Pharmaceutical industries, like most industries, are subjected to stringent quality and regulatory requirements to ensure the manufacturing of safe and high-quality medicinal products. Continuous manufacturing has emerged as a transformative approach offering the potential to meet global demands of medicines through efficient and continuous processes. However, its adoption in tablet manufacturing remains constrained by the complex, multivariate behaviour of particulate processes. Moreover, the lack of comprehensive modelling frameworks further hinders understanding and control of the multistage processes.
This thesis aims to develop and evaluate novel predictive modelling frameworks tailored to the continuous manufacturing of pharmaceutical tablets, using data collected from an industrial-scale pilot plant (Consigma-25) encompassing five critical unit operations. An integrated and sequential modelling framework was constructed using ensemble machine learning techniques, including gradient boosting machines and random forests, to predict key quality attributes across stages, with Gaussian mixture models incorporated to reduce uncertainties. To enhance interpretability, a hybrid modelling approach combining artificial neural networks with interval type-2 fuzzy inference system was developed. Additionally, a novel integration of Adaptive Neuro-Fuzzy Inference System with a Genetic Algorithm formed the basis of a model-informed optimisation strategy, enabling the identification of optimal process settings to control the final product quality under “Right-First-Time” manufacturing.
The results demonstrate that proposed frameworks were effective in capturing the non-linearity among process parameters and quality outcomes, achieving $R^2$ values exceeding 0.90 across the frameworks. This represents a predictive capability improvement of 56\% compared with prior studies. The incorporation of interpretable, uncertainty-aware methods ensured model outputs remained effective to illustrate the processes' understanding despite complexity. The model-informed optimisation strategy was validated through practical application within the right-first-time manufacturing concept. These research findings demonstrate the potential of the proposed frameworks to advance pharmaceutical tablet manufacturing by bridging the gap between scientific research innovation and scalable industrial implementation.
