Batch reactor scale-up of the mixing-sensitive Bechamp reaction based on the heat pulse method

Camps, Lennart; Moens, Luc; Groth, Urs; Braeken, Leen; Kuhn, Simon; Thomassen, Leen

doi:10.1016/j.ces.2021.116928

Cited by 3 publications

(5 citation statements)

References 25 publications

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“…For a given feed location, again as expected, the increase in impeller rotational speed reduced the mixing time. Nonreactive mixing times measured for our reactor range around tens of seconds, which is comparable to literature data (modeled or experimentally measured) for similar tank sizes and geometries for a range of impellers/stirrers. ,,, This mixing time is about 10-fold shorter than those measured herein even when accounting for the fact that we have used an unbaffled stirred tank. We note that, however, these literature data (e.g., that presented in Table 5 of Nere et al) were limited to homogeneous liquid phase systems with no reactions or precipitation involved, while for a reactive system, t mix of several minutes were reported for unbaffled stirred tanks (e.g., see ref ).…”

Section: Resultssupporting

confidence: 82%

“…Generating a homogeneity or pH map for each image is essential in determining the instantaneous degree of mixing (DoM) in the vessel for the duration of the experiment. Following the literature, DoM was defined as the mixing time necessary to achieve 95% homogeneity starting from an initially inhomogeneous mixture. ,, This is the same as the time needed to reach 5% from perfect mixing as defined by others. ,,, The images were processed using the RGB color model using a code written in MATLAB R2019b (Mathworks). In the RGB color model, a color can be represented as a combination of varying hue levels ranging from 0 to 255 of pure red, green, and blue light.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…55,65,66 This is the same as the time needed to reach 5% from perfect mixing as defined by others. 27,30,55,67 The images were processed using the RGB color model using a code written in MATLAB R2019b (Mathworks). In the RGB color model, a color can be represented as a combination of varying hue levels ranging from 0 to 255 of pure red, green, and blue light.…”

Section: Image Acquisition and Processingmentioning

confidence: 99%

“…There are three mixing mechanisms: macromixing, mesomixing, and micromixing . Macromixing which is the blend time in a system caused by mechanical stirring is defined as the mixing on the largest possible scale . Micromixing is mixing on the smallest scales of motion (the Kolmogorov scale) dominated by diffusion.…”

Section: Introductionmentioning

confidence: 99%

“…21 Macromixing which is the blend time in a system caused by mechanical stirring is defined as the mixing on the largest possible scale. 30 Micromixing is mixing on the smallest scales of motion (the Kolmogorov scale) dominated by diffusion. Mesomixing, on the other hand, is the turbulent dispersion of a feed stream shortly after it enters a mixing vessel caused by the action of the bulk fluid interacting with the feed stream.…”

Section: ■ Introductionmentioning

confidence: 99%

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A Novel Method for Understanding the Mixing Mechanisms to Enable Sustainable Manufacturing of Bioinspired Silica

2022

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Bioinspired silica (BIS) has received unmatched attention in recent times owing to its green synthesis, which offers a scalable, sustainable, and economical method to produce high-value silica for a wide range of applications, including catalysis, environmental remediation, biomedical, and energy storage. To scale-up BIS synthesis, it is critically important to understand how mixing affects the reaction at different scales. In particular, successful scale-up can be achieved if mixing time is measured, modeled, and kept constant across different production scales. To this end, a new image analysis technique was developed using pH, as one of the key parameters, to monitor the reaction and the mixing. Specifically, the technique involved image analysis of color (pH) change using a custom-written algorithm to produce a detailed pH map. The degree of mixing and mixing time were determined from this analysis for different impeller speeds and feed injection locations. Cross validation of the mean pH of selected frames with measurements using a pH calibration demonstrated the reliability of the image processing technique. The results suggest that the bioinspired silica formation is controlled by meso-and, to a lesser extent, micromixing. Based on the new data from this investigation, a mixing time correlation is developed as a function of Reynolds number�the first of a kind for green nanomaterials. Further, we correlated the effects of mixing conditions on the reaction and the product. These results provide valuable insights into the scale-up to enable sustainable manufacturing of BIS and other nanomaterials.

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