AI-generated from publicly available materials.Recent insights from biotechnology expert William Whitford highlight the potential of in silico AI-augmented models, termed digital shadows, to facilitate process intensification in manufacturing without the regulatory complexities associated with active control systems.
Whitford, the founder of Oamaru BioSystems, differentiates digital shadows from digital twins. While both serve as real-time digital representations of processes or equipment, digital shadows focus solely on reporting rather than controlling operations. This distinction simplifies their implementation, making them particularly appealing for companies that wish to avoid regulatory hurdles linked to active process management.
The utility of digital shadows lies in their ability to manage and interpret vast, intricate datasets generated by modern bioprocess analytics. Whitford emphasizes that machine learning techniques are adept at handling the complexity and high dimensionality of such data, which can encompass time series, imaging, and historical information from process development. For instance, by analyzing gas levels in a bioreactor, companies can infer metabolic activity and viable cell counts, insights that simpler analytical methods might miss.
By integrating various sources of data—including process measurements and contextual production information—digital shadows can enhance manufacturers' efficiency, maximizing output per unit of space or time. Whitford advocates for increased awareness of these tools, suggesting they can significantly support efforts in process intensification, ultimately leading to more productive manufacturing environments.