隔板精馏塔的优化设计及动态控制研究进展

doi:10.11949/0438-1157.20241438

摘要/Abstract

摘要：

隔板精馏塔作为一种高效节能的精馏技术，因其在化工分离过程中的优越性能，得到了广泛关注。综述了隔板塔在优化设计和动态控制方面的研究进展，分析列举了迭代法、元启发式算法和基于机器学习的优化设计算法在隔板塔操作参数优化过程中的应用，介绍了隔板塔控制结构的发展历程，从三点控制结构到四点控制结构、温度推断控制结构以及更加复杂和先进的智能控制策略，逐步改善了隔板塔的动态控制性能，重点介绍了模型预测控制策略在隔板塔控制中的应用。最后，指出隔板塔当前研究存在的问题并对未来发展给出展望，旨在进一步促进隔板塔的工业化进程。

关键词: 精馏, 隔板精馏塔, 优化设计, 动态控制, 模型预测控制

Abstract:

As a highly efficient and energy-saving distillation technology, dividing wall column (DWC) has attracted extensive attention due to its superior performance in chemical separation processes. The research progress on the optimal design and dynamic control of DWC is reviewed. The applications of iterative algorithms, metaheuristic algorithms, and machine learning-based optimization algorithms in optimizing the operating parameters of DWC are analyzed. Furthermore, the development of control structures for DWC is discussed, ranging from three-point control structures, four-point control structures, temperature inferential control structures, to more advanced intelligent control strategies, which have progressively enhanced the dynamic control performance of DWC. In particular, the application of model predictive control (MPC) strategy for controlling DWC is emphasized. Finally, this work highlights the current challenges in research and provides an outlook for future development, aiming to further promote the industrialization of DWC technology.

Key words: distillation, dividing wall column, optimal design, dynamic control, model predictive control

中图分类号:

TQ 028.3

张豪豪, 郭莉, 李馨怡, 陈锦溢, 华超, 陆平. 隔板精馏塔的优化设计及动态控制研究进展[J]. 化工学报, 2025, 76(6): 2434-2450.

Haohao ZHANG, Li GUO, Xinyi LI, Jinyi CHEN, Chao HUA, Ping LU. Research progress on optimal design and dynamic control of dividing wall column[J]. CIESC Journal, 2025, 76(6): 2434-2450.

图/表 12

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年份	设计/建造公司	地点	应用装置/体系	效果	标志	文献
1999	Sumitomo	日本Kyowa Yuka公司	乙酸乙酯分离装置	侧线乙酸乙酯纯度99.99% 节省30%设备投资和40%能耗	—	[15]
2000	Kellogg, BP	英国Coryton炼油厂	改造烷基重整流程	改造后塔操作能力增加一倍中间产物产量增加50%	—	[11]
2000	BASF Montz	南非Sosol公司	费托合成中回收碳氢化合物	板式塔，塔高107 m，塔径5.2 m	当时世界上最大的隔板塔	[16]
2002	Koch-Glitsch	西班牙CEPSA炼油厂	改造烷烃和异构烷烃分离精馏塔	增产异己烷的同时节能40%	—	[17]
2002	BASF	德国BASF SE工厂	未公开	—	全球首座工业化的四产品隔板塔	[18]
2004	Uhde	德国ARAL芳烃公司	重整生成油中回收苯	能耗节省20%	世界上第一座工业化的萃取隔板塔	[19]
2005	ExxonMobil	英国Fawley炼油厂法国Port Jerome炼油厂	改造二甲苯回收塔	二甲苯纯度提高，能耗降低53%	—	[20]
2005	ExxonMobil	荷兰鹿特丹石化厂	新建苯-甲苯-二甲苯隔板分离塔	—	—	[21]
2010	Lonza	瑞士Visp生产基地	未公开	隔板塔可替代带侧线的精馏塔、间歇精馏塔和带薄膜蒸发器的精馏塔	全球首个多用途隔板塔	[22]

年份	设计/建造公司	地点	应用装置/体系	效果	标志	文献
1999	Sumitomo	日本Kyowa Yuka公司	乙酸乙酯分离装置	侧线乙酸乙酯纯度99.99% 节省30%设备投资和40%能耗	—	[15]
2000	Kellogg, BP	英国Coryton炼油厂	改造烷基重整流程	改造后塔操作能力增加一倍中间产物产量增加50%	—	[11]
2000	BASF Montz	南非Sosol公司	费托合成中回收碳氢化合物	板式塔，塔高107 m，塔径5.2 m	当时世界上最大的隔板塔	[16]
2002	Koch-Glitsch	西班牙CEPSA炼油厂	改造烷烃和异构烷烃分离精馏塔	增产异己烷的同时节能40%	—	[17]
2002	BASF	德国BASF SE工厂	未公开	—	全球首座工业化的四产品隔板塔	[18]
2004	Uhde	德国ARAL芳烃公司	重整生成油中回收苯	能耗节省20%	世界上第一座工业化的萃取隔板塔	[19]
2005	ExxonMobil	英国Fawley炼油厂法国Port Jerome炼油厂	改造二甲苯回收塔	二甲苯纯度提高，能耗降低53%	—	[20]
2005	ExxonMobil	荷兰鹿特丹石化厂	新建苯-甲苯-二甲苯隔板分离塔	—	—	[21]
2010	Lonza	瑞士Visp生产基地	未公开	隔板塔可替代带侧线的精馏塔、间歇精馏塔和带薄膜蒸发器的精馏塔	全球首个多用途隔板塔	[22]

体系	隔板类型	目标函数	模拟软件	优化算法	优化结果	文献
乙酸提纯	DWC	再沸器负荷	Hysys	迭代试错法	较常规精馏序列能耗减少37.8%	[46]
异丙醇和水分离吡啶和水分离	ADWC-VRAP	TAC	Aspen	迭代试错法	较常规ADWC能耗减少47%~80%	[47]
乙酸正丙酯生产	RDWC	TAC	Matlab & Aspen	网格自适应直接搜索算法	较常规反应精馏工艺TAC减少10.44%	[48]
混合醇分离	DWC	TAC	Matlab & Aspen	改进的粒子群算法	较常规粒子群算法TAC减少1.18%	[49]
苯和环己烯分离	EDWC	TAC	Matlab & Aspen	粒子群算法	较常规萃取精馏工艺可减少19.15%的TAC、36.08%的二氧化碳排放量、19.99%的㶲损失以及8.03%的萃取剂消耗量	[50]
乙腈和正丙醇分离	EDWC	TAC	Python & Aspen	改进的和声搜索算法	较顺序迭代算法TAC可减少12.97%	[44]
混合苯分离	DWC	总投资成本总运营成本二氧化碳排放量苯、二甲苯回收率	Matlab & Aspen	多目标NSGA-Ⅱ算法	较常规精馏序列总投资成本减少23%、总运营成本减少45%、二氧化碳排放量减少45%	[51]
二乙二醇单甲醚和N-甲基吡咯烷酮分离	EDWC	总投资成本总运营成本	Matlab & Aspen	多目标遗传算法	较常规萃取精馏工艺能耗减少26.29%、TAC减少24.15%	[52]
乙酸乙酯和甲醇分离	EDWC	TAC 二氧化碳排放量过程安全指数（PRI）	Matlab & Aspen	多目标粒子群算法	较四塔基础工艺TAC减少20.20%、二氧化碳排放量降低33.81%、安全指数提高18%	[53]
环己烷和环己烯分离	EDWC	TAC 二氧化碳排放量	Matlab & Aspen	多目标NSGA-Ⅱ算法	较常规萃取精馏流程可减少9.46%的TAC和17.25%的二氧化碳排放	[54]

体系	隔板类型	目标函数	模拟软件	优化算法	优化结果	文献
乙酸提纯	DWC	再沸器负荷	Hysys	迭代试错法	较常规精馏序列能耗减少37.8%	[46]
异丙醇和水分离吡啶和水分离	ADWC-VRAP	TAC	Aspen	迭代试错法	较常规ADWC能耗减少47%~80%	[47]
乙酸正丙酯生产	RDWC	TAC	Matlab & Aspen	网格自适应直接搜索算法	较常规反应精馏工艺TAC减少10.44%	[48]
混合醇分离	DWC	TAC	Matlab & Aspen	改进的粒子群算法	较常规粒子群算法TAC减少1.18%	[49]
苯和环己烯分离	EDWC	TAC	Matlab & Aspen	粒子群算法	较常规萃取精馏工艺可减少19.15%的TAC、36.08%的二氧化碳排放量、19.99%的㶲损失以及8.03%的萃取剂消耗量	[50]
乙腈和正丙醇分离	EDWC	TAC	Python & Aspen	改进的和声搜索算法	较顺序迭代算法TAC可减少12.97%	[44]
混合苯分离	DWC	总投资成本总运营成本二氧化碳排放量苯、二甲苯回收率	Matlab & Aspen	多目标NSGA-Ⅱ算法	较常规精馏序列总投资成本减少23%、总运营成本减少45%、二氧化碳排放量减少45%	[51]
二乙二醇单甲醚和N-甲基吡咯烷酮分离	EDWC	总投资成本总运营成本	Matlab & Aspen	多目标遗传算法	较常规萃取精馏工艺能耗减少26.29%、TAC减少24.15%	[52]
乙酸乙酯和甲醇分离	EDWC	TAC 二氧化碳排放量过程安全指数（PRI）	Matlab & Aspen	多目标粒子群算法	较四塔基础工艺TAC减少20.20%、二氧化碳排放量降低33.81%、安全指数提高18%	[53]
环己烷和环己烯分离	EDWC	TAC 二氧化碳排放量	Matlab & Aspen	多目标NSGA-Ⅱ算法	较常规萃取精馏流程可减少9.46%的TAC和17.25%的二氧化碳排放	[54]

体系	隔板类型	控制结构	MPC的预测模型	仿真软件	文献
乙醇/正丙醇/正丁醇分离	DWC	9输入9输出 MPC控制结构	稳态点近似线性化状态空间模型	Simulink & Aspen Dynamic	[94]
氯硅烷混合物分离	DWC	7输入7输出 MPC控制结构 4输入4输出 MPC-PI混合控制结构	稳态点近似线性化状态空间模型	Simulink & Aspen Dynamic	[36]
甲苯和2-甲氧基乙醇分离	EDWC	4输入4输出 MPC-PI混合控制结构	系统辨识获得的线性状态空间模型	Matlab & Simulink & Aspen Dynamic	[80]
苯和环己烯分离	EDWC	4输入4输出 MPC控制结构	系统辨识获得的线性ARX模型	Python & Matlab & Simulink & Aspen Dynamic	[78]
C3的选择性加氢和分离	RDWC	8输入8输出 MPC控制结构	稳态点近似线性化状态空间模型	Simulink & Aspen Dynamic	[95]
甲酸生产	RDWC	11输入11输出 MPC-PI混合控制结构	稳态点近似线性化状态空间模型	Simulink & Aspen Dynamic	[96]
乙酸己酯和丁醇酯交换反应	RDWC	8输入8输出 MPC-PI混合控制结构	系统辨识得到的线性状态空间模型	Matlab & Simulink & Aspen Custom Modeler	[97]