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Updated: Jan 8, 2026

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用数据驱动的方法来预测使用轻型和重型原料充电的FCC单元中的汽油产量:以PCA为指导,为增强的建模体验进行PCA引导的分组

Kawthar Alelg¹, Azzam Alfarraj², Abdulkadir Tanimu^1,3

¹Department of Chemistry, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

Journal of chemical information and modeling

|December 22, 2025

概括

这项研究开发了流体催化裂变 (FCC) 装置的料意识框架,通过根据原料类型对数据进行细分来提高预测准确性. 高斯过程回归 (GPR) 显示出最佳表现,特别是在数据细分后.

科学领域:

化学工程是化学工程的重要组成部分.
数据科学数据科学数据科学

背景情况:

流体催化裂变 (FCC) 装置在石油精炼中至关重要.
由于非线性过程和不同的原料,FCC单元的准确建模是复杂的.
现有的模型经常与各种原料成分和产量预测作斗争.

研究的目的:

为FCC单位引入一个新的Feed-aware建模框架.
为了提高关键运营目标的预测准确度,如转换和汽油产量.
评估基于原料化学的数据细分的影响.

主要方法:

开发了一个整合集团异常检测,基于PCA的细分和贝叶斯优化回归的框架.
训练并优化了高斯过程回归 (GPR),增强树 (BT) 和强大的线性回归 (RLR) 模型.
利用试点工厂实验数据,采用5倍交叉验证和K-means集群进行细分.

主要成果:

不分割的建模显示GPR和BT实现了R2 ≈ 0.72-0.73的转换.
在汽油产量方面,BT表现最好 (R2 ≈ 0.67),而GPR和RLR的技能有限.
通过PCA和聚类成功按原料细分数据,显著提高了GPR性能 (R2>0.90用于纳夫塔子集的转换和产量).

结论:

基于原料的数据细分对于可靠的FCC单位建模至关重要.
高斯过程回归 (GPR) 在各种料类型中表现出卓越的准确性和通用性.
拟议的框架提供了一种可靠的方法,用于以数据为导向的催化裂变过程的优化.

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