On Digital Twins in Bioprocessing: Opportunities and Limitations

Abstract

Integrating Digital Twins (DTs) in bioprocessing has become a prominent focus within the industry. Despite the challenges associated with implementing this technology in the field, the bioprocessing sector is interested in utilizing it. This is due to its potential to enhance process efficiency and overall profitability. The adoption of DTs is driven by the prospect of online monitoring, control, and optimization, enabling the products with precise and desired characteristics. To realize this objective, researchers propose a novel strategy for implementing DTs in bioprocessing. This involves the development of a hybrid model that combines first principal models and Machine Learning (ML) algorithms. This approach effectively addresses the limitations of previous methods and establishes a closed control loop system, continuously monitoring the system and adjusting input variables to achieve optimal outcomes. This study comprehensively explores various aspects of DTs. Firstly, it discusses the concept and characteristics of DTs, along with an examination of the advantages and challenges associated with their implementation. Secondly, it comprehensively analyzes key factors that directly influence DT implementation, including sensors, data collection, and models. Thirdly, it reviews the implications of Digital Solutions (DS) and DT in downstream and upstream bioprocessing. By providing theories, case studies, and practical frameworks, this work seeks to motivate both researchers and industry practitioners to adopt DT methodologies, thereby facilitating the emergence of enhanced precision, operational efficiency, and economic viability within biomanufacturing.