Supported catalysts with a nominal Mo surface density of 0.15 and 1.5 Mo atoms nm−2 were synthesized by impregnation of alumina, silica, and alumina–silica supports with silica content between 1 and 70 wt %. They were tested for their activity and selectivity in the metathesis of ethylene and trans‐2‐butene to propene between 343 and 603 K at 125 kPa. The catalysts were characterized by UV/Vis, Raman, and IR spectroscopy, XRD and H2 temperature‐programmed reduction for elucidating the distribution, degree of polymerization, reducibility, and acidity of MoOx species. We established that Brønsted acidity of highly dispersed tetrahedral and polymerized octahedral MoOx species is required to ensure high metathesis activity. The acidic character of these species is influenced by their structure and support. Tetrahedral MoOx species with Brønsted acidic character are only formed on supports possessing such acidity, whereas Brønsted acidic octahedral MoOx is also created on supports without such acidic sites.

Mechanistic modeling has been repeatedly successfully applied in process development and control of protein chromatography. For each combination of adsorbate and adsorbent, the mechanistic models have to be calibrated. Some of the model parameters, such as system characteristics, can be determined reliably by applying well-established experimental methods, whereas others cannot be measured directly. In common practice of protein chromatography modeling, these parameters are identified by applying time-consuming methods such as frontal analysis combined with gradient experiments, curve-fitting, or combined Yamamoto approach. For new components in the chromatographic system, these traditional calibration approaches require to be conducted repeatedly. In the presented work, a novel method for the calibration of mechanistic models based on artificial neural network (ANN) modeling was applied. An in silico screening of possible model parameter combinations was performed to generate learning material for the ANN model. Once the ANN model was trained to recognize chromatograms and to respond with the corresponding model parameter set, it was used to calibrate the mechanistic model from measured chromatograms. The ANN model's capability of parameter estimation was tested by predicting gradient elution chromatograms. The time-consuming model parameter estimation process itself could be reduced down to milliseconds. The functionality of the method was successfully demonstrated in a study with the calibration of the transport-dispersive model (TDM) and the stoichiometric displacement model (SDM) for a protein mixture.

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In protein chromatography, process variations, such as aging of column or process errors, can result in deviations of the product and impurity levels. Consequently, the process performance described by purity, yield, or production rate may decrease. Based on visual inspection of the UV signal, it is hard to identify the source of the error and almost unfeasible to determine the quantity of deviation. The problem becomes even more pronounced, if multiple root causes of the deviation are interconnected and lead to an observable deviation. In the presented work, a novel method based on the combination of mechanistic chromatography models and the artificial neural networks is suggested to solve this problem. In a case study using a model protein mixture, the determination of deviations in column capacity and elution gradient length was shown. Maximal errors of 1.5% and 4.90% for the prediction of deviation in column capacity and elution gradient length respectively demonstrated the capability of this method for root cause investigation.

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