Evaluation of the effects of process parameters on granule mean size in a conical high shear granulator using response surface methodology

Modeling of Particulate Processes for the Continuous Manufacture of Solid-Based Pharmaceutical Dosage Forms

“…Box and Wilson first proposed RSM in 1951 as a means of optimizing operating conditions for a chemical processing [130]. In recent years RSM has been increasingly used for pharmaceutical applications [12,129,131,134,141,142]. The goal of RSM is to develop a functional representation for the response as a function of input variables.…”

“…[124][125][126][127][128]. Alternatively a computationally expensive model can be replaced by lower dimensional surrogate model obtained through fitting of experimental or simulated data using techniques such as kriging, response surface methodology (RSM), artificial neural networks (ANN) or high dimensional model representation (HDMR) [12,[129][130][131][132][133][134]. The motivation for using ROMs is that they are less computationally expensive than the original models and are therefore suitable for process simulation and optimization purposes.…”

“…This results in a mixed integer nonlinear program (MINLP) optimization problem formulation [12]. RSM has been used to model pharmaceutically relevant unit operations, including loss-in-weight feeding [129,131] and granulation [134,142]. It has also been used to explore design space and aid in the identification of critical process parameters for pharmaceutical applications [12,141].…”

A reduced order PBM–ANN model of a multi-scale PBM–DEM description of a wet granulation process

“…Even though a smaller system of particles are being simulated in the periodic section approach, the fact that DEM algorithm needs to be implemented multiple times for system analysis necessitates investigation into drastically faster reduced order modeling techniques. Various data fitting techniques are available in literature that have been used to replace full scale models including response surface methodology (RSM) Jia et al, 2009;Ranjbarian and Farhadi, 2013), Kriging method (Gao et al, 2012;Jia et al, 2009;Ranjbarian and Farhadi, 2013), high dimensional model representations (HDMR) (Banerjee et al, 2010;Banarjee and Ierapetritou, 2004), and artificial neural networking (ANN) (Boukouvala et al, , 2011Akkisetty et al, 2010;Basheer and Hajmeer, 2000). Developing such reduced-order models not only provide a quantitatively accurate description of the system dynamics which are far less computationally taxing than the original models but also provide a means by which the system dynamics can be readily interpreted for process simulation and optimization purposes (Lucia et al, 2004).…”

Zero‐order release three‐layered tablet with an acemetacin solid dispersion core and a hydroxypropyl methylcellulose capped matrix

“…Various factors, including the molecular weight and amounts of the polymers, could affect the release of drug from three‐layered matrix tablets; thus, Box–Behnken design (BBD) was used to optimize the formula. BBD is widely used to control pharmaceutical processes, including optimizing granulation, formulation of extended‐release matrix tablets, or transdermal delivery systems design…”