Review on machine learning-based bioprocess optimization, monitoring, and control systems

Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bio-processing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.

Automated summary

Machine Learning is seen as a game changer for leveraging big data in the bioprocessing industry. However, complex datasets, lagging sensor systems, and scalability issues hinder its real-time application. This review provides an understanding of machine learning and its complexities, discusses its relevance for bioprocess control, and explores the future prospects of integrating different modeling strategies and sensors in the bio-processing industry.