Title:
On the importance of robust and accurate mixture adsorption data for industrial separations
On the importance of robust and accurate mixture adsorption data for industrial separations
Author(s)
Bingel, Lukas Willi
Advisor(s)
Walton, Krista S.
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Abstract
Large-scale implementation of adsorption processes in industrial separations has enormous potential for reducing both energy costs and environmental footprint. To ease this transition, there is a need for reliable mixture adsorption data. As these data are difficult to obtain experimentally, the use of mixture prediction theories is commonplace. Here, the Ideal Adsorbed Solution Theory (IAST) has been established as the benchmark method. However, IAST relies on accurate, robust isotherm inputs and is derived based on ideality assumptions. Here, a new isotherm model is derived to accurately fit isotherm types I, III, and V with special emphasis on type V due to its appearance in many emerging adsorption systems. Then, meta-analyses of published single-component isotherm data for alkanes and alcohols are conducted to assess reproducibility and derive consensus isotherms as more robust input parameters for process modeling and simulations. To challenge the ideality assumptions, the impact of different metal-organic framework (MOF) motifs and mixed-linker configurations on the accuracy of IAST is investigated by comparing its predictions to mixture experiments. Traditional prediction theories reach their limits for novel MOFs with intrinsic flexibility due to a linker rotation-introduced gate-opening behavior. Thus, a dynamic adsorbent with record inverse selectivity for propane/propylene separation under real industrial conditions is investigated and a novel process integration is proposed. For the flexible ZIF-7, it is presented how a post-synthetic ligand exchange approach can be utilized to improve the capability for biogas separation. These results help bring adsorption-based separation systems one step closer to being reliable, energy-efficient alternatives to existing separation processes.
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Date Issued
2023-12-11
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Text
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Dissertation