Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 3Citation - Scopus: 3On Digital Twins in Bioprocessing: Opportunities and Limitations(Elsevier Ltd, 2025) Shariatifar, Mehrdad; Rizi, Mohammadsadegh Salimian; Sotudeh-Gharebagh, Rahmat; Zarghami, Reza; Mostoufi, NavidIntegrating 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.Review Citation - WoS: 9Citation - Scopus: 12Readiness and Maturity Models for Industry 4.0: a Systematic Literature Review(John Wiley and Sons Ltd, 2023) Ünlü, H.; Demirörs, O.; Garousi, V.Industry 4.0 changes traditional manufacturing relationships from isolated optimized cells to fully integrated data and product flows across borders with its technological pillars. However, the transition to Industry 4.0 is not a straightforward journey in which organizations need assistance. A well-known approach that can be utilized during the early phases of the transition is to assess the capability of the organization. Maturity models are frequently used to improve capability. In this systematic literature review (SLR), we analyzed 22 maturity and readiness models based on 10 criteria: year, type, focus, structure, research methodology followed during the design of models, base frameworks, tool support, community support, objectivity, and extent of usage in practice. Our SLR provides a well-defined comparison for organizations to choose and apply available models. This SLR showed that (1) there is no widely accepted maturity/readiness model for Industry 4.0, as well as no international standard; (2) only a few models have received positive feedback from the industry, whereas most do not provide any practical usage information; and (3) the objectivity of the assessment method is controversial in most of the models. We have also identified a number of issues as open research areas for assessing readiness and maturity models for Industry 4.0. © 2023 John Wiley & Sons, Ltd.