Modelling of Two Continuous Stirred Tank Heat Exchangers in Series Using Neural Network

  • Authors

    • Normah Abdullah
    • Muhammad Harith Anuar
    • Zulkifli Mohd Nopiah
    • Mohd Marzuki Mustafa
    • Azah Mohamed
    • Mohd. Zaki Nuawi
    • Abu Bakar Mohamad
    2019-01-30
    https://doi.org/10.14419/ijet.v8i1.2.24910
  • CSTHE, data generation, heat exchanger, modelling, neural network

  • Abstract

    This paper presents the application of artificial neural networks (ANN) in modeling of two continuous stirred tank heat echangers in series (2CSTHEs), which is a complex non-linear process. Non-linear models of the 2CSTHEs system were developed using ANN because of  ANN ability to model complex non-linear processes without requiring any explicit knowledge about input-output relationship. The ANN architecture  is based on the multilayer feed forward network and it is trained using the back-propagation algorithms. Three types of back-propagation algorithms are used in the study, namely, Levenberg-Marquardt, BFGS quasi-Newton, and conjugate gradient with Polak-Ribiére updates. Two dynamic models of the system are developed: ANN model for CSTHE 1and 2. Results from the study showed that the 2CSTHEs model trained using Levenberg-Marquardt algorithm produced the best predictive performance of the system behaviour. The results confirmed that ANN can be used in the modeling of the heat exchanger 2CSTHEs, and the model obtained can predict the outputs of the system process with very high accuracy. This proves that ANN modelling method can produce accurate system models that can simulate and predict the behaviour of complex non-linear processes.

     

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  • How to Cite

    Abdullah, N., Harith Anuar, M., Mohd Nopiah, Z., Marzuki Mustafa, M., Mohamed, A., Zaki Nuawi, M., & Bakar Mohamad, A. (2019). Modelling of Two Continuous Stirred Tank Heat Exchangers in Series Using Neural Network. International Journal of Engineering & Technology, 8(1.2), 250-255. https://doi.org/10.14419/ijet.v8i1.2.24910

    Received date: 2018-12-28

    Accepted date: 2018-12-28

    Published date: 2019-01-30