How can you monitor and manage fermentation in a bioreactor?

1 Online sensors

Online sensors are devices that measure and transmit various parameters of the fermentation broth in real-time, such as dissolved oxygen (DO), pH, temperature, biomass, carbon dioxide (CO2), and product concentration. Online sensors can help you to control the bioreactor environment, detect anomalies, and optimize the fermentation process. However, online sensors also have some limitations, such as accuracy, drift, fouling, calibration, and maintenance. Therefore, you should select online sensors that are suitable for your specific fermentation system, and validate their performance regularly.

2 Offline analysis

Offline analysis refers to the collection and measurement of samples from the bioreactor at specific time intervals, using laboratory techniques such as high-performance liquid chromatography (HPLC), gas chromatography (GC), or mass spectrometry (MS). Offline analysis can provide more detailed and accurate information about the fermentation broth composition, such as substrate consumption, product formation, by-products, and metabolites. Offline analysis can help you to verify the online sensor data, identify the metabolic pathways, and evaluate the fermentation efficiency. However, offline analysis also has some drawbacks, such as time delay, sample degradation, and human error. Therefore, you should choose offline analysis methods that are reliable, fast, and compatible with your fermentation system.

3 Data integration and analysis

Data integration and analysis is the process of combining and interpreting the online and offline data to obtain a comprehensive understanding of the fermentation process. Data integration and analysis can help you to monitor the fermentation kinetics, dynamics, and performance, and to identify the critical factors that affect the product quality and yield. Data integration and analysis can also help you to design and implement control strategies, such as feedback, feedforward, or adaptive control, to adjust the bioreactor parameters according to the fermentation state. However, data integration and analysis also requires advanced tools and skills, such as data mining, machine learning, artificial intelligence, or process modeling. Therefore, you should use data integration and analysis methods that are robust, scalable, and applicable to your fermentation system.

4 Process optimization and scale-up

Process optimization and scale-up is the goal of monitoring and managing fermentation in a bioreactor. Process optimization and scale-up aims to improve the product quality and yield, reduce the production cost and time, and increase the production capacity and reproducibility. Process optimization and scale-up can be achieved by applying various methods, such as experimental design, response surface methodology, evolutionary algorithms, or neural networks, to optimize the bioreactor parameters and operating conditions. However, process optimization and scale-up also faces many challenges, such as non-linearity, uncertainty, variability, and complexity of the fermentation process. Therefore, you should consider the trade-offs, constraints, and risks of process optimization and scale-up, and validate the results in different bioreactor scales and modes.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?