Digital twin of low dosage continuous powder blending - Artificial neural networks and residence time distribution models.

In this paper we present a thorough description of the digital twin development for a continuous pharmaceutical powder blending process in accordance with the Process Analytical Technologies (PAT) and Quality by Design (QbD) guidelines. A low-dosage system of caffeine active pharmaceutical ingredient (API) and dextrose excipient was examined via continuous blending experiments. Near infrared (NIR) spectroscopy-based process analytics were applied; quantitative evaluation of spectra was achieved using multivariate data analysis. The blending system was represented with mechanistic residence time distribution (RTD) models and two types of recurrent artificial neural networks (ANN), experimental datasets were used for model training or fitting and validation. Detailed comparison of the two modelling approaches, the optimization of the model-based digital twin, and efficiency of the soft sensor-based process monitoring is presented through several validating simulations. Both RTD models and nonlinear autoregressive neural networks demonstrated excellent predictive power for the low dosage blending process. RTD models can prove to be more advantageous in industrial development as they are less resource-intensive to develop and prediction accuracy on low concentration levels lacks dependency from the precision of chemometric calibration. Reduced material costs and limited development timeframe render the digital twin an efficient tool in technological development.

Process Design of Continuous Powder Blending Using Residence Time Distribution and Feeding Models.

The present paper reports a thorough continuous powder blending process design of acetylsalicylic acid (ASA) and microcrystalline cellulose (MCC) based on the Process Analytical Technology (PAT) guideline. A NIR-based method was applied using multivariate data analysis to achieve in-line process monitoring. The process dynamics were described with residence time distribution (RTD) models to achieve deep process understanding. The RTD was determined using the active pharmaceutical ingredient (API) as a tracer with multiple designs of experiment (DoE) studies to determine the effect of critical process parameters (CPPs) on the process dynamics. To achieve quality control through material diversion from feeding data, soft sensor-based process control tools were designed using the RTD model. The operation block model of the system was designed to select feasible experimental setups using the RTD model, and feeder characterizations as digital twins, therefore visualizing the output of theoretical setups. The concept significantly reduces the material and instrumental costs of process design and implementation.