While machine learning (ML) has made significant contributions to the biopharmaceutical
field, its applications are still in the early stages in terms of providing direct support for quality …

Process models are a growing tool for pharmaceutical manufacturing process design and
control. The Industry 4.0 paradigm promises to increase the amount of data available to …

Abstract Model predictive control (MPC) is a well-established control methodology in
chemical engineering, but the increasing complexity of chemical processes necessitates the …

The biopharmaceutical industry continuously seeks to optimize the critical quality attributes
to maintain the reliability and cost‐effectiveness of its products. Such optimization demands …

Advanced process control in the biopharmaceutical industry often lacks real‐time
measurements due to resource constraints. Raman spectroscopy and Partial Least Squares …

While machine learning (ML) has made significant contributions to the biopharmaceutical
field, its applications are still in the early stages in terms of providing direct support for quality …

Protein production in the biopharmaceutical industry necessitates the utilization of multiple
analytical techniques and control methodologies to ensure both safety and consistency. To …

Recently, the advancement in process analytical technology and artificial intelligence (AI)
has enabled the generation of enormous culture data sets from biomanufacturing processes …

The biopharmaceutical industry is rapidly advancing, driven by the need for cutting‐edge
technologies to meet the growing demand for life‐saving treatments. In this context, Model …

Chinese hamster ovary (CHO) cells play an important role in the biopharmaceutical industry,
but their production efficiency requires enhancement to meet the growing market demands …