Methodology for a precise model-based characterization of liquid-phase adsorption processes

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Liquid-phase adsorption is an efficient and highly-selective cleaning and product recovery process especially for products with a low thermal resistance. The adsorption equilibrium, the basic adsorption and desorption kinetics as well as the adsorption dynamics have to be identified properly to design and to improve these adsorption processes. However, several factors are prohibitive when it comes to liquid-phase characterization, e.g., unknown or neglected process and measurement uncertainties and a limited amount of adsorbent or adsorptive especially in the early stage of research and development. Moreover, a precise analysis of the liquid-phase adsorption is challenging because of the complex interactions between adsorbate and adsorbent which is affected by various factors. To gain credible system insights, this research project is aiming for a methodology which combines dynamic experiments, innovative system identification concepts, and model-based experimental design. In detail, this project focuses on a precise model-based characterization of the liquid-phase adsorption of monosaccharides on zeolite BEA considering measurement and process uncertainties. To this end, the working group Scholl studies the adsorption process experimentally under static and dynamic operating conditions, analyzes the impact of process design parameters on the measurement uncertainties, and evaluates the experimental setups regarding their experimental expense and material demand. In parallel, the working group Schenkendorf develops algebraic identification routines for model and parameter identification using systems theory concepts and combines these algebraic identification routines with model-based experimental design. A representative sensitivity analysis ensures an optimal combination of the algebraic identification routines with conventional methods for parameter identification. Finally, model and parameter uncertainties are quantified and directly incorporated in the robust experimental design. Thus, the optimal experimental design provides effective and efficient experiments, i.e., highly informative data with minimum material input. In summary, this project leads to a better understanding of the complex interaction of adsorbate, adsorbent, and process design parameters and in a credible uncertainty quantification of adsorption kinetics and adsorption equilibrium - which we also apply for multi-component adsorption processes. Based on these results, adsorption processes can be designed and operated more reliably.