(342e) Surrogate-Based Optimization for the Techno-Economic Feasibility Analysis of Membrane Capture Chromatography Platforms

Recently, we developed a computational framework to simulate and optimize the capture chromatography process for mAb purification, which was used to compare the performance of membrane and resin-based platforms. The process simulation incorporated dynamic models for affinity chromatography that were validated with experimental breakthrough curves. The results were integrated with an Intelligen SuperPro Designer process simulation for the evaluation of key performance indicators of the operations. The resulting simulation was robust, adaptable, and capable of providing the information needed for decision making in specific production context. Nevertheless, the use of multiple simulation blocks (including approximating nonlinear partial differential equations with finite elements to generate breakthrough curves) made this framework computationally expensive and difficult to optimize. Computational resources are a major limitation for implementing simulation-based optimization in process design. To reduce the computational time and make the framework more attractive for industrial applications, we created a library of breakthrough curves to generate surrogate functions used by the optimization algorithm. This strategy yielded accurate results with a 92% decrease in processing time. Additionally, the surrogate function can use experimental or simulated breakthrough data, an important advantage in situations where the available models cannot represent the system, experimental data can be easily obtained, or there are no resources for the simulation of dynamic systems. In this work, we present the newly development framework and used to optimize cost of goods and process time for the case study of a industrial-scale membrane-based capture chromatography platform.