Process analysis and optimization of continuous pharmaceutical manufacturing using flowsheet models

• Published in: Computers & chemical engineering Vol. 107; pp. 77 - 91
• Main Authors: Wang, Zilong, Escotet-Espinoza, M. Sebastian, Ierapetritou, Marianthi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 05.12.2017
• Subjects: Continuous pharmaceutical manufacturing; Design space; Flowsheet modeling; Optimization; Sensitivity analysis; Design space; Continuous pharmaceutical manufacturing; Sensitivity analysis; Optimization; Flowsheet modeling
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2017.02.030

•A systematic process analysis approach is proposed to enhance the process understanding of continuous pharmaceutical manufacturing using flowsheet models.•Sensitivity analysis is used to prioritize the model input according to the influence on the output.•Surrogate-based feasibility analysis is used to efficiently and accurately characterize the design space.•Simulation-based optimization is conducted to find the optimal operation conditions and refill strategy which results in minimum operation cost. Continuous manufacturing has attracted increasing research attention in the pharmaceutical industry within the last decade. Based on the extensive experimental studies, numerous modeling and computational approaches have been developed to capture the process information and make predictions. Moreover, flowsheet models have been built to simulate the dynamic behaviors of a plant-wide manufacturing process with respect to different process input factors. However, there still lacks a systematic way to make the best use of flowsheet models in pharmaceutical processes. In this work, we propose a framework of process analysis and optimization for the continuous pharmaceutical manufacturing process where flowsheet models are available. Specifically, sensitivity analysis is conducted to identify the input factors that are most influential on the output; feasibility analysis is then implemented to characterize the design space in the high-dimensional space. Finally, process optimization is performed to find the optimal operation conditions that result in the minimum cost.