Computational resources are a major limitation for implementing simulation-based optimization in process design. In this work, we developed an efficient and robust computational framework for process design using surrogate functions, hybrid optimization algorithms and multiple software with complementary features for the process simulation. Our model process was the capture chromatography unit operation for monoclonal antibody (mAb) purification, an important step in biopharmaceutical manufacturing. 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 is robust, adaptable, and capable of providing the information needed for decision making in a 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. To reduce the computational time, 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. For the process optimization, we developed a hybrid method, which uses MATLAB built-in tools. We compared its performance against individual optimization algorithms for problems with integer, continuous, and mixed-integer variables.

Surrogate-based optimization of capture chromatography platforms that improve computational efficiency in techno-economic feasibility analyses

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