Kinetic modelling covers a key role in process simulation and design. Recently the methanol sector is assisting a remarkable enhancement due to its applications as fuel, solvent, and precursor as shown in Bozzano and Manenti (2016). The increasing number of patents, the market prospects, and recent research witness this renewed interest. However, despite this rate in developing and improving technologies, the kinetics modelling does not follow these trends. The methanol synthesis chemical paths, the intermediates, and the real role of the active sites are nowadays still unclear. However, process engineering requires reliable models to estimate the methanol synthesis rate, hence, to design and size the reactor and downstream equipment. Currently, the most used kinetics are Graaf and Vanden Bussche - Froment's models which in any case show some shortcomings and weaknesses. Starting from these premises, the need for updated kinetics is clear. This work aims at comparing and highlighting the impact of different kinetic models (1) original Graaf (or-GR), (2) Vanden Bussche - Froment (VBF), and (3) refitted Graaf (ref-GR) on the methanol synthesis configuration for different feedstocks through an in-silico assessment. The general simulation flowsheet includes the single-stage PFR for the methanol synthesis, the condensation step, and recycle loop for the unreacted syngas. The comparison with industrial data proves that the ref-GR model predicts better than the original Graaf model as in Graaf et al. (1988), while the VBF, Vanden Bussche and Froment (1997), tends to overestimate methanol production. The validation exploits industrial data published in the literature.

Impact of Methanol Synthesis Kinetics on Bulk Production Prediction: an In-Silico Assessment / F. Bisotti, M. Fedeli, C. Pirola, G. Bozzano, F. Manenti (COMPUTER-AIDED CHEMICAL ENGINEERING). - In: 32nd European Symposium on Computer Aided Process Engineering / [a cura di] L. Montastruc, S. Negny. - [s.l] : Elsevier, 2022. - ISBN 9780323958790. - pp. 55-60 (( Intervento presentato al 32. convegno European Symposium on Computer Aided Process Engineering [10.1016/b978-0-323-95879-0.50010-2].

Impact of Methanol Synthesis Kinetics on Bulk Production Prediction: an In-Silico Assessment

C. Pirola;
2022

Abstract

Kinetic modelling covers a key role in process simulation and design. Recently the methanol sector is assisting a remarkable enhancement due to its applications as fuel, solvent, and precursor as shown in Bozzano and Manenti (2016). The increasing number of patents, the market prospects, and recent research witness this renewed interest. However, despite this rate in developing and improving technologies, the kinetics modelling does not follow these trends. The methanol synthesis chemical paths, the intermediates, and the real role of the active sites are nowadays still unclear. However, process engineering requires reliable models to estimate the methanol synthesis rate, hence, to design and size the reactor and downstream equipment. Currently, the most used kinetics are Graaf and Vanden Bussche - Froment's models which in any case show some shortcomings and weaknesses. Starting from these premises, the need for updated kinetics is clear. This work aims at comparing and highlighting the impact of different kinetic models (1) original Graaf (or-GR), (2) Vanden Bussche - Froment (VBF), and (3) refitted Graaf (ref-GR) on the methanol synthesis configuration for different feedstocks through an in-silico assessment. The general simulation flowsheet includes the single-stage PFR for the methanol synthesis, the condensation step, and recycle loop for the unreacted syngas. The comparison with industrial data proves that the ref-GR model predicts better than the original Graaf model as in Graaf et al. (1988), while the VBF, Vanden Bussche and Froment (1997), tends to overestimate methanol production. The validation exploits industrial data published in the literature.
industrial comparative case studies; kinetics comparison; Lurgi and ICI technologies; methanol reactor; process simulation
Settore ING-IND/25 - Impianti Chimici
Settore ICHI-02/A - Impianti chimici
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1106308
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