This study presents a new approach for the recycling of bilayered PET waste in an efficient, continuous process with a depolymerization degree >97%. The complex PET waste was converted by chemolysis into its monomers ethylene glycol (EG) and the corresponding salt of terephthalic acid (TA) in a twin-screw extruder (TSE). Via this method, the starting materials for PET production were recovered, and highly contaminated PET waste and PET composite materials were transformed into valuable starting materials. The PE layer of the composite PET/PE material remained inert under depolymerization conditions and could be separated by filtration. An increase in the rotational speed by 200 rpm in the TSE reduced the residence time, but the degree of depolymerization was not affected in a proportional manner. Thus, the results indicate that a shorter residence time can be compensated with intensified mechanical agitation due to higher rotational speeds to obtain a similar degree of depolymerization. These results support the potential of this recycling concept to substantially contribute to the implementation of a circular PET economy.

In the field of chemical engineering, mathematical models have been proven to be an indispensable tool for process analysis, process design, and condition monitoring. To gain the most benefit from model-based approaches, the implemented mathematical models have to be based on sound principles, and they need to be calibrated to the process under study with suitable model parameter estimates. Often, the model parameters identified by experimental data, however, pose severe uncertainties leading to incorrect or biased inferences. This applies in particular in the field of pharmaceutical manufacturing, where usually the measurement data are limited in quantity and quality when analyzing novel active pharmaceutical ingredients. Optimally designed experiments, in turn, aim to increase the quality of the gathered data in the most efficient way. Any improvement in data quality results in more precise parameter estimates and more reliable model candidates. The applied methods for parameter sensitivity analyses and design criteria are crucial for the effectiveness of the optimal experimental design. In this work, different design measures based on global parameter sensitivities are critically compared with state-of-the-art concepts that follow simplifying linearization principles. The efficient implementation of the proposed sensitivity measures is explicitly addressed to be applicable to complex chemical engineering problems of practical relevance. As a case study, the homogeneous synthesis of 3,4-dihydro-1H-1-benzazepine-2,5-dione, a scaffold for the preparation of various protein kinase inhibitors, is analyzed followed by a more complex model of biochemical reactions. In both studies, the model-based optimal experimental design benefits from global parameter sensitivities combined with proper design measures.

This paper presents investigations into the chronoamperometric experiments performed on polybenzimidazole/phosphoric acid based high temperature polymer electrolyte membrane fuel cells. Electrolyte loadings in the membrane electrode assembly are varied to realise three different degrees of flooding of the electrode. Steady state as well as dynamic measurements at three different temperatures (160, 120 and 80 °C) is made via chronoamperometry on these cells. The transient current behaviour resulting from a voltage step change from open circuit voltage (∼1 V) to low voltages (0.1 V) are recorded and analysed. Analysis using the Cottrell plot depicts the underlying mass transport processes in the three different electrodes, catalyst utilisation as well as a better description of the three phase boundary reaction zone. An extension of the Cottrell equation is deduced to describe a regular commercial fuel cell behaviour.