🔬 The model considers factors such as nonideal mixing, biofilm resistance, and variations in injection volumes, flow rates, tracer concentrations, and filter surface areas.
🧪 Using sodium nitrate as an inert tracer, the researchers validated the model through various experiments.
💯 The model accurately characterizes filter performance and can reduce the number of experiments required.
🔬 RTD measurements can be used for traceability, defining batch definitions, timing start-up and shut-down, and determining sampling frequency.
Introduction:
Scientists at BOKU University in Vienna have developed a mathematical model that can predict the resident time distribution (RTD) in continuous manufacturing processes for biologics. The model takes into account factors such as nonideal mixing, biofilm resistance, and variations in injection volumes, flow rates, tracer concentrations, and filter surface areas. The accuracy of the model was validated through experiments, achieving a coefficient of determination of at least 0.97. The researchers believe that this model can reduce the number of experiments required and provide a cost-effective and nontoxic alternative for studying material traceability and quality in continuous manufacturing.
- A mathematical model has been developed to predict the resident time distribution (RTD) in continuous manufacturing processes for biologics.
- The model considers factors such as nonideal mixing, biofilm resistance, and variations in injection volumes, flow rates, tracer concentrations, and filter surface areas.
- Experiments were conducted to validate the accuracy of the model, achieving a coefficient of determination of at least 0.97.
- The model can reduce the number of experiments required and provide a cost-effective and nontoxic alternative for studying material traceability and quality in continuous manufacturing.
Conclusion:
The development of a mathematical model to predict the RTD in continuous manufacturing processes for biologics is a significant advancement. This model provides a cost-effective and nontoxic alternative to traditional experimental methods and can reduce the number of experiments required. By considering various factors and variations in the process, the model can accurately characterize filter performance and improve our understanding of material traceability and quality in continuous manufacturing. This model has the potential to enhance efficiency and quality control in the biologics manufacturing industry.
