Performance of a cell-foam trickle-bed reactor for phenol wet oxidation: Influence of operation parameters and modelling
| Autor: | Rita R. Zapico, Fernando V. Díez, Pablo Marín, Salvador Ordóñez |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Pressure drop
Ceramic foam Environmental Engineering Materials science Superficial velocity Chromatography General Chemical Engineering chemistry.chemical_element 02 engineering and technology Trickle-bed reactor 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences Oxygen 0104 chemical sciences chemistry.chemical_compound chemistry Chemical engineering Mass transfer Environmental Chemistry Phenol Wet oxidation 0210 nano-technology Safety Risk Reliability and Quality |
| Zdroj: | Process Safety and Environmental Protection. 107:35-43 |
| ISSN: | 0957-5820 |
| DOI: | 10.1016/j.psep.2017.01.020 |
| Popis: | The homogeneous wet oxidation of phenol, catalysed by Cu(II), has been studied in a trickle-bed reactor. The reactor bed consisted of a ceramic foam made of alumina with a cell density of 20 ppi. This bed is especially suited to promote mass transfer between phases with very low pressure drop. The gas phase (oxygen at 0.6 MPa) was circulated continuously, while the liquid phase (40 mol/m 3 phenol in water) was circulated in discontinuous mode, i.e. with total recirculation of liquid. The experiments were planned to determine the influence of the main operating conditions, i.e. liquid superficial velocity (0.9–3.3 × 10 −3 m/s), Cu(II) concentration (0.41–0.945 mol/m 3 ) and temperature (110–143 °C). Temperature and liquid superficial velocity were found to have the most marked influence in phenol conversion. The use of the foam packing, particularly at high liquid superficial velocities, enhances oxygen mass transfer between phases and increases the efficiency of the process (higher phenol conversion). A mathematical model, based on conservation equations applied to the bulk liquid and liquid film, has been proposed and validated with the experimental results. It has been found that the model is able to predict the experiments within an error of ±10%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect