物理锚点残差驱动的胺液消泡智能软测量

李佳琪; 兰忠; 郝小静; 曹瑛; 张筠鹿

doi:10.16085/j.issn.1000-6613.2026-0989

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物理锚点残差驱动的胺液消泡智能软测量

AI化工 | 更新时间：2026-08-20

- 物理锚点残差驱动的胺液消泡智能软测量
- Physical Anchor Residual-Driven Intelligent Soft Sensing for Amine Defoaming
- 化工进展 2026年
- 作者机构：
  
  大连理工大学化工学院，辽宁大连 116031
- 作者简介：
  
  李佳琪（2003—），男，硕士研究生，研究方向为过程强化与智能化工，E-mail:ljqkaqiya@163.com。
  兰忠，副教授，博士生导师，研究方向为过程强化与智能化工，E-mail:LanZhong@dlut.edu.cn。
- 基金信息：
  
  中央高校基本科研业务费(DUT25RC(3)036)
- DOI：10.16085/j.issn.1000-6613.2026-0989
  中图分类号： TQ 02;TP183
- 收稿：2026-06-19，
  
  修回：2026-08-10，
  
  录用：2026-08-18，
- 稿件说明：
移动端阅览
李佳琪, 兰忠, 郝小静, 等. 物理锚点残差驱动的胺液消泡智能软测量[J/OL]. 化工进展, 2026.

Li Jiaqi, Lan Zhong, Hao Xiaojing, et al. Physical Anchor Residual-Driven Intelligent Soft Sensing for Amine Defoaming[J/OL]. Chemical Industry and Engineering Progress, 2026.
李佳琪, 兰忠, 郝小静, 等. 物理锚点残差驱动的胺液消泡智能软测量[J/OL]. 化工进展, 2026. DOI： 10.16085/j.issn.1000-6613.2026-0989.

Li Jiaqi, Lan Zhong, Hao Xiaojing, et al. Physical Anchor Residual-Driven Intelligent Soft Sensing for Amine Defoaming[J/OL]. Chemical Industry and Engineering Progress, 2026. DOI： 10.16085/j.issn.1000-6613.2026-0989.

摘要

胺液吸收塔在污油回炼过程中频繁发泡，严重影响装置平稳运行。本文提出一种融合时间卷积网络（TCN）、双向长短期记忆网络（BiLSTM）、自适应注意力及物理锚点残差的复合软测量模型。该模型将预测目标从泡沫的绝对高度改为残差增量，以此缓解强自回归场景下的过度平滑和时滞问题。模型前端用TCN做多尺度空间解耦，用BiLSTM捕捉消泡剂降解的长周期依赖，注意力层聚焦突变时刻，最后通过双向极值钳制损失函数强化极值区间的预测精度。在发泡动力学仿真数据上的测试结果为：决定系数R²达0.9456，均方误差MSE相比LSTM基线降低19.5%。消融实验分析，物理锚点残差范式向模型注入强归纳偏置，为胺液吸收塔发泡过程的精准预测与安全调控提供了可解释的智能策略。

Abstract

Frequent foaming in amine absorption towers during waste-oil recycling seriously affects the stable and safe operation of refining units. To achieve accurate soft sensing of foam height under strong inertia

nonlinear disturbances

and defoamer degradation

this study proposed a hybrid model driven by physical-anchor residual learning. Instead of directly predicting the absolute foam height

the previous foam height was introduced as a physical anchor

and the network was trained to learn the residual increment caused by gas-flow fluctuation

impurity accumulation

and defoamer attenuation. This design reduced the dominance of strong autoregressive trends and alleviated over-smoothing and time-delay problems in dynamic prediction. The proposed framework integrated a Temporal Convolutional Network (TCN)

a Bidirectional Long Short-Term Memory (BiLSTM) network

an adaptive attention mechanism

and a Dual-Extreme Loss function. The TCN module extracted and decoupled multi-scale features from noisy process variables

while BiLSTM captured long-term dynamic dependencies related to defoamer degradation. The attention mechanism enhanced the response to abrupt operating changes

and the Dual-Extreme Loss strengthened prediction accuracy in both high-risk high-foam regions and low-foam defoaming regions. Tests on foaming-kinetics simulation data showed that the proposed residual TCN-BiLSTM-Attention model achieved a coefficient of determination (R²) of 0.9456 and a mean squared error (MSE) of 2.760

reducing MSE by 19.5% compared with the Long Short-Term Memory (LSTM) baseline. Ablation experiments demonstrated that the physical-anchor residual paradigm introduced a strong inductive bias

suppressed overfitting in limited-sample conditions

and enabled the deep network to focus on disturbance-induced increments. The proposed method provides an interpretable and reliable intelligent strategy for foam prediction

early warning

and safety-oriented control in amine absorption towers.

关键词

Keywords

references

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