Impact of Non-Ideality on Stability and Performance in Benzaldehyde Production.
Saved in:
| Title: | Impact of Non-Ideality on Stability and Performance in Benzaldehyde Production. |
|---|---|
| Authors: | Andriani, G.1 giuseppe.andriani@unipd.it, Pio, G., Vianello, C.1, Salzano, E.2, Mocellin, P.1,3 |
| Source: | Chemical & Biochemical Engineering Quarterly. 2025, Vol. 39 Issue 4, p175-184. 10p. |
| Subjects: | Tubular reactors, Benzaldehyde, Exothermic reactions, Transport theory, Chemical reactors |
| Abstract: | Exothermic reactive processes pose significant safety risks due to the possibility of thermal runaway. Reliable tools for the design, control, and optimization of such systems are therefore essential. Stability analysis provides a powerful framework, but its effectiveness depends on an adequate representation of reactor hydrodynamics. Following the pioneering work of Varma, Morbidelli, and Wu, tubular reactors have mainly been analyzed using ideal plug flow reactor (PFR) models, which may lead to a partial assessment of critical operating regimes. In particular, neglecting non-idealities such as axial dispersion can underestimate runaway risk and distort predicted stability limits. In this work, the impact of non-ideal plug flow behavior on reactor performance and stability is investigated by comparing ideal and axially dispersed PFR models within a sensitivity-based stability analysis (VMWT). The methodology is applied to the design of a multitubular reactor for the liquid-phase oxidation of benzyl alcohol to benzaldehyde. The results show how incorporating axial dispersion yields a more realistic stability picture while preserving computational efficiency, thereby supporting safer and more robust reactor design. [ABSTRACT FROM AUTHOR] |
| Copyright of Chemical & Biochemical Engineering Quarterly is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
