The pharmaceutical industry has traditionally implemented batch manufacturing for the production of a wide range of products due to its mature technological development and ability for recall of products where necessary. However, several demonstrations of Continuous Pharmaceutical Manufacturing (CPM) in the past two decades have drawn significant attention from academia, industry and regulatory bodies due to its potential for smaller equipment, enhanced efficiencies, access to difficult or hazardous process conditions with greater ease and safety and reduced costs and waste. While continuous processing is not new in other manufacturing sectors, its application to pharmaceutical production has only drawn significant attention in recent years due to the numerous demonstrations of continuous flow syntheses of complex molecules and functional groups inherent of Active Pharmaceutical Ingredients (APIs), which is the foundation of any end-to-end CPM plant. The literature to date has predominantly focussed on design and optimisation of flow synthesis routes; however, the development of efficient continuous separation processes is a major bottleneck to CPM and are often challenging and materially intensive unit operations. The design of effective continuous separation processes for societally important APIs amenable to continuous production is essential for CPM success. Mathematical modelling is a viable and useful tool in the elucidation of promising designs prior to pilot plant studies that can allow rapid screening of multiple candidate configurations and can circumvent expensive and laborious experimental campaigns. Moreover, they allow optimisation of process design configurations to maximise their operational and economic benefits. This PhD thesis aims to elucidate cost-optimal upstream CPM plant and continuous separation process designs for a range of APIs. Steady-state process models for upstream CPM plants for different APIs are constructed, using published data for reaction rate law elucidation and kinetic parameter estimation, activity coefficient and group contribution models for non-ideal multicomponent mixture phase equilibria prediction and pharmaceutical process costing methodologies. The constructed models are then used for process simulation, design and optimisation of CPM plants, using Nonlinear Programming (NLP) for individual case-based process optimisation and Mixed Integer Nonlinear Programming (MINLP) for CPM process synthesis to optimality. The systematic frameworks and methods used in this work can be expanded to other APIs amenable to CPM with similar processes. This work highlights the immense value in systematic and rigorous model-based simulation and optimisation campaigns for CPM process development
