Rapid estimation of kinetic parameters with high precision is facilitated by automation combined with online Model-Based Design of Experiments.

Rapid and precise estimation of kinetic parameters is facilitated by transient flow experiments designed using model-based design of experiments.

Automated model identification platforms were recently employed to identify parametric models online in the course of unmanned experimental campaigns. The algorithms controlling these platforms include two computational elements: i) a tool for parameter estimation; ii) a tool for model-based experimental design. Both tools require the solution of complex optimisation problems and their effective outcome relies on their respective objective functions being wellconditioned. Ill-conditioned objective functions may arise when the model is characterised by a weak parametrisation, i.e. the model parameters are practically non-identifiable and/or extremely correlated. In this work, a robust reparametrisation technique is proposed and tested both in-silico and in an automated model identification platform. The benefit of reparametrisation is demonstrated on a case study for the identification of a kinetic model of catalytic esterification of benzoic acid with ethanol in a flow microreactor.

An autonomous reactor platform was developed to rapidly identify a kinetic model for the esterification of benzoic acid and ethanol with the heterogeneous Amberlyst-15 catalyst. A five-step methodology for kinetic studies was employed to systematically reduce the number of experiments required to identify a practical kinetic model. This included i) initial screening using traditional factorial designed steady-state experiments, ii) proposing and testing candidate kinetic models, iii) performing an identifiability analysis to reject models whose model parameters cannot be estimated for a given experimental budget, iv) performing online Model-Based Design of Experiments (MBDoE) for model discrimination to identify the best model from a list of candidates and v) performing online MBDoE for improving parameter precision for the chosen model. This methodology, combined with the reactor platform which conducted all kinetic experiments unattended, reduces the number of experiments and time required to identify kinetic models, significantly increasing lab productivity.

An automated flow micropacked bed catalytic reactor platform was developed to conduct pre-planned experiments for rapid screening of kinetic models. The microreactor was fabricated using photolithography and deep reactive ion etching of a silicon wafer, with a reaction channel width and depth of 2 mm and 420 μm respectively. It was packed with ca. 10 mg of 5 wt. % Pd/Al 2 O 3 catalyst to perform methane combustion, which was the selected reaction to test the developed platform. The experimental system was monitored and controlled by LabVIEW to which Python scripts for online design of experiments and data analysis were integrated. Within each experimental campaign, the platform automatically adjusted the experimental conditions, and the analysis of the product stream was conducted by online gas chromatography. The experimental platform demonstrated the capability of identifying the most probable kinetic models amidst potential models within two days.