<scp>l</scp>-Glutamic Acid Precipitation: Agglomeration Effects

Lindenberg, Christian; Schöll, Jochen; Vicum, Lars; Mazzotti, Marco; Brozio, Jörg

doi:10.1021/cg070161f

Cited by 63 publications

(86 citation statements)

References 34 publications

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“…The mixing efficiency of the Ymixer has been characterized previously using competitiveparallel chemical reactions and CFD. 6 L-Asparagine monohydrate was precipitated at 25 C from an aqueous solution by mixing it with 2-propanol as antisolvent. L-Asparagine monohydrate ([99%, Sigma-Aldrich, Buchs, Switzerland), 2-propanol ([99.8%, Sigma-Aldrich, Buchs, Switzerland), and deionized water were used in all experiments.…”

Section: Experimental Section Materials and Methodsmentioning

confidence: 99%

“…The solubility of L-asparagine monohydrate in aqueous solution at 25 C was measured in the range of 50-90 wt % 2-propanol by high performance liquid chromatography (HPLC) analysis of saturated solutions. A HPLC system from Waters (Waters GmbH, Eschborn, Germany) with a 125-mm GROM-SIL 100 ODS-2 column (Alltech Grom GmbH, Rottenburg-Hailfingen, Germany), a variable wavelength UV detector, and an aqueous mobile phase containing 0.1 wt % NaH 2 PO 4 as a buffer were used in all experiments.…”

Section: Solubility Measurementmentioning

confidence: 99%

“…The PBE is solved using the method developed by Kumar and Ramkrishna 24 as described elsewhere. 25 The spatial evolution of the PSD can be converted to a temporal one using the following equation:…”

mentioning

confidence: 99%

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Continuous precipitation of L‐asparagine monohydrate in a micromixer: Estimation of nucleation and growth kinetics

Lindenberg

Mazzotti

2011

AIChE Journal

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An experimental setup based on a static micromixer is used to determine nucleation and growth kinetics of L-asparagine monohydrate precipitated via antisolvent addition. Mixing in static micromixers was characterized previously using competitive-parallel reactions and computational fluid dynamics (Lindenberg et al., Chem Eng Sci. 2008;63:4135-4149). In this work, the mixer setup is used to determine nucleation and growth kinetics of L-asparagine at high supersaturations, i.e., true kinetics which are not affected by transport limitations. The method is based on measuring the particle size distribution obtained at different residence times. A population balance equation model of the process is used for the design of a continuous precipitation process. Finally, an analysis of the characteristic time scales of nucleation, growth, and mixing shows that, under the conditions in this study, mixing is much faster than precipitation and that the two processes can be decoupled. V V C 2010 American Institute of Chemical Engineers AIChE J, 57: 942-950, 2011

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Section: Experimental Section Materials and Methodsmentioning

confidence: 99%

Section: Solubility Measurementmentioning

confidence: 99%

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Continuous precipitation of L‐asparagine monohydrate in a micromixer: Estimation of nucleation and growth kinetics

Lindenberg

Mazzotti

2011

AIChE Journal

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“…Furthermore, the effect of a-form crystals on the secondary nucleation of bform crystals was also considered, to take into account that b-form crystals can nucleate from the surface of a-form crystals. 13 The growth and nucleation expressions are assumed to have similar dependencies on supersaturation as reported in past studies, 20,21 with a length-dependent term included in the crystal growth kinetic expression. The expressions for the crystallization kinetics are: a-form crystallization kinetics when S a > 1…”

Section: Crystallization Mechanisms and Kineticsmentioning

confidence: 99%

Modeling and Bayesian parameter estimation for semibatch pH‐shift reactive crystallization of l‐glutamic acid

Chiu

Braatz

2014

AIChE Journal

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A mathematical model for semibatch pH-shift reactive crystallization of L-glutamic acid is developed that takes into account the effects of protonation and deprotonation in the species balance of glutamic acid, crystal size distribution, polymorphic crystallization, and nonideal solution properties. The crystallization mechanisms of a-and b-forms of glutamic acid are addressed by considering primary and secondary nucleation, size-dependent growth rate, and mixing effects on nucleation. The kinetic parameters are estimated by Bayesian inference from batch experimental data collected from literature. Probability distributions of the estimated parameters in addition to their point estimates are obtained by Markov Chain Monte Carlo simulation. The first-principles model is observed in good agreement with the experimental data and can be further used for model predictions in robust control strategies.

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“…1,8,9 Typically, when modeling crystal formation, a complex number of phenomena need to be included, such as crystal nucleation, growth, agglomeration, and breakage. 1,10 As a result of the agglomeration of particles, clusters with highly irregular structures are often produced. The geometrical features of these clusters cannot be described by means of conventional Euclidean geometry.…”

Section: Introductionmentioning

confidence: 99%

Growth and Aggregation Regulate Clusters Structural Properties and Gel Time

Lazzari

Lattuada

2017

J. Phys. Chem. B

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When particles undergo aggregation, very often they form structures that can be described with fractal geometry concepts. The spatial organization of the particles embedded in these aggregates, quantified by means of their fractal dimension, plays a key role in the clusters' diffusion and aggregation process. Fractal dimensions are typically known for some specific, ideal aggregation scenarios, such as for diluted diffusion limited cluster aggregation (DLCA) or reaction limited cluster aggregation (RLCA). The situation becomes significantly more complicated as soon as the initial particle concentration increases and fractal-dimension changing phenomena, such as particle growth, occur. In this frame, the aim of the present work is twofold: (i) to investigate the clusters' spatial organization in a scenario where growth and aggregation occur simultaneously and (ii) to assess the corresponding aggregation kinetics. To this end, an ad hoc Monte Carlo model has been developed. Both DLCA and RLCA regimes have been explored at several initial primary particle concentrations and for different growth rates. The results were discussed in terms of the characteristic times of growth (τ G ) and aggregation (τ A ), as well as the rate at which the structural properties change, v R . It was then possible to propose empirical correlations in the form d m = d m (v R ) and t gel = t gel (τ A , τ G ) to relate the evolution of the mass mobility exponent d m and the gel times to the simultaneously occurring processes of growth and aggregation.

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l-Glutamic Acid Precipitation: Agglomeration Effects

Cited by 63 publications

References 34 publications

Continuous precipitation of L‐asparagine monohydrate in a micromixer: Estimation of nucleation and growth kinetics

Continuous precipitation of L‐asparagine monohydrate in a micromixer: Estimation of nucleation and growth kinetics

Modeling and Bayesian parameter estimation for semibatch pH‐shift reactive crystallization of l‐glutamic acid

Growth and Aggregation Regulate Clusters Structural Properties and Gel Time

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