Artificial Intelligence in Bioprocess Control and Optimization

Industrial bioprocesses play a crucial role in producing biofuels, materials, and healthcare products. However, challenges such as low bioconversion rates and productivity limitations persist. The transfer from lab to industrial scale, known as the "scale-up effect," introduces additional complexities. Over the past decades, strides have been made in optimizing bioprocesses, utilizing techniques like orthogonal experimental design (OED) and response surface methodology (RSM). Despite these advancements, there is a growing need for more sophisticated control and optimization methods. In this context, artificial intelligence (AI) has emerged as a promising solution, offering data-driven approaches independent of complex mathematical models.

A review authored by members of Jiangnan University, China, published in February 2023, looks at how AI can revolutionise the bioprocess industry, and offers theoretical guidance on how to incorporate AI into bioprocess optimization.

AI-Guided Modeling and Optimization of Bioprocess

To address the intricate nature of bioprocesses, AI-guided modeling approaches have gained prominence. These approaches, whether theory-driven or data-driven, provide insights into critical process parameters (CPPs). Classical methods like OED and RSM have been effective, but AI introduces new dimensions such as machine learning algorithms, where artificial neural networks (ANN) or particle swarm optimization (PSO), can be applied to model and optimize various bioprocesses. These technologies offer a more nuanced understanding of the nonlinear relationships between variables.

Rapid Detection Based on Spectroscopy and Image

Recent advancements in spectroscopy, particularly near-infrared reflectance spectroscopy (NIRS), have enabled rapid detection in bioprocesses. Spectroscopy proves convenient and accurate, utilizing overtones and combination bands of functional groups for qualitative and quantitative analysis. Image recognition technology further aids in the swift assessment of compost maturity, showcasing the potential of AI-assisted rapid detection and monitoring technologies. 

AI-Guided Bioprocess Control

Effective control strategies are essential for optimizing the yield and productivity of bioprocesses. Unlike chemical processes, bioprocesses exhibit high time scale variability due to factors like inoculation size, seed age, and culture conditions. AI technologies, such as machine vision and soft sensors, have been instrumental in developing smart control strategies that adapt to these dynamic conditions, enhancing the precision of bioprocess control.

Challenges and Perspectives

While AI technologies offer significant promise in bioprocess optimization and control, challenges and research gaps certainly exist. Obtaining relevant data remains a crucial hurdle, demanding effective strategies for data acquisition. Additionally, there is a need for more comprehensive industrial applications of advanced control strategies, like adaptive control and model-based control. The authors suggest that addressing these challenges will be pivotal in realizing the full potential of AI in bioprocess optimization and control.

In the review, the Jiangnan University author’s indicate the transformative role AI will likely have in advancing bioprocess control and optimization. Technologies such as ANN, SVM, FL, GA, and PSO have, and continue to, mature, offering valuable insights and improvements. The integration of machine vision, spectroscopy, and soft sensors enhances monitoring accuracy, while smart control strategies adapt to the dynamic nature of bioprocesses. As the bio-based economy continues to evolve, the judicious application of AI technologies holds immense promise for unlocking the full metabolic potential of microorganisms in industrial bioprocesses.