Kristen A. Severson

TL;DR: In this article, a machine learning method was used to predict battery lifetime before the onset of capacity degradation with high accuracy. But, the prediction often cannot be made unless a battery has already degraded significantly.

TL;DR: A closed-loop machine learning methodology of optimizing fast-charging protocols for lithium-ion batteries can identify high-lifetime charging protocols accurately and efficiently, considerably reducing the experimental time compared to simpler approaches.

TL;DR: In this paper, the authors provide some perspectives on progress in the design of process monitoring systems over the last twenty years and discuss the challenges in the field and opportunities for future research.

TL;DR: Challenges and opportunities are described for microscale technologies for high-speed continuous processing, plug-and-play modular unit operations with integrated monitoring and control systems, dynamic modeling of unit operations and entire biopharmaceutical manufacturing plants to support process development and plant-wide control, and model-based control technologies for optimizing startup, changeover, and shutdown.

TL;DR: Sensors (process analytical technology, PAT) and control strategies that enable RTRT for the spectrum of critical quality attributes (CQAs) in biopharmaceutical manufacturing are discussed.