Modeling of Nucleation, Growth, and Dissolution of Paracetamol in Ethanol Solution for Unseeded Batch Cooling Crystallization with Temperature-Cycling Strategy

Abstract

Controlling crystal size distributions is critical to ensure the quality of products and operational efficiency for downstream processing. In batch cooling crystallization, temperature cycling strategies are often employed to control the crystal size distribution and minimize fines. Optimizing the complex operation of this strategy, where crystallization and dissolution occur in a single batch run, remains a challenge. For this problem, we develop a model of the process by a population balance considering nucleation, growth, and dissolution of crystals. In a case study of paracetamol in ethanol, the parameters in the population balance model were estimated from multiple experimental runs. The developed model was validated against additional experimental data sets, where the concentration and crystal size distributions of the final crystal products were predicted successfully. R 2 values are greater than 0.96 in all cases.

Porous Starch-Based Capsule as an Effective Colon-Targeting Carrier for Oral Paclitaxel Delivery

“…The amorphous structure has no specific cell parameters due to the disorganized arrangement of internal atoms . The crystal structure determines the voids, which in turn affects the dissolution rate . Therefore, the high free energy and low density of PTX microspheres lead to faster dissolution of the drug than their crystalline forms. , However, metastable properties and crystallization tendency during preparation and storage are of serious concern to drug developers.…”

Crystallization Process Design by Proximal Policy Optimization

“…Fig. 1 shows a sketch of a batch cooling crystallization of paracetamol (in ethanol) for the production of crystal particles [5]. In the following work, the process was implemented as a simulation, and the agent had to find the optimal action in each state in order to traverse the state space and accumulate a certain reward, which will be defined below.…”

Statistical Analyses of a Population Balance Model of a Batch Crystallization Process

“…Different from the study of Moraes et al and commonly used in the literature, ,, the model applied in this work uses the ratio between the higher–order moments and the zero–order moment as dependent variables. This change was implemented because the ratios represent the averages commonly used for interpreting experimental data and exhibit a normal distribution of errors, as discussed in Section .…”

“…In these cases, population balance models are usually applied to describe the behavior of the particle population and its properties of interest, commonly size and shape, once the rate equations are established. , McDonald et al proposed a population balance model to describe the continuous reactive crystallization of β-lactam antibiotics. The population balance approach was also extensively applied to model paracetamol crystallization. − Moreover, previous studies formulated population balance models to describe the tetragonal hen-egg-white lysozyme crystallization considering different kinds of crystallizers, such as continuous flow reactors , and batch tank reactors. − Considering, in particular, the crystallization of salts, population balance models were also developed to describe the crystallization of potassium dihydrogen phosphate and potassium sulfate . From experimental data, it becomes possible to estimate appropriate values for the kinetic parameters, enabling the evaluation of mechanisms such as nucleation, growth, agglomeration, and crystal breakage.…”

“…In recent years, many studies in the literature have focused on applying optimization techniques to estimate the kinetic parameters of crystallization models. ,,,,− Kim et al developed a population balance model for the crystallization of paracetamol and combined a weighted least-squares procedure with a sequential quadratic programming (SQP) algorithm to address the optimization problem. They incorporated the final crystal volume density distribution and concentration deviations into the objective function for estimating nucleation and growth kinetic rate parameters, while concentration deviations alone were used for estimating dissolution kinetic rate parameters.…”