Catalytic reactor engineering ⇒ information-driven design of packed (operando), fluidized, multi-functional, and -phase reactors

Problem statement

At lab-scale, the ultimate goal of a catalytic reactor is to provide (1) reliable kinetic information, neglecting or controlling other phenomena (heat-mass transfer and hydrodynamics); (2) high-throughput data to amplify the results, accelerate model and catalyst discoveries; and (3) results with the minimum requirements of reactants and wastes generated. The pillars of these reactors are quality, quantity, and safety.

We design, build and test different laboratory-scale reactors. Our strategy involves creating and testing reactor prototypes while modeling these using our workflow. We have high-speed cameras, probes, and other measuring instruments to understand the reactor behavior. We focus on packed-, fluidized-bed, and multiphase reactors:

In packed bed reactors, we focus on forced dynamic and operando reactors. These are the quintessence of information-driven reactors where the dynamics can involve flow changes, temperature, pressure, partial pressure, presence of activity modifiers (poissons, H2O…). In operando reactors, we follow a spectro-kinetic-deactivation-hydrodynamic approach to resolve the individual steps involved. In fluidized bed reactors, we focus on downers and multifunctional reactors (circulating, multizone or two-zone, Berty reactors) We focus on trickle-bed, slurry, and bio-electrochemical reactors in multiphase bed reactors.

Al pilot-plant scale, we aim to reach the maximum productivity levels while solving the growing pains: the scale-up. Based on a robust kinetic model obtained in the intrinsic kinetic reactor (lab-scale) and using computational fluid dynamics, we design, build, and operate pilot plants. At this stage, we seek partnerships with investment or industrial enterprises to make these pilot plants.

Goals

  • Multifunctional fluidized bed reactors ⇒ multizone, circulating...
  • Packed bed membrane reactors
  • Forced dynamic reactors ⇒ pulsing, SSITKA...
  • Forced dynamic operando reactors ⇒ DRIFTS, TPSR...
  • Operando reactors
  • Spray fluidized bed reactors
  • Downer reactor I ⇒ micro downer
  • Downer reactor II ⇒ counter-current and scale-up
  • Batch Berty reactor ⇒ short contact time
  • Multiphase reactors ⇒ trickle bed and slurry
  • High throughput experimentation (HTE) reactors
  • Photo-thermal and bioreactors
  • Reactor visualization and prototyping lab
  • Spatio-temporal hydrodynamic characterization and validation

Related People

Related Publications

Streamlining the estimation of kinetic parameters using periodic reaction conditions: The methanol-to-hydrocarbon reaction as a case study

by Vicente, Gayubo, Aguayo, Castaño
Chem. Eng. J. Year: 2022 DOI: https://doi.org/10.1016/j.cej.2022.134800

Abstract

Reducing the experimental time required to obtain a robust kinetic model with reliable kinetic parameters has been a long-standing objective in reaction engineering. In the present study, we compare the kinetic modeling of two sets of data obtained using periodic reaction conditions (PRC) and stationary reaction conditions (SRC). As a case study, we use the well-known methanol-to-hydrocarbon reaction on HZSM-5 zeolite. The SRC experiments are conducted with a temperature of 425–475 °C, a total pressure of 2.5 bar, a partial pressure for methanol of 1.125 bar, a space time of 0.1–1.5 gcat h molC−1, a initial molar ratio water:methanol of 0–0.66 and 16 h on stream. The PRC experiments involve sinusoidal variation in the methanol and water flowrates of 135 ± 88 µL min−1 and 20 ± 20 µL min−1, respectively, with a period of 16 h or sinusoidal variation in the temperature of 450 ± 25 °C with periods of 8 and 16 h. Several strategies are then used in fitting the kinetic parameters of five models. We obtain relatively similar results in terms of model discrimination, the parameters, and confidence intervals with a cumulative experimental time of 64 h on stream under the PRC compared with 192 h on stream under the SRC, a reduction of 67% in the experimental time.

Keywords

OLG MKM CRE