Bioprocess Modelling using AI and Real-Time Monitoring
At the intersection of synthetic biology, artificial intelligence, and process engineering, our research group at UCL Biochemical Engineering is pioneering advancements in bioprocess modelling. Our multi-disciplinary team employs different modelling approaches, including state-of-the-art AI algorithms and real-time monitoring technologies with some our UK and European partners to optimize and control biological processes. The aim is to revolutionize how we understand, predict, and manipulate bioprocesses, making them more efficient, scalable, and sustainable.
Bioprocesses are inherently complex, governed by intricate biological and physical principles. Traditional methods for bioprocess optimization often fall short due to their inability to deal with such complexity and variability. By integrating AI and real-time monitoring, we aim to transcend these limitations, providing precise control and adaptability. This has profound implications for industrial biotechnology, pharmaceuticals, and beyond.
Techniques and Tools
Our toolbox is a blend of cutting-edge computational and experimental approaches:
- Machine Learning Algorithms: These include supervised and unsupervised learning, as well as reinforcement learning techniques to develop predictive models for bioprocess optimization.
- Real-Time Monitoring Systems: Utilization of sensors and analytical devices to continuously monitor key process parameters such as pH, temperature, and metabolite concentrations.
- Data Analytics: Advanced data analytics and statistical methods for real-time interpretation of complex biological data.
- Cloud Computing: Harnessing the scalability and flexibility of cloud computing for data storage and computational power.
Our research has wide-ranging applications across various domains:
- Biofuel Production: Enhanced control mechanisms for microbial cultures involved in the conversion of waste to biofuels.
- Pharmaceutical Manufacturing: Real-time optimization of fermentation processes for drug production.
- Food and Beverage Industry: Intelligent monitoring systems for quality control and waste reduction.
- Environmental Monitoring: Real-time tracking of biological treatment processes in wastewater treatment plants.