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[en] Highlights: • The NPK method is extended to more than two variables. • It can derive the kinetic model from any combination of experiments. • The algorithm is validated by applying it to simulated datasets with noise. - Abstract: A novel algorithm to identify the full kinetic model of solid state reactions according to the General Kinetic Equation is presented. It is a higher-order generalization of the Non-Parametric Kinetics method (NPK-method) and allows for the simultaneous identification of the conversion, temperature and pressure dependency from any combination of measurements. As a model-free identification method, it does not rely on a-priori assumptions about the kinetic model. The result vectors can be used to identify the kinetic parameters by means of model fitting for each variable independently. The steps of the algorithm are described and its effectiveness is demonstrated by applying it to simulated datasets. The kinetic parameters could be recovered very accurately from the test data, also in the presence of noise. Overall the higher order NPK-method is a very promising approach to derive kinetic models from experimental data with a minimum of a-priori assumptions about the reaction.
[en] Highlights: • An automated search for reaction systems suitable for thermochemical energy storage was performed. • Algorithm to build reaction systems for thermochemical energy storage is presented. • Close to 1000 possible reaction systems for 5 different reactive gases were found. • The VIENNA TCES-database for thermochemical energy storage materials is presented. - Abstract: Thermochemical energy storage (TCES) is considered as an emerging green technology for increased energy utilization efficiency, thereby achieving a reduction of greenhouse gases. Various reaction systems based on different substance classes (e.g. hydrates, hydroxides, oxides) were suggested and investigated so far. Nevertheless, the number of know reactions which are suitable is still limited, as the main focus concentrates on the investigation of a handful known substances, their further improvement or applicability. To find novel promising candidates for thermochemical energy storage and also to allow for a broader view on the topic, this work present a systematic search approach for thermochemical storage reactions based on chemical databases. A mathematical search algorithm identifies potential reactions categorized by the reactant necessary for the reaction cycle and ranked by storage density. These candidates are listed in the online available VIENNA TCES-database, combined with experimental results, assessing the suitability of these reactions regarding of e.g. decomposition/recombination temperature, reversibility, cycle stability, etc.