Solid Holdup and Circulating Velocity in a Novel Airlift High-Speed Loop Reactor for the Methanol to Olefin Reaction

Abstract

Based on the reaction characteristics of methanol to olefins, a novel high-speed loop reactor (HSLR) is proposed. Distinct hydrodynamics are anticipated to optimize the olefin yield and selectivity. The HSLR mainly comprises a fluidized bed and a draft tube. There are two main regions, i.e., the draft tube and annulus region. The draft tube region is a turbulent/fast fluidized bed where the solids in the reactor flow upward, while the annulus region is a bubbling/turbulent bed where the solids move downward. The solid holdup in the two regions and internal solid circulating velocity is investigated systematically. After dimensionless analysis and continuity of solid flow, calculation models for overall solid holdup in the two regions are established. The ratio of solid circulating flow in the lower loop section ranges from 0.769 to 0.965. In addition, a mathematical model for the internal solid circulating velocity is achieved on the basis of energy equilibrium, and energy dissipation rates are analyzed accordingly.

Fast versus turbulent fluidization of Geldart Group B particles

“…The red horizontal line represents the median, the upper and lower boundaries of the box represent respectively the 75th and 25th percentiles, and the upper and lower whiskers extend to the most extreme data points within 1.5 times the difference between the 75th and 25th percentiles from the box boundaries. For local flux, the fast fluidized bed had 1320 datasets, 15 while the turbulent fluidized bed had 255 datasets 21 . For overall flux, the fast fluidized bed had 1320 datasets, 15 while the turbulent fluidized bed had 378 datasets 21 [Color figure can be viewed at wileyonlinelibrary.com]…”

“…Random Forest out-of-bag permuted predictor importance estimates for overall mass flux for the fast fluidized bed 15 and turbulent fluidized bed. 21 Then, 1320 and 378 datasets analyzed for fast and turbulent fluidization, respectively. The error bars represent two standard deviations of 10 repeats [Color figure can be viewed at wileyonlinelibrary.com] F I G U R E 9 Self-organizing map weight planes (100 neurons) of three most dominant influences (top row) and two least influences (bottom row) on overall flux, as depicted in Figure 8, for the fast and turbulent fluidized beds.…”

“…The fast fluidized bed schematic is reprinted from Reference 17, Copyright 2012, with permission from Elsevier. The turbulent fluidized bed schematic is reprinted from Reference 21, Copyright 2012, with permission from Elsevier…”

“…The fast fluidization regime is operated as a circulating fluidized bed due to the much higher entrainment rates 9 . This regime similarly garners much interest in applications similar to that of the turbulent bed like carbon capture, 19 combustion, 20 chemical synthesis, 21 and biomass gasification 22 . In contrast to the turbulent fluidized bed in which only the gas flow is controlled, both the gas and solid flows are controlled in the fast fluidized bed.…”

“…Random Forest out-of-bag permuted predictor importance estimates for local segregation data in terms of percentage mass of the more massive species for binary mixtures and broad particle size distributions. The fast fluidized bed had 324 datasets,14 while the turbulent fluidized bed had 190 datasets21 [Color figure can be viewed at wileyonlinelibrary.com] Random Forest out-of-bag permuted predictor importance estimates for local segregation data in terms of percentage mass of the more massive species for binary mixtures (90 datasets) and broad PSDs (100 datasets)14 [Color figure can be viewed at wileyonlinelibrary.com] earlier studies that also indicated different behaviors between binary mixtures and broad PSDs 28,30,34,35,[63][64][65]. For binary mixtures, the composition of the constituents was approximately as important as the overall mass flux (G s ) in influencing segregation.…”