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Preliminary investigations into the potential application of static feed forward neural networks in the dynamic modelling of pH in complex, time-varying systems have been carried out. To assist in network training and testing, a simplified, ‘global first principles’ (FP) model of the pH of such systems was developed, and used successfully to simulate input-output data. Neural networks with input information vectors enhanced by the introduction of auxiliary variables derived from acid-base principles were trained and tested on this data, using both Levenberg-Marquardt (L-M) and heuristic training algorithms. Both algorithms produced good predictions, but the heuristic algorithm required data pre-treatment to minimize its error. However, it trained much faster than the standard, L-M algorithm. |
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