Dynamic control of reactive pressure-swing distillation process for separating tetrahydrofuran/methanol/water

IF 2.8 4区生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Journal of chemical technology and biotechnology Pub Date : 2025-02-07 DOI:10.1002/jctb.7825

Kun Yang, Shangkun Wang, Zeng Li, Xiaojing Liu, Fangkun Zhang, Baoming Shan, Peizhe Cui, Qilei Xu

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引用次数: 0

Abstract

BACKGROUND

Reactive pressure-swing distillation (RPSD) is an innovative and promising technology for the separation of azeotropic systems, offering high integration and efficiency. However, the dynamic control of RPSD processes presents significant challenges due to complex coupling and nonlinearity. This paper investigates the practical control structures for the RPSD processes, and the dynamic control structure was first designed for the RPSD system for separating tetrahydrofuran/methanol/water.

RESULT

Three practical control structures based on PID control were developed for existing separation processes, both with and without heat integration. The results demonstrated that all designed control structures effectively withstand disturbances of ±20% in feed flow rate and composition, exhibiting strong anti-interference capabilities. Furthermore, an advanced intelligent control strategy was designed, integrating PID control with Back Propagation Neural Networks (BPNN) to enhance the performance of the product composition controllers.

CONCLUSION

The results indicated that the BPNN can accurately predict temperature setpoints using easily measurable variables, facilitating stable control of product concentrations without the necessity for direct composition measurements. This proposed control strategy offers an efficient and reliable alternative for the control of RPSD processes. © 2025 Society of Chemical Industry (SCI).

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四氢呋喃/甲醇/水分离反应变压精馏过程的动态控制

反应变压蒸馏（RPSD）是一种具有高集成度和高效性的分离共沸体系的创新技术。然而，由于复杂的耦合和非线性，RPSD过程的动态控制面临着巨大的挑战。本文研究了RPSD过程的实用控制结构，并首次设计了四氢呋喃/甲醇/水分离RPSD系统的动态控制结构。结果针对现有分离过程，开发了三种基于PID控制的实用控制结构，包括热集成和不热集成。结果表明，所有设计的控制结构都能有效抵御±20%进料流量和成分的干扰，具有较强的抗干扰能力。在此基础上，设计了一种先进的智能控制策略，将PID控制与bp神经网络（Back Propagation Neural Networks， BPNN）相结合，提高了产品组合控制器的性能。结论BPNN可以利用易于测量的变量准确预测温度设定值，无需直接测量成分，即可稳定控制产品浓度。提出的控制策略为RPSD过程的控制提供了一种高效可靠的替代方案。©2025化学工业学会（SCI）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of chemical technology and biotechnology 工程技术-工程：化工

CiteScore

7.00

自引率

5.90%

发文量

268

审稿时长

1.7 months

期刊介绍： Journal of Chemical Technology and Biotechnology(JCTB) is an international, inter-disciplinary peer-reviewed journal concerned with the application of scientific discoveries and advancements in chemical and biological technology that aim towards economically and environmentally sustainable industrial processes.