Development of a Hybrid Model for Industrial Ethylene Oxide Reactor

Luo, Na; Du, Wenli; Ye, Zhencheng; Qian, Feng

doi:10.1021/ie202619d

Cited by 18 publications

(10 citation statements)

References 16 publications

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“…[ 6 ] Luo et al integrated reaction kinetics, a reactor model, and a catalyst deactivation model of an industrial EO reactor and compared the results with an actual EO plant. [ 7 ] Nawaz et al used Aspen Custom Modeler to optimize several design variables, such as reactor temperature and inlet compositions. [ 8 ] Peschel et al considered interactions between reaction concepts and the entire EO process, and modeled the physical and chemical principles.…”

Section: Introductionmentioning

confidence: 99%

Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

Iwama

Kaneko

2021

J Adv Manuf & Process

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In process design, the values of design variables X for equipment and operating conditions should be appropriately selected for entire processes, including all unit operations, such as reactors and distillation columns, to consider effects between unit operations. However, as the number of X increases, many more simulations are required to search for the optimal X values. Furthermore, multiple objective variables Y, such as yields, make the optimization problem difficult. We propose a process design method based on adaptive design of experiments and Bayesian optimization. Selection of X values that satisfy the target values of multiple Y variables are searched, and simulations for the selected X values are then repeated. Therefore, the X will be selected by a small number of simulations. We verify the effectiveness of this method by simulating an ethylene oxide production plant.

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Section: Introductionmentioning

confidence: 99%

Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

Iwama

Kaneko

2021

J Adv Manuf & Process

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“…Rahimpour et al modeled catalyst deactivation using artificial neural networks to determine the optimal amount of ethylene dichloride 6 . Luo et al integrated reaction kinetics, a reactor model, and a catalyst deactivation model of an industrial ethylene oxide reactor and compared the results with an actual EO plant 7 . Nawaz et al used Aspen Custom Modeler to optimize several design variables, such as reactor temperature and inlet compositions 8 .…”

Section: Introductionmentioning

confidence: 99%

Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

Iwama

Kaneko

2020

Preprint

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In process design, the values of design variables X for equipment and operating conditions should be optimized for entire processes, including all unit operations, such as reactors and distillation columns, to consider effects between unit operations. However, as the number of X increases, many more simulations are required to search for the optimal X values. Furthermore, multiple objective variables Y, such as yields, make the optimization problem difficult. We propose a process design method based on adaptive design of experiments and Bayesian optimization. Optimization of X values that satisfy target values of multiple Y variables are searched, and simulations for the optimized X values are then repeated. Therefore, X will be optimized by a small number of simulations. We verify the effectiveness of this method by simulating an ethylene oxide production plant.

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“…First, experimental results are obtained under limited conditions in the laboratory batch reactor with a volume in liters, whereas the process of industrial BPA synthesis is carried out under continuous operation conditions, and the reactor volume is generally cubic meters. Therefore, the nature of mixing, mass transfer, and heat transfer in industrial fixed‐bed reactor is significantly different from that in laboratory batch reactor due to the amplification effects and different operation conditions of the reactor, making it very difficult to extrapolate the experimental kinetic models to a wide range of operation conditions in the industrial process . Because of this, research on mechanism modeling of industrial BPA production is unavailable so far.…”

Section: Introductionmentioning

confidence: 99%

Hybrid soft sensor modeling for bisphenol‐A synthesis reaction process

Cang

Yang

2019

Asia-Pacific J Chem Eng

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To online estimate the concentrations of key components at the outlet of bisphenol‐A (BPA) synthesis reactor, a hybrid modeling approach is proposed in this paper. First, the simplified mechanism model for the synthesis of BPA in an industrial fixed‐bed reactor is deduced on the basis of mass and energy balance. Second, considering that the initial concentrations of reactants at the inlet of BPA synthesis reactor cannot be measured in real‐time, the modified stochastic gradient boosting‐Gaussian process regression approach previously proposed by the authors is employed to online estimate the initial concentrations of reactants. Furthermore, modified stochastic gradient boosting‐Gaussian process regression method is also adopted to model the nonlinear influence of the reaction factors ( i.e., initial concentrations of reactants and inlet temperature) on the reaction rate constants in the simplified mechanism model. Finally, the hybrid modeling approach for BPA synthesis process is achieved by integrating the simplified mechanism model with the estimation models of initial concentrations and reaction rate constants. The feasibility and validity of the proposed hybrid model is verified through an industrial BPA synthesis process. On the basis of the hybrid model, the sensitivity analyses of the initial concentrations and inlet temperature are conducted, which can provide theoretical guidance for the operation optimization study in future work.

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Development of a Hybrid Model for Industrial Ethylene Oxide Reactor

Cited by 18 publications

References 16 publications

Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

Hybrid soft sensor modeling for bisphenol‐A synthesis reaction process

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