CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 496-507.DOI: 10.11949/j.issn.0438-1157.20181082

• Process system engineering • Previous Articles     Next Articles

Hybrid modeling and optimization of acetylene hydrogenation process

Zhencheng YE(),Huanlan ZHOU,Debao RAO   

  1. Key Laboratory of Chemical Process Control and Optimization Technology, Ministry of Education, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
  • Received:2018-09-26 Revised:2018-10-23 Online:2019-02-05 Published:2019-02-05
  • Contact: Zhencheng YE

乙炔加氢反应过程混合建模与优化

叶贞成(),周换兰,饶德宝   

  1. 华东理工大学信息科学与工程学院,化工过程先进控制和优化技术教育部重点实验室,上海 200237
  • 通讯作者: 叶贞成
  • 作者简介:叶贞成(1977—),男,副研究员,<email>yzc@ecust.edu.cn</email>
  • 基金资助:
    国家自然科学基金重点项目(61533003);国家自然科学基金青年项目(21506050);中央高校基本科研业务费重点科研基地创新基金(222201717006,22221817014)

Abstract:

The mathematical model of the acetylene hydrogenation reactor established by traditional single modeling method does not meet the needs of industrial practical applications in predictive performance. This paper proposes a mechanism and neural network nesting modeling method, which fully utilizes the mechanism model. It makes full use of mass and energy balance information in mechanism model to reduce the degree of constraint violation of the neural network model, which can describe the process characteristics of industrial reactor well. The optimization problem which targets the operational profits as the objective function is studied basing on the hybrid model. The main decision variables include several key parameters, such as the reactor feed hydrogen-alkyne ratio, the feed temperature, and the two-stage reactor operating cycle and many more. For the long-term operation of the reactor, processing capacity of the reactor will decrease due to the decreased catalyst activity, and an improved optimizing strategy is proposed by adjusting the hydrogen-alkyne ratio as well as the reaction temperature simultaneously. The sequence method is used to discretize the operating cycle of the reactor. The two-stage difference algorithm is improved by introducing differential mutation strategy and potential solution alternative strategy. Then the optimization problem is solved by combining the incremental coding method with the improved two-stage difference algorithm. And the results confirm the effectiveness. Furthermore the optimal operating cycle and operating strategy of the reactor are given.

Key words: acetylene hydrogenation, dynamic model, control, algorithm, optimization

摘要:

针对传统单一建模方法所构建的乙炔加氢反应器数学模型存在预测性能无法满足工业实际应用需求的问题,提出了一种机理与神经网络嵌套的建模方法,充分利用机理模型包含的能质约束信息降低神经网络模型的约束违反度,得到了能够良好描述实际工业乙炔加氢反应过程特性的混合模型。基于反应器混合模型,研究了以运行效益为目标函数的优化问题。主要决策变量包括:一段反应器进料中氢气与乙炔的摩尔比(R H/A)、进料温度和反应器运行周期等几个关键参数。针对反应器长期运行后,催化剂活性降低造成的处理能力下降的问题,提出了反应温度补偿机制和R H/A并行调节的运行优化策略,并采用序列法对反应器运行周期进行离散化处理。通过引入差异化变异策略、潜在解替代策略对两阶段差分算法进行改进,采用增量式编码法结合改进两阶段差分算法,对优化问题进行求解。结果证实了优化策略与改进算法的有效性,并据此确定了反应器最佳运行方案。

关键词: 乙炔加氢, 动力学模型, 控制, 算法, 优化

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