Nonlinear predictive control strategies of pH neutralization process based on neural networks

WANG Zhizhen; ZOU Zhiyun

doi:10.11949/j.issn.0438-1157.20181035

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Nonlinear predictive control strategies of pH neutralization process based on neural networks

Process system engineering | 更新时间：2026-07-24

- Nonlinear predictive control strategies of pH neutralization process based on neural networks
- CIESC Journal Vol. 70, Issue 2, Pages: 678-686(2019)
- 作者机构：
  
  军事科学院防化研究院，北京 102205
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20181035
  CLC： TP 273
- Received：12 September 2018，
  
  Revised：2018-10-18，
  
  Online First：16 June 2020，
  
  Published：05 February 2019
- 稿件说明：
移动端阅览
王志甄, 邹志云. 基于神经网络的pH中和过程非线性预测控制[J]. 化工学报, 2019, 70(2): 678-686

WANG Zhizhen, ZOU Zhiyun. Nonlinear predictive control strategies of pH neutralization process based on neural networks[J]. CIESC Journal, 2019, 70(2): 678-686
王志甄, 邹志云. 基于神经网络的pH中和过程非线性预测控制[J]. 化工学报, 2019, 70(2): 678-686 DOI： 10.11949/j.issn.0438-1157.20181035.

WANG Zhizhen, ZOU Zhiyun. Nonlinear predictive control strategies of pH neutralization process based on neural networks[J]. CIESC Journal, 2019, 70(2): 678-686 DOI： 10.11949/j.issn.0438-1157.20181035.

摘要

针对pH中和过程这一化工过程系统中的典型非线性对象特点，应用神经网络建模思想和模型预测控制方法，并结合Hammerstein模型特点，研究pH中和过程非线性系统的两种新型模型预测控制手段，分别建立基于神经网络的非线性预测控制系统整体求解策略和基于Hammerstein模型的两步法预测控制策略，并用MATLAB对其进行仿真。控制仿真结果表明，建立的神经网络预测控制策略和非线性Hammerstein模型预测控制均优于传统PID控制方法，具有良好的设定值跟踪效果和抗干扰控制响应，说明这两种控制策略是非线性过程的有效控制方法。

Abstract

To solve the control problems of nonlinear process systems

nonlinear model-predictive control algorithms are studied. pH neutralization process is a typical nonlinear process in chemical process systems. In view of the characteristic of pH neutralization process

the entire model of pH neutralization process system and the inverse model of static nonlinear block are established by neural networks. Then two novel nonlinear predictive control strategies are studied based on model-predictive control and Hammerstein model. The neural networks model predictive control (NNMPC)

which is a global solution strategy for nonlinear predictive control systems and nonlinear Hammerstein model predictive control (NLHMPC)

which is a strategy based on two steppes separation control are developed and simulated by MATLAB. Control simulation results show that the NNMPC and NLHMPC control strategies have better performances on set-point tracking and anti-interference control response than PID control. They can give effective control performance to nonlinear processes.

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references

钱锋 , 杜文莉 , 钟伟民 , 等 . 石油和化工行业智能优化制造若干问题及挑战 [J ] . 自动化学报 , 2017 , 43 ( 6 ): 893 - 901 .

Qian F , Du W L , Zhong W M , et al . Problems and challenges of smart optimization manufacturing in petrochemical industries [J ] . Acta Automatica Sinica , 2017 , 43 ( 6 ): 893 - 901 .

柴天佑 . 工业过程控制系统研究现状与发展方向 [J ] . 中国科学:信息科学 , 2016 , 46 ( 8 ): 1003 - 1015 .

Chai T Y . Industrial process control systems: research status and development direction [J ] . Scientia Sinica , 2016 , 46 ( 8 ): 1003 - 1015 .

Mota A S , Menezes M R , Schmitz J E , et al . Identification and online validation of a pH neutralization process using an adaptive network-based fuzzy inference system [J ] . Chemical Engineering Communications , 2016 , 203 ( 4 ): 516 - 526 .

Chen X , Xue A K , Peng D L , et al . Modeling of pH neutralization process using fuzzy recurrent neural network and DNA based NSGA-Ⅱ [J ] . Journal of the Franklin Institute , 2014 , 351 ( 7 ): 3847 - 3864 .

Zhang Y J , Jia Y , Chai T Y , et al . Data-driven PID controller and its application to pulp neutralization process [J ] . IEEE Transactions on Control Systems Technology , 2018 , 26 ( 3 ): 828 - 841 .

Vatankhah B , Farrokhi M . Nonlinear model-predictive control with disturbance rejection property using adaptive neural networks [J ] . Journal of the Franklin Institute , 2017 , 354 ( 13 ): 5201 - 5220 .

Singh P K , Bhanot S , Mohanta H K . Genetic optimization based adaptive fuzzy logic control of a pH neutralization process [J ] . International Journal of Control & Automation , 2014 , 7 ( 11 ): 233 - 248 .

李泽龙 , 杨春节 , 刘文辉 , 等 . 基于LSTM-RNN模型的铁水硅含量预测 [J ] . 化工学报 , 2018 , 69 ( 3 ): 992 - 997 .

Li Z L , Yang C J , Liu W H , et al . Research on hot metal Si-content prediction based on LSTM-RNN [J ] . CIESC Journal , 2018 , 69 ( 3 ): 992 - 997 .

Schmidhuber J . Deep learning in neural networks: an overview [J ] . Neural Networks , 2015 , 61 : 85 - 117 .

焦李成 , 杨淑媛 , 刘芳 , 等 . 神经网络七十年: 回顾与展望 [J ] . 计算机学报 , 2016 , 39 ( 8 ): 1697 - 1716 .

Jiao L C , Yang S Y , Liu F , et al . Seventy years beyond neural networks: retrospect and prospect [J ] . Chinese Journal of Computers , 2016 , 39 ( 8 ): 1697 - 1716 .

王康成 , 尚超 , 柯文思 , 等 . 化工过程深度神经网络软测量的结构与参数自动调整方法 [J ] . 化工学报 , 2018 , 69 ( 3 ): 900 - 906 .

Wang K C , Shang C , Ke W S , et al . Automatic structure and parameters tuning method for deep neural network soft sensor in chemical industries [J ] . CIESC Journal , 2018 , 69 ( 3 ): 900 - 906 .

席裕庚 . 预测控制 [M ] . 北京 : 国防工业出版社 , 2013 .

Xi Y G . Predictive Control [M ] . Beijing : National Defense Industry Press , 2013 .

Mayne D Q . Model predictive control: recent developments and future promise [J ] . Automatica , 2014 , 50 ( 10 ): 2967 - 2986 .

Anang , Sutanto H , Edi L . Model predictive control design and performance analysis of a pasteurization process plant [C ] // International Conference on Instrumentation, Control and Automation. Bandung , Indonesia : IEEE , 2017 : 81 - 87 .

Qin S J , Badgwell T A . A survey of industrial model predictive control technology [J ] . Control Engineering Practice , 2003 , 11 ( 7 ): 733 - 764 .

Allgöwer F , Findeisen R , Nagy Z K . Nonlinear model predictive control: from theory to application [J ] . Journal of the Chinese Institute of Chemical Engineers , 2004 , 35 ( 3 ): 299 - 315 .

席裕庚 , 李德伟 , 林姝 . 模型预测控制——现状与挑战 [J ] . 自动化学报 , 2013 , 39 ( 3 ): 222 - 236 .

Xi Y G , Li D W , Lin S . Model predictive control—status and challenges [J ] . Acta Automatica Sinica , 2013 , 39 ( 3 ): 222 - 236 .

何德峰 , 丁宝苍 , 于树友 . 非线性系统模型预测控制若干基本特点与主题回顾 [J ] . 控制理论与应用 , 2013 , 30 ( 3 ): 273 - 287 .

He D F , Ding B C , Yu S Y . Review of fundamental properties and topics of model predictive control for nonlinear systems [J ] . Control Theory & Applications , 2013 , 30 ( 3 ): 273 - 287 .

孔小兵 , 刘向杰 . 基于输入输出线性化的连续系统非线性模型预测控制 [J ] . 控制理论与应用 , 2012 , 29 ( 2 ): 217 - 224 .

Kong X B , Liu X J . Continuous-time nonlinear model predictive control with input/output linearization [J ] . Control Theory & Applications , 2012 , 29 ( 2 ): 217 - 224 .

盖俊峰 , 赵国荣 , 周大旺 . 一种基于线性近似的非线性系统模型预测控制方法 [J ] . 航天控制 , 2015 , 33 ( 5 ): 3 - 7 .

Gai J F , Zhao G R , Zhou D W . Nonlinear model predictive control scheme based on linearization approximation [J ] . Aerospace Control , 2015 , 33 ( 5 ): 3 - 7 .

Ye Q , Lou X Y , Sheng L . Generalized predictive control of a class of MIMO models via a projection neural network [J ] . Neurocomputing , 2017 , 234 : 192 - 197 .

Zhao J M . NN-adaptive predictive control for a class of discrete-time nonlinear systems with input-delay [J ] . Neurocomputing , 2016 , 173 : 1832 - 1838 .

Killian M , Mayer B , Schirrer A , et al . Cooperative fuzzy model predictive control [J ] . Elektrotechnik Und Informationstechnik , 2015 , 132 ( 8 ): 474 - 480 .

王书斌 , 单胜男 , 罗雄麟 . 基于T-S模糊模型与粒子群优化的非线性预测控制 [J ] . 化工学报 , 2012 , 63 ( S1 ): 176 - 187 .

Wang S B , Shan S N , Luo X L . Nonlinear predictive control based on T-S fuzzy model and particle-swarm optimization [J ] . CIESC Journal , 2012 , 63 (S 1 ) : 176 - 187 .

Yusuf Z , Wahab N A , Abusam A . Neural network-based model predictive control with CPSOGSA for SMBR filtration [J ] . International Journal of Electrical & Computer Engineering , 2017 , 7 ( 3 ): 1538 - 1545 .

Kittisupakorn P , Somsong P , Hussain M A , et al . Improving of crystal size distribution control based on neural network-based hybrid model for purified terephthalic acid batch crystallizer [J ] . Engineering Journal , 2017 , 21 ( 7 ): 319 - 331 .

Kumar D , Budman H . Robust nonlinear MPC based on Volterra series and polynomial chaos expansions [J ] . Journal of Process Control , 2014 , 24 ( 1 ): 304 - 317 .

Figueroa J L , Biagiola S I , Alvarez M P , et al . Robust model predictive control of a Wiener-like system [J ] . Journal of the Franklin Institute , 2013 , 350 ( 3 ): 556 - 574 .

Ławryńczuk M . Nonlinear predictive control for Hammerstein-Wiener systems [J ] . ISA Transactions , 2015 , 55 ( 1 ): 49 - 62 .

Alamir M . Contraction-based nonlinear model predictive control formulation without stability-related terminal constraints [J ] . Automatica , 2017 , 75 : 288 - 292 .

Grüne L , Stieler M . Asymptotic stability and transient optimality of economic MPC without terminal conditions [J ] . Journal of Process Control , 2014 , 24 ( 8 ): 1187 - 1196 .

邹志云 , 郭宇晴 , 王志甄 , 等 . 非线性Hammerstein模型预测控制策略及其在pH中和过程中的应用 [J ] . 化工学报 , 2012 , 63 ( 12 ): 3965 - 3970 .

Zou Z Y , Guo Y Q , Wang Z Z , et al . Nonlinear Hammerstein model predictive control strategy and its application to pH neutralization process [J ] . CIESC Journal , 2012 , 63 ( 12 ): 3965 - 3970 .

周洪煜 , 梁东义 , 周松杰 . 废水中和过程的RBF神经网络预测控制 [J ] . 控制工程 , 2014 , 21 ( 1 ): 79 - 83 .

Zhou H Y , Liang D Y , Zhou S J . RBFNN predictive control of wastewater neutralization process [J ] . Control Engineering of China , 2014 , 21 ( 1 ): 79 - 83 .

安爱民 , 刘云利 , 张浩琛 , 等 . 微生物燃料电池的动态性能分析及其神经网络预测控制 [J ] . 化工学报 , 2017 , 68 ( 3 ): 1090 - 1098 .

An A M , Liu Y L , Zhang H C , et al . Dynamic performance analysis and neural network predictive control of microbial fuel cell [J ] . CIESC Journal , 2017 , 68 ( 3 ): 1090 - 1098 .

李明河 , 周磊 , 王健 . 基于LM算法的溶解氧神经网络预测控制 [J ] . 农业机械学报 , 2016 , 47 ( 6 ): 297 - 302 .

Li M H , Zhou L , Wang J . Neural network predictive control for dissolved oxygen based on Levenberg-Marquardt algorithm [J ] . Transactions of the Chinese Society for Agricultural Machinery , 2016 , 47 ( 6 ): 297 - 302 .

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