确描述原油中水滴尺寸分布的演变对于评估脱水系统中的水分离效率至关重要。通过在管道中施加静电场来增大水滴尺寸，可以增强分散在介电常数明显低于分散相的介电油相中的水相的分离。数学模型虽然准确，但计算成本高昂。在此，我们引入了一种基于总体平衡模型开发的约束机器学习（ML）替代模型。该模型是一种实用的替代方法，有助于快速准确地进行预测。该约束ML模型利用通过遗传算法（GA）调优的极端梯度提升（XGBoost）算法，将静电脱水过程的关键参数作为输入变量，包括液滴直径、电压、原油性质、温度和停留时间，输出为单位体积内的水滴数量。此外，我们通过纳入两个惩罚项来修改XGBoost算法的目标函数，以确保模型的预测符合物理原理。该约束模型在测试集上表现出准确性，均方误差为0.005，决定系数为0.998。通过与实验数据和总体平衡数学模型的结果进行比较，验证了该模型的效率。分析表明，初始液滴直径和电压对模型的影响最大，这与实际过程中观察到的行为一致。
