Review on machine learning-based bioprocess optimization, monitoring, and control systems. (February 2023) - Mondal, Partha Pratim; Galodha, Abhinav; Verma, Vishal Kumar; Singh, Vijai; Show, Pau Loke; Awasthi, Mukesh Kumar; Lall, Brejesh; Anees, Sanya; Pollmann, Katrin; Jain, Rohan
Graphical abstract: Highlights: A facile conceptual introduction to the basics of ML for bioprocessing applications. Exploration of ML algorithms in Pharmaceutical, biofuel, and wastewater treatment. Hybrid model for multi-dimensional aspects & better prediction capabilities. ANN, GA, RSM, and SVM models dominate biofuel and water treatment. PLS and ANN are the most used algorithm in biopharmaceutical production. Abstract: 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 bioprocessing 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.