(233aw) Feasibility Analysis of Flowsheet Models in Continuous Pharmaceutical Manufacturing Processes Considering the Effects of Noise
- Conference: AIChE Annual Meeting
- Year: 2016
- Proceeding: 2016 AIChE Annual Meeting
- Group: Pharmaceutical Discovery, Development and Manufacturing Forum
- Time: Monday, November 14, 2016 - 3:15pm-5:45pm
Recently, extensive modeling work has been done to simulate the dynamic behaviors of unit operations, based on which integrated flowsheet models can be built to predict the process output under a certain set of input values [3, 5]. Such models can not only correlate the materials properties (e.g. flow rate, powder bulk density, mixture concentration, etc.) and operating conditions (e.g. blade speed, turret speed, etc.) to the steady-state process response (mass hold-up, product properties), but also dynamically trace the variation in any input factors and its propagation through the subsequent unit operations. Such information can be useful for risk assessment and sensitivity analysis of the process. Following the requirement from Quality by Design (QbD) , it is naturally to consider how to apply the predictability of flowsheet models to the quantification and optimization of the design space of the manufacturing process, which is known as â??feasibility analysis problemsâ? in the PSE community.
Feasibility analysis is to identify the feasible region where the process can meet all operating, quality, and product constraints under uncertainty [6, 7]. This is important to help understand how much variation the process can withstand while maintaining operability. However, the feasibility analysis problem can be very difficult to solve because of the complexity of the flowsheet model, which may include hundreds of variables and constraints. Moreover, the computational cost for the simulation can be high, which requires a highly efficient sampling strategy for the analysis. In this presentation, we focus on using surrogate-based approaches (e.g. Kriging, Radial Basis Function, etc.) to efficiently approximate the computational expensive flowsheet model and predict the feasible region under uncertainty. More specifically, we apply this approach not only to deterministic models, but also to models that contain noise in the output prediction. The objective is to provide a framework of feasibility analysis for flowsheet models that contain various sources of uncertainty.
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