基于时滞感知Transformer的加氢装置柴油收率预测方法

阮志坚; 李佳鹤; 李佳洋

doi:10.11949/0438-1157.20260582

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基于时滞感知Transformer的加氢装置柴油收率预测方法

智能过程工程 | 更新时间：2026-06-26

- 基于时滞感知Transformer的加氢装置柴油收率预测方法
- A method for predicting diesel yield of hydrocracking unit based on time-lag-aware transformer
- 化工学报 2026年
- 作者机构：
  
  蓝卓数字科技有限公司，浙江杭州 310053
- 作者简介：
  
  阮志坚（1985—），男，硕士，高级工程师，ruanzhijian@supos.com
  李佳鹤（1983—），男，硕士，高级工程师，lijiahe@supos.com
- 基金信息：
  
  国家重点研发计划项目(2024YFB3311905)
- DOI：10.11949/0438-1157.20260582
  中图分类号： TP183;TE624
- 收稿：2026-04-24，
  
  修回：2026-06-26，
  
  录用：2026-06-26，
- 稿件说明：
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阮志坚, 李佳鹤, 李佳洋. 基于时滞感知Transformer的加氢装置柴油收率预测方法[J/OL]. 化工学报, 2026.

RUAN Zhijian, LI Jiahe, LI Jiayang. A method for predicting diesel yield of hydrocracking unit based on time-lag-aware transformer[J/OL]. CIESC Journal, 2026.
阮志坚, 李佳鹤, 李佳洋. 基于时滞感知Transformer的加氢装置柴油收率预测方法[J/OL]. 化工学报, 2026. DOI： 10.11949/0438-1157.20260582.

RUAN Zhijian, LI Jiahe, LI Jiayang. A method for predicting diesel yield of hydrocracking unit based on time-lag-aware transformer[J/OL]. CIESC Journal, 2026. DOI： 10.11949/0438-1157.20260582.

摘要

柴油收率受原料、催化剂及操作参数的非线性、异构性与时滞效应耦合影响，传统数据驱动模型因忽略物理可解释的时序依赖而精度受限。针对上述问题，提出一种基于时滞感知Transformer（Time-Lag Aware Transformer，TLAT）的柴油收率预测方法：首先基于条件互信息量化各变量对收率的有效时滞窗口；继而在Temporal Fusion Transformer基础上，设计工艺先验引导的结构化注意力掩码，约束模型仅在合理时滞范围内建模动态依赖；同时引入静态-动态门控融合模块，利用静态上下文生成可学习门控信号，动态调节动态特征的权重，实现静态变量对动态特征的自适应调制。基于某石化企业140万吨/年加氢裂化装置近3年104维高频率运行数据，TLAT在测试集上MAE达0.0182、R²为0.937，显著优于XGBoost、LSTM、Informer和标准TFT，为智能工厂精益管控提供有效支撑。

Abstract

Diesel yield is jointly influenced by the nonlinear

heterogeneous

and time-lag effects of feedstock properties

catalyst performance

and operational parameters. Conventional data-driven models suffer from limited prediction accuracy due to their inability to explicitly capture physically interpretable temporal dependencies. To address this issue

a diesel yield prediction method based on a Time-Lag Aware Transformer (TLAT) is proposed. First

conditional mutual information is employed to quantitatively identify the effective time-lag window of each process variable with respect to diesel yield. On this basis

a process-prior-guided structured attention mask is constructed within the Temporal Fusion Transformer (TFT) framework

which constrains the model to learn dynamic dependencies only within physically reasonable time-lag ranges. In addition

a static–dynamic gating fusion module is introduced

where static contextual information is utilized to generate learnable gating signals that dynamically adjust the contribution of dynamic features

enabling adaptive modulation of dynamic representations by static variables. The proposed TLAT model is validated using 104-dimensional high-frequency operational data collected over three years from a 1.4 Mt/a hydrocracking unit of a petrochemical enterprise. Experimental results demonstrate that TLAT achieves a mean absolute error (MAE) of 0.0182 and a coefficient of determination (R²) of 0.937 on the test set

significantly outperforming benchmark models including XGBoost

LSTM

Informer and the standard TFT. These results indicate that the proposed method provides effective technical support for refined monitoring and control in smart factory applications.

关键词

Keywords

references

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