Estimating the Chemical Reaction Kinetics of <i>p</i>-Xylene Oxidation Using Artificial Neural Network without Traditional Kinetic Equations

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  • Dong Yaming
    Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology
  • Yan Xuefeng
    Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology

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  • Estimating the Chemical Reaction Kinetics of p-Xylene Oxidation Using Artificial Neural Network without Traditional Kinetic Equations

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Abstract

An alternative method for the estimation of the chemical reaction rate without the use of traditional kinetic model equations is investigated in this study. The proposed method based on artificial neural network (ANN) is an efficient and accurate means to predict the chemical reaction rate because of the complex and unknown reaction kinetics of the p-xylene (PX) oxidation process. A mechanism model of PX oxidation is also integrated in the proposed neural network reaction rate model to predict the concentrations of target materials in the chemical process. The results show that the neural network model can accurately predict the chemical reaction rate of PX oxidation and that the proposed process hybrid model exhibits better performance than the pure mechanism and pure ANN models.

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