Using AI/ML to predict blending performance and process sensitivity for Continuous Direct Compression (CDC)

Utilising three artificial intelligence (AI)/machine learning (ML) tools, this study explores the prediction of fill level in inclined linear blenders at steady state by mapping a wide range of bulk powder characteristics to processing parameters. Predicting fill levels enables the calculation of blade passes (strain), known from existing literature to enhance content uniformity. We present and train three AI/ML models, each demonstrating unique predictive capabilities for fill level. These models collectively identify the following rank order of feature importance: RPM, Mixing Blade Region (MB) size, Wall Friction Angle (WFA), and Feed Rate (FR). Random Forest Regression, a machine learning algorithm that constructs a multitude of decision trees at training time and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees, develops a series of individually useful decision trees. but also allows the extraction of logic and breakpoints within the data. A novel tool which utilises smart optimisation and symbolic regression to model complex systems into simple, closed-form equations, is used to build an accurate reduced-order model. Finally, an Artificial Neural Network (ANN), though less interrogable emerges as the most accurate fill level predictor, with an r2 value of 0.97. Following training on single-component mixtures, the models are tested with a four-component powdered paracetamol formulation, mimicking an existing commercial drug product. The ANN predicts the fill level of this formulation at three RPMs (250, 350 and 450) with a mean absolute error of 1.4%. Ultimately, the modelling tools showcase a framework to better understand the interaction between process and formulation. The result of this allows for a first-time-right approach for formulation development whilst gaining process understanding from fewer experiments. Resulting in the ability to approach risk during product development whilst gaining a greater holistic understanding of the processing environment of the desired formulation.</p

Convection and segregation in fluidised granular systems exposed to two-dimensional vibration

Convection and segregation in granular systems not only provide a rich phenomenology of scientifically interesting behaviours but are also crucial to numerous 'real-world' processes ranging from important and widely used industrial procedures to potentially cataclysmic geophysical phenomena. Simple, small-scale experimental or simulated test systems are often employed by researchers in order to gain an understanding of the fundamental physics underlying the behaviours of granular media. Such systems have been the subject of extensive research over several decades, with numerous system geometries and manners of producing excitation explored. Energy is commonly provided to granular assemblies through the application of vibration - the simplicity of the dynamical systems produced and the high degree of control afforded over their behaviour make vibrated granular beds a valuable canonical system by which to explore a diverse range of phenomena. Although a wide variety of vibrated systems have been explored in the existing literature, the vast majority are exposed to vibration along only a single spatial direction. In this paper, we study highly fluidised systems subjected to strong, multi-directional driving, providing a first insight into the dynamics and behaviours of these systems which may potentially hold valuable new information relevant to important industrial and natural processes. With a particular focus on the processes of convection and segregation, we analyse the various states and phase transitions exhibited by our system, detailing a number of previously unobserved dynamical phenomena and system states.</p