Modeling of batch distillation process based on optimized CNN-BiLSTM neural network

doi:10.11949/0438-1157.20250145

Abstract

Abstract:

Batchwise distillation is widely employed in fine chemical, pharmaceutical, and food processing industries due to its operational flexibility and adaptability. However, its inherent non-steady-state characteristics and significantly varying operating conditions render traditional static models inadequate for accurately describing system dynamic behaviors, consequently leading to suboptimal separation efficiency of components within the column. To address this challenge, this study establishes a hybrid soft-sensor model (CNN-BiLSTM) integrating convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM), specifically targeting the prediction of ethanol mass fractions in both distillate and bottom products of an ethanol-water binary mixture system. The model hyperparameters are systematically optimized through an improved snow ablation optimization (ISAO) algorithm, aiming to develop a reliable alternative to online measurement instruments for enhanced batch distillation control. The experimental results show that in the prediction of the mass fraction of distillate and bottom ethanol, the root mean square error and mean absolute error of the CNN-BiLSTM neural network after ISAO optimization on the test set are reduced by at least 82.27% compared with the initial model. This significant enhancement in dynamic prediction capabilities validates the proposed methodology's effectiveness in addressing the operational challenges inherent to batch distillation processes.

Key words: batchwise, binary mixture, separation, neural network, hyperparameter optimization, optimization algorithm

摘要：

间歇式精馏因其操作灵活性和适应性广泛应用于精细化工、制药及食品加工等行业，然而其非稳态特性和显著变化的操作条件使得传统静态模型难以精确描述系统动态行为，最终导致物质在塔内的分离效果不佳。为此本研究以乙醇-水二元混合物体系间歇精馏塔馏出液和塔釜乙醇质量分数数据为研究对象，提出了一种基于卷积神经网络（convolutional neural network，CNN）和双向长短期记忆网络（bidirectional long short-term memory，BiLSTM）复合的预测软测量模型（CNN-BiLSTM），并通过改进的雪消融优化器（improved snow ablation optimizer，ISAO）优化模型超参数，旨在使其能够替代在线测量仪对间歇精馏控制起到辅助作用。实验结果表明，在馏出液和塔釜乙醇质量分数预测中，经过ISAO优化后的CNN-BiLSTM神经网络在测试集上的均方根误差和平均绝对误差相较于初始模型降幅至少为82.27%，其动态预测性能显著提升。

关键词: 间歇式, 二元混合物, 分离, 神经网络, 超参数优化, 优化算法

CLC Number:

O 6-31

Xu GUO, Jining JIA, Kejian YAO. Modeling of batch distillation process based on optimized CNN-BiLSTM neural network[J]. CIESC Journal, 2025, 76(9): 4613-4629.

郭旭, 贾继宁, 姚克俭. 基于优化CNN-BiLSTM神经网络的间歇精馏过程建模[J]. 化工学报, 2025, 76(9): 4613-4629.

Figures/Tables 22

References 30

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参数	数值
色谱柱温度/℃	180
进样口温度/℃	180
热导池温度/℃	200
桥电流/mA	110
氢气压力/MPa	0.01
进样体积/μl	1
色谱柱类型	Porapak-Q

参数	数值
色谱柱温度/℃	180
进样口温度/℃	180
热导池温度/℃	200
桥电流/mA	110
氢气压力/MPa	0.01
进样体积/μl	1
色谱柱类型	Porapak-Q

批次	加热功率/kW	原料乙醇质量分数	回流比	备注
1	1.75~2.50	0.429	1.0	调节加热功率使蒸汽率为5.6 L/h
2	1.70~2.50	0.492	1.0
3	1.65~2.50	0.589	1.0
4	1.70~2.50	0.652	1.0	调节加热功率使蒸汽率为6.5 L/h
5	1.73~2.50	0.490	1.5	调节加热功率使蒸汽率为5.6 L/h
6	1.70~2.50	0.590	1.5	调节加热功率使蒸汽率为5.6 L/h
7	1.75~2.25	0.650	1.5	调节加热功率使蒸汽率为6.5 L/h
8	1.75~2.50	0.493	2.0	调节加热功率使蒸汽率为5.6 L/h
9	1.70~2.50	0.592	2.0	调节加热功率使蒸汽率为5.6 L/h
10	1.78~2.25	0.654	2.0	调节加热功率使蒸汽率为6.5 L/h
11	1.77~2.50	0.486	2.5	调节加热功率使蒸汽率为5.6 L/h
12	1.72~2.50	0.614	2.5	调节加热功率使蒸汽率为5.6 L/h
13	1.75~2.25	0.685	2.5	调节加热功率使蒸汽率为6.5 L/h
14	2.0~2.25	0.674	2.5	调节加热功率使蒸汽率为8.5 L/h
15	1.65~2.50	0.683	2.5	调节加热功率使蒸汽率为5.6 L/h
16	1.80~2.25	0.677	3.0	调节加热功率使蒸汽率为6.5 L/h
17	1.75~2.50	0.726	3.5	调节加热功率使蒸汽率为6.5 L/h
18	1.60~2.50	0.483	3.3	恒定回流比生产操作，调节加热功率使蒸汽率为5.0 L/h
19	1.60~2.50	0.463	1.0 ~ 3.0	阶跃回流比生产操作，调节加热功率使蒸汽率为5.0 L/h

批次	加热功率/kW	原料乙醇质量分数	回流比	备注
1	1.75~2.50	0.429	1.0	调节加热功率使蒸汽率为5.6 L/h
2	1.70~2.50	0.492	1.0
3	1.65~2.50	0.589	1.0
4	1.70~2.50	0.652	1.0	调节加热功率使蒸汽率为6.5 L/h
5	1.73~2.50	0.490	1.5	调节加热功率使蒸汽率为5.6 L/h
6	1.70~2.50	0.590	1.5	调节加热功率使蒸汽率为5.6 L/h
7	1.75~2.25	0.650	1.5	调节加热功率使蒸汽率为6.5 L/h
8	1.75~2.50	0.493	2.0	调节加热功率使蒸汽率为5.6 L/h
9	1.70~2.50	0.592	2.0	调节加热功率使蒸汽率为5.6 L/h
10	1.78~2.25	0.654	2.0	调节加热功率使蒸汽率为6.5 L/h
11	1.77~2.50	0.486	2.5	调节加热功率使蒸汽率为5.6 L/h
12	1.72~2.50	0.614	2.5	调节加热功率使蒸汽率为5.6 L/h
13	1.75~2.25	0.685	2.5	调节加热功率使蒸汽率为6.5 L/h
14	2.0~2.25	0.674	2.5	调节加热功率使蒸汽率为8.5 L/h
15	1.65~2.50	0.683	2.5	调节加热功率使蒸汽率为5.6 L/h
16	1.80~2.25	0.677	3.0	调节加热功率使蒸汽率为6.5 L/h
17	1.75~2.50	0.726	3.5	调节加热功率使蒸汽率为6.5 L/h
18	1.60~2.50	0.483	3.3	恒定回流比生产操作，调节加热功率使蒸汽率为5.0 L/h
19	1.60~2.50	0.463	1.0 ~ 3.0	阶跃回流比生产操作，调节加热功率使蒸汽率为5.0 L/h

序号	特征		MIC	特征		MIC
序号	X	Y₁	MIC	X	Y₂	MIC
1	塔釜温度	馏出液乙醇浓度	0.3	塔釜温度	塔釜乙醇浓度	0.7
2	塔节一温度	馏出液乙醇浓度	0.3	塔节一温度	塔釜乙醇浓度	0.7
3	塔节三温度	馏出液乙醇浓度	0.5	塔节三温度	塔釜乙醇浓度	0.4
4	塔节五温度	馏出液乙醇浓度	0.7	塔节五温度	塔釜乙醇浓度	0.2
5	塔节六温度	馏出液乙醇浓度	0.5	塔节六温度	塔釜乙醇浓度	0.2
6	塔顶温度	馏出液乙醇浓度	0.4	塔顶温度	塔釜乙醇浓度	0.2
7	产品温度	馏出液乙醇浓度	0.4	产品温度	塔釜乙醇浓度	0.1
8	冷凝水流量	馏出液乙醇浓度	0.08	冷凝水流量	塔釜乙醇浓度	0.1
9	馏出液体积	馏出液乙醇浓度	0.2	馏出液体积	塔釜乙醇浓度	0.2
10	原料浓度	馏出液乙醇浓度	0.4	原料浓度	塔釜乙醇浓度	0.2
11	回流比	馏出液乙醇浓度	0.4	回流比	塔釜乙醇浓度	0.1
12	塔釜液位	馏出液乙醇浓度	0.2	塔釜液位	塔釜乙醇浓度	0.4
13	塔釜压力	馏出液乙醇浓度	0.4	塔釜压力	塔釜乙醇浓度	0.1
14	塔节二压力	馏出液乙醇浓度	0.4	塔节二压力	塔釜乙醇浓度	0.1
15	塔节四压力	馏出液乙醇浓度	0.5	塔节四压力	塔釜乙醇浓度	0.1
16	塔节七压力	馏出液乙醇浓度	0.4	塔节七压力	塔釜乙醇浓度	0.1