Diagnosis of undesired scenarios in hydrogen production by photo-fermentation.

This study presents the use of a machine learning method from the artificial intelligence area, such as the support vector machines, applied to the construction of data-based classification models for diagnosing undesired scenarios in the hydrogen production process by photo-fermentation, which was carried out by an immobilized photo-bacteria consortium. The diagnosis models were constructed with data obtained from simulations run with a mechanistic model of the process and assessed on both modelled and experimental batches. The results revealed a 100% diagnosis performance in those batches where light intensity was below and above an optimum operation range. Nevertheless, 55% diagnosis performance was obtained in modelled batches where pH was away from its optimum operation range, showing that diagnosis model predictions during the first observations of those batches were classified as normal operation and revealing diagnosis delay in pH oscillations. In general, results demonstrate the reliability of classification models to be used in future applications such as the on-line process monitoring to detect and diagnose undesired operating conditions and take corrective actions on time to maintain high hydrogen productivities.