Machine-learning for biopharmaceutical batch process monitoring with limited data
Machine-learning for biopharmaceutical batch process monitoring with limited data is a scholarly work, published in 2018 in ''IFAC Proceedings Volumes''. The main subjects of the publication include probabilistic logic, parametric statistics, batch processing, data mining, artificial intelligence, computer science, process, model predictive control, fault detection and isolation, statistical process control, machine learning, biopharmaceutical, and Bayesian probability. The authors propose an approach to transition from a Low-N scenario to a Large-N scenario by generating an arbitrarily large number of in silico batch data sets.