Flexible on-line modeling and control of pH in waste neutralization reactors

The control of pH in waste neutralization processes presents a challenging highly nonlinear and time-varying problem in which the reactor also suffers from inaccessible state information. The ability to characterize the changing dynamics of such reactors is essential to the success of advanced control schemes for these applications. In this work, flexible on-line modeling of a pH reactor simulating nonstationary behavior was studied. This entailed a comparison of the most popular connectionist learning algorithm, the "Widrow-Hoff delta rule", with a classical tool in adaptive identification and control, recursive least squares (RLS). The modeling was pursued within the framework of neural networks using the ADALINE neural network. Further, two heuristically defined first-principles-based transforms were investigated for providing "general globally linearizing" information to the ADALINE. The comparisons of the learning algorithms for different neural network information vectors has led to a critical understanding of the flexibility of each algorithm for on-line learning of the diverse process gain characteristics encountered in pH reactors.