In view of growing interest and investment in continuous manufacturing, the development and utilization of mathematical model(s) of the manufacturing line is of prime importance. These models are essential for understanding the complex interplay between process-wide critical process parameters (CPPs) and critical quality attributes (CQAs) beyond the individual process operations. In this work, a flowsheet model that is an approximate representation of the ConsiGma TM -25 line for continuous tablet manufacturing, including wet granulation, is developed. The manufacturing line involves various unit operations, i.e., feeders, blenders, a twin-screw wet granulator, a fluidized bed dryer, a mill, and a tablet press. The unit operations are simulated using various modeling approaches such as data-driven models, semi-empirical models, population balance models, and mechanistic models. Intermediate feeders, blenders, and transfer lines between the units are also simulated. The continuous process is simulated using the flowsheet model thus developed and case studies are provided to demonstrate its application for dynamic simulation. Finally, the flowsheet model is used to systematically identify critical process parameters (CPPs) that affect process responses of interest using global sensitivity analysis methods. Liquid feed rate to the granulator, and air temperature and drying time in the dryer are identified as CPPs affecting the tablet properties.

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Milling is an essential unit operation used for particle size reduction in solid oral dosage manufacturing. The breakage of particles in a comil is due to the intense shear applied on the particles between impeller and the screen. Breakage also occurs due to the impact from a rotating impeller. Particles exit the mill based on their size relative to the aperture size of the screen bores. This study was set up to understand the working of the comil better. A new CPP (Critical Process Parameter), in the form of batch loading was identified. It was found that there are two different regimes (quasi static regime and impact regime) in which a comil generally operates, and the effect of the CPP’s (batch loading and impeller speed) on these regimes was studied. Knowledge of the effect of upstream operations on a particular unit operation is of significant importance, especially for pharmaceutical industry. For this reason, the effect of granulation variables such as liquid-to-solid ratio, granulator impeller speed and the amount of binder in the formulation were analyzed. Milled particle size distribution and other critical quality attributes such as bulk density, friability, and porosity were studied. Batch loading and the interaction effect of batch loading with impeller speed are significant parameters that affect the quality attributes of the mill. Predictive regression models were developed for throughput of the mill, milled product bulk density and milled product tapped density (with an R2 of 0.987, 0.953, 0.995 respectively) to enable their use in downstream process modeling.

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Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black‐box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical‐based approach (Jack‐knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.