🔬 Nadav Bar, a chemical engineering professor, will discuss the use of Model Predictive Control (MPC) at a conference.
📊 MPC uses algorithms to optimize processes and minimize resources while maximizing product output.
💡 The team uses measurements of chemical compounds to ensure the balance of the bioprocess.
💻 They have connected an MPC model to a machine learning algorithm to deal with unexpected data.
⭐️ This approach allows for better control and optimization of bioprocesses, improving productivity and efficiency.
Introduction:
This article discusses the use of algorithms, specifically Model Predictive Control (MPC), in optimizing bioprocesses. The article highlights a presentation by Nadav Bar, PhD, professor of chemical engineering at the Norwegian University of Science and Technology, who explains how algorithms can maximize the production of a product while minimizing resources in microbial bioprocesses. The article emphasizes the need for real-time feedback and the use of advanced instruments to provide accurate measurements for the algorithm. Bar also mentions the incorporation of machine learning algorithms to update and improve the model.
- Algorithms, specifically Model Predictive Control (MPC), can optimize bioprocesses by maximizing product production and minimizing resource usage.
- MPC uses measurements of chemical compounds to determine the balance of the bioprocess.
- The mathematical model behind the MPC algorithm is kept simple and requires rapid feedback to adjust and improve predictions.
- Real-time measurements and advanced instruments are used to provide accurate data for the algorithm.
- The model can be adjusted to handle complex sugars and handle unreliable or noisy data.
Conclusion:
The use of algorithms, specifically Model Predictive Control, in optimizing bioprocesses can lead to the maximization of product production while minimizing resource usage. Real-time feedback and advanced instruments play a crucial role in providing accurate measurements for the algorithm. The integration of machine learning algorithms further improves the model’s ability to adapt to unexpected data. This approach opens up new possibilities for optimizing bioprocesses and improving efficiency in biological manufacturing.
