Real-Time Cell Growth Control Using a Lactate-Based Model Predictive Controller

Providing a cost-efficient feeding strategy for cell expansion processes remains challenging task due to, among other factors, donor variability. The current method to use fixed medium replacement all batches results often in either over- or underfeeding these cells. In order take into account the individual needs of cells, model predictive controller was developed this work. Reference experiments were performed by expanding human periosteum derived progenitor cells (hPDCs) tissue flasks acquire reference data. With data, time-variant prediction identified describe relation between accumulated replaced as control input and lactate produced process output. Several forecast methods predict growth designed using multiple collected datasets applying transfer function models machine learning. first experiment values from static target over time, resulting second used time-adaptive combining data well measured real-time without