Comprehensive kinetic model for the dissolution, reaction, and crystallization processes involved in the synthesis of aspirin

Abstract

Kinetic modeling of batch reactions monitored by in situ spectroscopy has been shown to be a helpful method for developing a complete understanding of reaction systems. Much work has been carried out to demonstrate the ability to model dissolution, reaction, and crystallization processes separately; however, little has been performed in terms of combining all of these into one comprehensive kinetic model. This paper demonstrates the integration of models of dissolution, temperature-dependent solubility, and unseeded crystallization driven by cooling into a comprehensive kinetic model describing the evolution of a slurry reaction monitored by in situ attenuated total reflectance ultraviolet-visible spectroscopy. The model estimates changes in the volume of the dissolved fraction of the slurry by use of the partial molar volume of the dissolved species that change during the course of reagent addition, dissolution, reaction, and crystallization. The comprehensive model accurately estimates concentration profiles of dissolved and undissolved components of the slurry and, thereby, the degree of undersaturation and supersaturation necessary for estimation of the rates of dissolution and crystallization. Results were validated across two subsequent batches via offline high-performance liquid chromatography measurements.

An approach for simultaneous estimation of reaction kinetics and curve resolution from process and spectral data

“…We now consider a larger parameter estimation problem with more complex kinetics. The reaction mechanism of aspirin production is described by Joiner et al with the following reactions: …”

“…Here, we add system noise η to produce concentration profiles for SA , ASA , and ASAA by solving the corresponding stochastic differential equations. Also, the pure component spectra for SA , ASA , and ASAA are the same as in the work of Joiner et al, and the measurement error for D was introduced with normally distributed random numbers. The dimension of the D matrix is 471 × 111.…”

“…Such techniques rely on the availability of accurate pure component spectra to identify the compounds participating in the reaction. The approach based on Gauss‐Newton‐Levenberg‐Marquardt is widely used to adjust the kinetic parameters for minimizing the difference between the measured and modeled absorbance spectra and usually has good performance . This kind of approach fixes the kinetic parameters and integrates the system of ordinary differential equations (ODEs) to get the concentration profiles.…”

A Unified Framework for Kinetic Parameter Estimation Based on Spectroscopic Data with or without Unwanted Contributions

“…A large parameter estimation problem with complex kinetics is considered. The reaction mechanism of aspirin production (a nonisochoric process) is described in ref with the following reactions: The stoichiometric coefficients matrix St is each column of corresponds to SA­(solid), SA, AA, HA, ASA­(solid), ASA, ASAA, and H 2 O.…”

“…Another widely applied approach is the Gauss–Newton–Levenberg–Marquardt (GNLM) , based hard-modeling method. Several studies − claim that hard-modeling type methods are more appropriate than soft-modeling approaches in chemical kinetics. In the course of this approach, the kinetic parameters are refined to minimize the sum of squares of the residuals between the measured and modeled spectroscopic data.…”