Modelling and Control for the Advancement of Biologics

本研究的目的是建立一个先进的控制策略，以操作一个流加式生物反应器。该提案将利用人工智能方法来确定一个过程模型，该模型将允许更明智的控制方法。目前，批次是使用基于历史成功的固定方案操作的。该行业的主要挑战之一是细胞对环境变化的敏感性，即使是轻微的偏差也可能导致废弃批次。这种方法是专门设计来保持一个健康的细胞环境，利用现有的知识，细胞生长的建模方法。控制器将明确地考虑干扰对细胞生长的影响，同时能够同时调整所有输入。为了建立这种方法，将进行试点实验，以收集建模数据，并最终，规模系统将与所提出的方法进行控制。这项工作将导致神经网络建模方法和模型预测控制算法，旨在最大限度地提高中国仓鼠卵巢细胞的生产。通过使生物反应器操作更准确且更少依赖历史数据，预计将为生物制药公司带来巨大利益。

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.