Modeling component concentrations of sodium aluminate solution via hammerstein recurrent neural networks

Wei Wang, Tianyou Chai, Wen Yu, Hong Wang, Chunyi Su

    Research output: Contribution to journalArticlepeer-review


    The component concentrations of sodium aluminate solution are important indices in alumina processing. At present, they are obtained by laboratory titration on samples taken from the production process. Due to the delays in taking and testing samples, they cannot be used for real-time control and optimization. Existing online measurements are not adopted because of the characteristics of the sodium aluminate solution such as high viscosity and the ease of precipitation which leads to pipeline blocking and decreased precision. In this paper, a new modeling method is proposed to measure the component concentrations online using the measurements of conductivity and temperature. The method combines the partial least squares (PLS) technique and the Hammerstein recurrent neural networks (HRNN), where a stable learning algorithm with theoretical analysis is given for the HRNN model. For this PLS-based HRNN, the PLS technique is used to solve the high dimensional and correlated data. Meanwhile, the HRNN technique is used to fit the nonlinear and dynamic characters of the process. An industrial experimental study on a sodium aluminate solution is described. The experiment results show that the proposed method is sufficient to warrant further evaluation in industrial scale experiments. © 2011 IEEE.
    Original languageEnglish
    Article number5948397
    Pages (from-to)971-982
    Number of pages11
    JournalIEEE Transactions on Control Systems Technology
    Issue number4
    Publication statusPublished - 2012


    • Hammerstein model
    • neural networks
    • partial least squares (PLS)
    • sodium aluminate solution
    • soft sensing


    Dive into the research topics of 'Modeling component concentrations of sodium aluminate solution via hammerstein recurrent neural networks'. Together they form a unique fingerprint.

    Cite this