A Digital Twin for Process Optimisation in Pharmaceutical Manufacturing

Gerogiorgis, Dimitrios I.; Castro-Rodriguez, Daniel

doi:10.1016/b978-0-323-88506-5.50041-3

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…Digital Twins in the area of chemistry refer to the development of comprehensive virtual models that accurately replicate chemical reactions, processes or systems. [58][59][60][61][62][63][64] These digital models are designed to simulate the physical and chemical behaviors of their real-world counterparts in real time, integrating data from various sources, including sensors, operational data, and collected performance records. The concept is based on the convergence of the physical and digital worlds, where every aspect of a chemical process or system is mirrored in a virtual model, allowing for indepth analysis, prediction, and decision-making.…”

Section: Digital Twinsmentioning

confidence: 99%

Top 20 Influential AI-Based Technologies in Chemistry

Ananikov

2024

Preprint

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The beginning and ripening of digital chemistry is analyzed focusing on the role of artificial intelligence (AI) in an expected leap in chemical sciences to bring this area to the next evolutionary level. The analytic description selects and highlights the top 20 AI-based technologies and 7 broader themes that are reshaping the field. It underscores the integration of digital tools such as machine learning, big data, digital twins, the Internet of Things (IoT), robotic platforms, smart control of chemical processes, virtual reality and blockchain, among many others, in enhancing research methods, educational approaches, and industrial practices in chemistry. The significance of this study lies in its focused overview of how these digital innovations foster a more efficient, sustainable, and innovative future in chemical sciences. This article not only illustrates the transformative impact of these technologies but also draws new pathways in chemistry, offering a broad appeal to researchers, educators, and industry professionals to embrace these advancements for addressing contemporary challenges in the field.

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Section: Digital Twinsmentioning

confidence: 99%

Top 20 Influential AI-Based Technologies in Chemistry

Ananikov

2024

Preprint

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“…Although DT and CPS can offer numerous advantageous features to the economic performance of a system, the cost associated with the implementation of these data-driven technologies is often overlooked. For example, numerous past studies that have conducted TEA on DT (Xu et al, 2019;Gerogiorgis and Castro-Rodriguez, 2021;Koulouris et al, 2021) and CPS (Zhao et al, 2019) incorporated only processes, taking into account the cost associated with production processes (i.e., equipment cost, contractor's fee, etc. ), while ignoring the cost required to set-up these smart technologies.…”

Section: Cloud Computingmentioning

confidence: 99%

Future era of techno-economic analysis: Insights from review

et al. 2022

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Techno-economic analysis (TEA) has been considered an important tool to evaluate the economic performance of industrial processes. Recently, the application of TEA has been observed to have exponential growth due to the increasing competition among businesses across various industries. Thus, this review presents a deliberate overview of TEA to inculcate the importance and relevance of TEA. To further support the aforementioned points, this review article starts with a bibliometric analysis to evaluate the applicability of TEA within the research community. Conventional TEA is widely known to be conducted via software modeling (i.e., Python, AMIS, MATLAB, Aspen HYSYS, Aspen Plus, HOMER Pro, FORTRAN, R, SysML and Microsoft Excel) without involving any correlation or optimization between the process and economic performance. Apart from that, due to the arrival of the industrial revolution (IR) 4.0, industrial processes are being revolutionized into smart industries. Thus, to retain the integrity of TEA, a similar evolution to smart industries is deemed necessary. Studies have begun to incorporate data-driven technologies (i.e., artificial intelligence (AI) and blockchain) into TEA to effectively optimize both processes and economic parameters simultaneously. With this, this review explores the integration of data-driven technologies in the TEA framework. From literature reviews, it was found that genetic algorithm (GA) is the most applied data-driven technology in TEA, while the applications of blockchain, machine learning (ML), and artificial neural network (ANN) in TEA are still considerably scarce. Not to mention other advanced technologies, such as cyber-physical systems (CPS), IoT, cloud computing, big data analytics, digital twin (DT), and metaverse are yet to be incorporated into the existing TEA. The inclusion of set-up costs for the aforementioned technologies is also crucial for accurate TEA representation of smart industries deployment. Overall, this review serves as a reference note for future process engineers and industry stakeholders who wish to perform relevant TEA, which is capable to cover the new state-of-art elements under the new modern era.

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“…Models are constructed using mathematical equations that accurately represent physical and biological aspects of the process, which significantly enhances comprehension [8,9]. Therefore, there is a need for rapid and precise methods to determine model parameters with minimal effort [10,11].…”

Section: Introductionmentioning

confidence: 99%

Automation in Analytical Chemistry: The Role of Ai in Chromatography

KALPANA,

PRASAD

2024

Int J App Pharm

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Artificial Intelligence (AI) has facilitated significant breakthroughs in drug discovery, the design of materials, and organic synthesis. The advancements in the latter group are especially remarkable due to the abilities of the latest computational methods (molecular design algorithms) that enable the exploration of extensive chemical spaces and enhance research in fields such as predicting molecule properties, designing molecules, retrosynthesis, predicting reaction conditions, and predicting reaction outcomes. A literary review was conducted following PRISMA guidelines. This study aimed to review existing data on the application of AI in separation chromatography. The evolution and utilization of AI in the pharmaceutical industry and its future aspects were articulated in this study. The utilization of AI can completely transform the field of chromatography analysis by facilitating expedited, more precise, and more effective data processing. By automating chromatography analysis, AI can enhance efficiency and minimize the potential for human mistakes. This advancement enables scientists to dedicate their efforts towards addressing intricate and demanding analytical issues. With the evolution of technology and the increasing adoption, we can anticipate more progress in chromatography analysis and analytical chemistry.

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A Digital Twin for Process Optimisation in Pharmaceutical Manufacturing

Cited by 10 publications

References 15 publications

Top 20 Influential AI-Based Technologies in Chemistry

Top 20 Influential AI-Based Technologies in Chemistry

Future era of techno-economic analysis: Insights from review

Automation in Analytical Chemistry: The Role of Ai in Chromatography

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