Simulation-based techno-economic optimization of continuous ibuprofen manufacturing under uncertainty

This is the authors' abstract. We don't add key points for this paper.

Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such technologies, as well as to guide process development. We present a novel framework that integrates stochastic optimization with rigorous process simulation for uncertainty-aware techno-economic assessment. This framework combines conceptual process design and simulation, global sensitivity analysis, stochastic optimization, and economic evaluation under uncertainty in a unified workflow. We apply the framework to the continuous manufacturing of ibuprofen, which is predominantly manufactured through established batch processes, and benchmark its performance against a conventional deterministic optimization approach. While the deterministic optimization converges more rapidly to a lower levelized cost of production (LCOP), the stochastic approach provides a more conservative, robust solution, reflecting real-world process variability. The resulting optimized design remains economically competitive, with an LCOP below current market prices, while ensuring greater reliability under uncertainty. By capturing variability in economic parameters, our approach enhances decision-making and facilitates exploration of conceptual design performance under uncertainty. This comparison highlights the importance of integrating uncertainty quantification into technology shifts and strategic process development.