Periodic steady-state flow crystallization of a pharmaceutical drug using MSMPR operation

AbstractIn this paper, a novel concept of periodic mixed suspension mixed product removal (PMSMPR) crystallization process is demonstrated. An integrated array of process analytical technologies (PATs), based on attenuated total reflectance ultra violet/visible spectroscopy, focused beam reflectance measurement, particle vision microscopy and Raman spectroscopy, and in-house developed crystallization process informatics system software (CryPRINS) were used to monitor the periodic steady-state flow crystallization of paracetamol. Periodic steady-state is a new concept defined as a state of a system that maintains itself despite transitory effects caused by periodic, but controlled disruptions (state of controlled operation). This work also illustrates the concept of “state of controlled operation” instead of “steady-state operation” as a state that can characterize continuous (periodic) operation. The PMSMPR was configured as either a single- or two-stage unit and operated for up to 11 residence times without blockage or encrustation problems. The number of PMSMPR stages, seed characteristics (size, shape and distribution), and use of recycle stream were the main variables that influenced the periodic operation, significantly affecting the extent of secondary nucleation and growth. The results further illustrate the use of PAT and information system tools together to determine when the periodic operation reaches a state of controlled operation (periodic steady-state). These tools provided a better understanding of the variables and operating procedures influencing the periodic operation

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques