Modelling of a fermentation process using a time-delay neural network

The use of time-delay neural networks for the modelling of a fermentation process is addressed in this paper. The networks are used for the estimation of state variables, such as the secondary product concentration and the residual nitrogen. The networks were trained with data generated from an industrial fermentation process. The networks were tested as one-step ahead predictors and as models. The proposed approach provided reliable results and demonstrated that state estimation of the fermentation process can be achieved using neural networks.<
>