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use of multiscale modElling to Minimize coke ProducTion during the methanol-to- HYdrocarbon process

Objective

The methanol-to-hydrocarbon (MTH) process is a versatile catalytic process that are gradually playing a more important role in the economy. However, an important factor that is inhibiting the profitability of MTH is accumulation of coke in the pores of the catalyst during operations. To reduce or eliminate the coke formation during MTH operations, it is necessary to have a detailed mechanistic insight into its cause of formation. In this proposal, I will achieve this insight through a computational modelling strategy. I will study the mechanism of the MTH process at various time- and length scales, using various computational methodologies. I will use computational fluid dynamics (CFD) to study the fluid flow at the reactor scale and the diffusion in the macropores. I will use kinetic Monte Carlo (kMC) and molecular dynamics (MD) to study the diffusion in the meso- and micropores. Finally, I will use density functional theory (DFT) to study the reactions at the active sites. The processes studied at the various length scales will be coupled together through a multiscale methodology.
Multiscale modelling has steadily evolved over the past decade, but the concept is still at the proof-of-principle stage where the methodology has been demonstrated for simple test systems such as CO oxidation. The methodologies that will provide data to the multiscale simulation, CFD, kMC, MD, and DFT have all reached a high level of maturity. Now is the right moment to use a multiscale methodology to couple these methodologies together and solve the problem of coke formation in the MTH process.
The potential outcomes are the following: 1) an understanding of how coke is formed in the MTH process; 2) a larger acceptance in the catalysis community to use multiscale modelling in the design of new catalysts; and 3) tighter interdisciplinary collaborations.

Field of science

  • /natural sciences/computer and information sciences/artificial intelligence/computational intelligence
  • /natural sciences/chemical sciences/electrochemistry/electrolysis
  • /natural sciences/physical sciences/classical mechanics/fluid mechanics/fluid dynamics

Call for proposal

H2020-MSCA-IF-2018
See other projects for this call

Funding Scheme

MSCA-IF-EF-ST - Standard EF

Coordinator

POLITECNICO DI MILANO
Address
Piazza Leonardo Da Vinci 32
20133 Milano
Italy
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 171 473,28