(158a) Modular Design and Uncertainty Evaluation for Flexible Continuous Pharmaceutical Manufacturing

The pharmaceutical industry is under increasing pressure to reduce costs, operate sustainably, comply with stringent regulations, and respond to rapidly evolving product portfolios and fluctuating demand driven by therapeutic needs and market dynamics. Although continuous manufacturing has demonstrated advantages over traditional batch processes, conventional continuous plants often lack the flexibility required to respond to variable demand. Modular plant concepts, using standardized and interchangeable small-scale process units, have emerged as a promising solution to address this gap. These modular systems enable faster adaptation to different products and production scales through reconfiguration, such as exchanging or numbering up units.

This study introduces a conceptual framework for modular continuous process design and evaluation under demand uncertainty. The framework consists of four elements: 1) definition of standardized process modules, 2) process flowsheet design using a series reactor arrangement, 3) sensitivity analysis for reactor utilization decisions, and 4) uncertainty analysis via Monte Carlo simulations to assess system adaptability.

We demonstrate the framework through a case study on the continuous production of ibuprofen using a two-step catalytic reaction involving hydrogenation and carbonylation. A modular reactor configuration is implemented using multiple small plug flow reactors (PFRs) in series, with the capability to selectively activate the reactors via bypassing strategies. This flexible setup allows dynamic adjustment of the number of operating reactors without disrupting continuous flow.

Simulations of both conventional and modular manufacturing configurations were carried out using the AVEVA process simulator. The model incorporates upstream and downstream steps, reaction kinetics, and appropriate thermodynamic models. Python scripting is integrated with AVEVA to automate Monte Carlo simulations for fluctuating market demands. Sensitivity analysis guided the development of operational strategies by identifying the impact of varying demands on key performance indicators.

This work highlights the benefits of modularity in achieving operational flexibility and cost efficiency. While the use of reactors in series is a known approach, the integration of uncertainty analysis and dynamic reactor utilization strategy offers new perspectives for modular pharmaceutical process design. While reactor sizing is critical in conventional systems to maintain process performance, the modular approach removes this sensitivity by allowing the number of active reactors to be adjusted based on real-time demand and performance metrics. As a result, the modular configuration can maintain consistent process performance across a wide range of operating conditions without compromising economic outcomes. By minimizing unnecessary reactor operation and optimizing utility use, the modular configuration also supports sustainability objectives through reduced energy consumption and operational waste.

In conclusion, the framework enables scalable, flexible, and robust process design under demand uncertainty. The study illustrates how modular manufacturing can help meet shifting market demands, making it a valuable direction for future pharmaceutical manufacturing strategies.