Modelling and Control for the Advancement of Biologics

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This research proposal is focused on building a novel advanced process control strategy to operate a fed-batch bioreactor. This proposal will utilize artificial intelligence methods to identify a process model that will allow for a more informed control approach. Currently, batches are operated using a fixed protocol based on historical successes. One of the main challenges in this industry is the sensitivity of cells to changes in the environment, where even slight deviations can result in a discarded batch. This approach is specifically designed to maintain a healthy cell environment by utilizing existing knowledge of cell growth in the modelling approach. The controller will explicitly account for the effects of disturbances to cell growth while being able to adjust all of the inputs simultaneously. To build this approach, pilot experiments will be conducted to collect data for modelling, and eventually, scale systems will be controlled with the proposed approach. This work will result in a neural network modelling approach and model predictive control algorithm designed to maximize the production of Chinese hamster ovary cells. It is expected to yield large benefits to the biopharmaceutical company by making bioreactor operation more accurate and less reliant on historical data.