Hello Everyone,
I’m the workstream lead for NIIMBL’s BD5 (Big Data) program for downstream process modeling. For FY2027 we’re drafting a request for proposal (RFP) to implement hybrid parameter fitting and process optimization into CADET. Here’s a short summary of the proposal:
This request for proposal entails the development of a hybrid machine learning–mechanistic modeling framework for automated parameterization and optimization of chromatographic unit operations within the CADET simulation environment. The framework integrates physics-informed neural network architectures to provide rapid initial estimates of mechanistic model parameters — including adsorption equilibrium constants, mass transfer coefficients, and dispersion terms — from either experimental chromatographic data or molecular and resin descriptors. These data-driven parameter estimates serve as initializations for a hybrid refinement loop in which CADET’s forward mechanistic simulator is leveraged iteratively to minimize residuals between predicted and observed chromatograms via gradient-based or gradient-free optimization.
Upon model validation, a weighted multi-objective cost function is employed to optimize controllable process variables. The framework is designed to be modality-agnostic and is intended to close the existing gap between parameter estimation and process optimization in a single, accessible, open-source platform.
The RFP will be open globally in the next week or so and close by July 10th. Don’t hesitate to reach out if you’re interested.
-M. Rosario Cervellere,