Spherical agglomeration (SA) is a novel process intensification strategy for particulate manufacturing. In the context of pharmaceutical manufacturing, it has the potential to reduce the number of unit operations in downstream processing from seven to three, which significantly reduces the manufacturing cost. However, SA process development for a new API in the drug pipeline is still a challenging exercise, which has impeded its practical implementation. The major bottleneck lies in the lack of fundamental understanding of the mechanistic principles underlying agglomeration of primary crystals, which can enable rational process design. In addition, most SA processes reported in literature focus on only the API, which does not eliminate the blending and wet granulation unit operations. The major purposes of this thesis are to (i) develop a first principle mathematical framework which can identify the fundamental agglomeration mechanism (ii) develop a model based online optimization framework, which can control the process, even in the presence of model parametric uncertainties (iii) develop a rational framework for co-agglomerating APIs and excipients, guided by process analytical technology tools. It is believed that the novel technology developed in this thesis will lay the groundwork for fast and robust process development of co-agglomerating APIs and excipients in the future, thereby enabling one-step direct compression. The large-scale development and deployment of this technology will significantly reduce the time to market and the manufacturing costs for new APIs, thereby ensuring higher accessibility of life-saving drugs.

<p>  </p> <p>Crystallization is a purity and particle control unit operation commonly used in industries such as pharmaceuticals, agrochemicals, and energetics. Often, the active ingredient’s crystal mean size, polymorphic form, morphology, and distribution can impact the critical quality attributes of the final product. The active ingredient typically goes through a series of process development iterations to optimize and scale-up production to reach production scale. Guided by the FDA, the paradigm shift towards continuous processing and crystallization has shown benefits in introducing cheaper and greener technologies and relieving drawbacks of batch processing. To achieve successful batch scale-up or robust continuous crystallization design, process intensification of unit operations, crystallization techniques, and utilizing data driven approaches are effective in designing optimal process parameters and achieving target quality attributes. </p> <p>In this thesis, a collection or toolbox of various process intensification techniques was developed to aid in control, optimization, and digitalization of crystallization processes. The first technique involves developing a novel control algorithm to control agrochemical crystals of high aspect ratio to improve the efficiency of downstream processes (filtration, washing, and drying). The second technique involves the further improvement of the first technique through digitalization of the crystallization process to perform simulated optimization and obtain a more nominal operating profile while reducing material consumption and experimentation time. The third method involves developing a calibration procedure and framework for in-line video microscopy. After a quick calibration, the in-line video microscopy can provide accurate real-time measurements to allow for future control capabilities and improve data scarcity in crystallization processes. The last technique addresses the need for polymorphic control and process longevity for continuous tubular crystallizers. Through a sequential stirred tank and tubular crystallizer experimental setup, the control of polymorphism, particle mean size, and size distribution were characterized. Each part of this thesis highlights the importance and benefits of process intensification by creating a wholistic process intensification framework coupled with novel equipment, array of PAT tools, feedback control, and model-based digital design.</p>