Artificial neural networks and data fusion as a biomass virtual sensor

The ability of artificial neural networks (ANN) to learn from experience rather than from mechanistic descriptions is making them the preferred choice to model processes with intricate variable interrelations. We apply data fusion methods (one of which is ANN) to provide estimations of biomass in a fermentation process. The readings of biomass must be periodic, of the desired frequency and reliable to a 5% error. A desired feature is that the measurement method must be robust to sensor perturbations and failures. The robustness of the presented estimator system has been tested with simulated noisy inputs and with sensor failures and a mean average error of near 5% has been obtained. A new technique is presented as a data fusion method. The technique is tested on real process data. Simulated tests are applied to evaluate performance and robustness. We demonstrated that an ANN is able to learn the interrelations between certain inputs and biomass for a fermentation process.