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Two substantially different methods have become popular in building fast computing catalyst models: physico-chemical approaches focusing on dimensionality reduction and machine learning approaches. Data driven models are known to be very fast computing and to achieve high accuracy but they can lack of extrapolation capability. Physico-chemical models are usually slower and less accurate but superior regarding robustness. The robustness can even be reinforced by implementing an extended Kalman filter, which enables the model to adapt its states based on actual sensor values, even if the sensors are drifting. The present study proposes a combination of both approaches into one hybrid model, keeping the robustness of the physico-chemical model in edge cases while also achieving the accuracy of the data based model in well-known regimes. The output of the hybrid model is controlled by an autoencoder, utilizing methods well known from the field of anomaly detection.