基于BO-LSTM的低温甲醇洗净化气CO2含量预测

doi:10.16085/j.issn.1000-6613.2024-0244

摘要/Abstract

摘要：

低温甲醇洗工艺作为我国煤化工生产中较为成熟的净化工艺，对其进行建模研究有利于工厂智能化建设与发展。本文基于某石化企业低温甲醇洗装置智能化建设的需要，建立了实时数据预处理规则，并基于贝叶斯优化（BO）的长短时记忆（LSTM）神经网络建立净化气CO₂含量预测模型。结果表明，通过人工筛选及校正和最大信息系数（MIC）方法对装置的实时数据预处理后，可将采集的实时数据变量数由84个降低到22个，降低了数据冗余度；运用BO算法对LSTM模型的6个超参数进行调优，根据优化后的超参数组合建立BO-LSTM预测模型，得到BO-LSTM预测模型的评价指标均方根误差（RMSE）、平均绝对误差（MAE）、回归系数（R²）分别为0.0395、0.0275、0.9843，相比于传统的反向传播（BP）与LSTM模型精度更高、回归效果更好，证明了该模型在低温甲醇洗净化气组成预测运用中的可行性与准确性，能够为净化气产品的生产优化做指导。基于低温甲醇洗净化气CO₂含量预测模型的产品优化建模方法可为相关工艺数字化和智能化建设提供思路。

关键词: 低温甲醇洗, 贝叶斯优化, 神经网络, 计算机模拟, 预测

Abstract:

Rectisol process is a relatively mature purification process in China's coal chemical production, and its modeling research is conducive to the intelligent construction and development of the plant. In this paper, based on the need of intelligent construction of rectisol plant in a petrochemical enterprise, real-time data preprocessing rules were established, and the CO₂ content prediction model of purified gas was built based on the Bayesian optimization (BO) long short-term memory (LSTM) neural network. The results showed that after preprocessing the real-time data of the device by manual screening and correction and the maximum information coefficient (MIC) method, the number of variables of the collected real-time data could be reduced from 84 to 22, which reduced the redundancy of the data. The six hyper-parameters of the LSTM model were tuned by using the BO, and the BO-LSTM prediction model was established based on the optimized hyper-parameter combinations. The evaluation indexes of RMSE, MAE and R² of the prediction model were 0.0395, 0.0275 and 0.9843, respectively, which were higher in precision and better in regression effect than the traditional BP and LSTM model, proving the feasibility and accuracy of the model in the prediction of the rectisol purified gas composition, and it could guide the optimization of the production of purified gas products. The product optimization modeling method based on the CO₂ content prediction model of rectisol purified gas could provide ideas for the digital and intelligent construction of related processes.

Key words: rectisol, Bayesian optimization, neural network, computer simulation, prediction

中图分类号:

TQ015

孙悦鹏, 孙延吉, 潘艳秋, 王成宇. 基于BO-LSTM的低温甲醇洗净化气CO₂含量预测[J]. 化工进展, 2025, 44(2): 688-697.

SUN Yuepeng, SUN Yanji, PAN Yanqiu, WANG Chengyu. Prediction of CO₂ content in Rectisol purified gas based on BO-LSTM[J]. Chemical Industry and Engineering Progress, 2025, 44(2): 688-697.

图/表 12

图1 低温甲醇洗工艺流程简图V101—水分离器；T101—甲醇洗涤塔；T102—CO2产品塔；T103—H2S浓缩塔；T104—CO2气提塔；T105—热再生塔；T106—甲醇/水分离塔；T107—尾气洗涤塔；V102—H2S分离器；P101—贫甲醇泵

表1 低温甲醇洗装置实时数据采集表

时间	FIA1001 /m³·h^-1	TIA1001 /℃	PI1002 /MPa	TI1008 /℃	…	AIA1006 /%
11/16/8:00	343.332	26.691	5.323	-17.368	…	3.275
11/16/7:59	340.978	26.553	5.322	-17.423	…	3.278
11/16/7:58	341.077	26.722	5.323	-17.395	…	3.274
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$	…	$⋮$
10/16/8:02	367.376	27.265	5.365	-17.726	…	3.334
10/16/8:01	368.837	27.490	5.364	-17.561	…	3.327
10/16/8:00	364.665	27.425	5.364	-17.539	…	3.295

表1 低温甲醇洗装置实时数据采集表

时间	FIA1001 /m³·h^-1	TIA1001 /℃	PI1002 /MPa	TI1008 /℃	…	AIA1006 /%
11/16/8:00	343.332	26.691	5.323	-17.368	…	3.275
11/16/7:59	340.978	26.553	5.322	-17.423	…	3.278
11/16/7:58	341.077	26.722	5.323	-17.395	…	3.274
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$	…	$⋮$
10/16/8:02	367.376	27.265	5.365	-17.726	…	3.334
10/16/8:01	368.837	27.490	5.364	-17.561	…	3.327
10/16/8:00	364.665	27.425	5.364	-17.539	…	3.295

图2 实时数据预处理规则流程

表2 输入变量与输出变量相关性分析结果示例

变量序号	MIC数值
1	0.1776
2	0.1965
3	0.1575
4	0.2387
5	0.2136
$⋮$	$⋮$
71	0.1831

表2 输入变量与输出变量相关性分析结果示例

变量序号	MIC数值
1	0.1776
2	0.1965
3	0.1575
4	0.2387
5	0.2136
$⋮$	$⋮$
71	0.1831

表3 输入变量位号及含义

序号	位号	含义
1	PI1002	原料气压力
2	TI1003	原料气进入水分离器温度
3	TI1072	尾气洗涤塔塔顶出口物料温度
4	TIA1099	尾气洗涤塔塔底进料温度
5	FICA1005	甲醇洗涤塔中段回流流量
6	TI1010	甲醇洗涤塔中段回流温度
7	TI1013	H₂S浓缩塔中段物料压缩后温度
8	TIA1006	甲醇洗涤塔塔顶出口物料温度
9	TIA1015	甲醇洗涤塔塔顶甲醇进料温度
10	FICA1051	CO₂产品塔塔顶溶液进料流量
11	TI1033	循环甲醇中段换热器出口温度
12	TI1036	H₂S浓缩塔塔底换热器出口温度
13	TI1037	贫甲醇泵出口换热器温度
14	TI1041	CO₂汽提塔塔顶出口物料温度
15	TI1094	H₂S浓缩塔塔顶富甲醇进料温度
16	TIA1031	H₂S浓缩塔中段物料采出温度
17	TIA1076	贫甲醇泵进料温度
18	TIA1106	CO₂产品塔塔顶出口物料温度
19	FIA1038	克劳斯气流量
20	TIA1051	热再生塔底再沸器回流温度
21	TIA1059	克劳斯气温度

图3 LSTM神经网络结构示意图

图4 LSTM隐含层中细胞层结构示意图

图5 BO-LSTM预测模型流程图

表4 预测模型超参数选取范围与优化结果

超参数	取值范围	优化结果
LSTM层1神经元个数	[40,100]	84
LSTM层2神经元个数	[10,50]	21
Dropout层1丢弃概率	[0.1,0.5]	0.4262
Dropout层2丢弃概率	[0.1,0.5]	0.1520
初始学习率	[0.001,0.1]	0.0564
批次大小	[1,50]	38

图6 BO-LSTM模型预测结果R2

表5 神经网络样本模型超参数组合

超参数	模型1	模型2	模型3	模型4	模型5
LSTM层1神经元个数	54	68	97	92	74
LSTM层2神经元个数	46	39	20	18	53
Dropout层1丢弃概率	0.1043	0.4660	0.1222	0.3935	0.4971
Dropout层2丢弃概率	0.1279	0.4137	0.2510	0.2021	0.3734
初始学习率	0.0013	0.0195	0.0208	0.0596	0.0846
批次大小	25	11	14	27	49

图7 模型预测结果评价指标对比

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基于BO-LSTM的低温甲醇洗净化气CO₂含量预测

Prediction of CO₂ content in Rectisol purified gas based on BO-LSTM

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