16 Reimagining perfusion bioreactors with artificial intelligence
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Hardik S. Shah
Abstract
Perfusion bioreactors, characterized by continuous media exchange, have emerged as a cornerstone in biopharmaceutical manufacturing. However, optimizing their performance necessitates precise control over numerous parameters, including nutrient feeding, pH, dissolved oxygen, and temperature. The complexity and dynamic nature of these systems demand sophisticated strategies for real-time monitoring, analysis, and control. Artificial intelligence (AI) may offer a promising solution to address these challenges by providing machine learning (ML)machine learning algorithms and real-time analytics for data-driven decision-making. Attempt has been made to explore the integration of AI into perfusion bioreactorperfusion bioreactor technology, focusing on its potential to enhance process efficiency, product quality, and overall system performance. AI can analyze vast amounts of process data to identify patterns, trends, and correlations by integrating machine learningML algorithms. This information can be used to develop predictive models for cell growth, metabolite production, and product formation, enabling proactive optimization of culture conditions. Furthermore, AI-powered control systems can adapt to changing process dynamics, ensuring optimal performance and reducing the risk of deviations from target set points. This chapter contains an insight to the application of AI in various aspects of perfusion bioreactorperfusion bioreactor technology, highlighting its potential to revolutionize biopharmaceutical manufacturing.
Abstract
Perfusion bioreactors, characterized by continuous media exchange, have emerged as a cornerstone in biopharmaceutical manufacturing. However, optimizing their performance necessitates precise control over numerous parameters, including nutrient feeding, pH, dissolved oxygen, and temperature. The complexity and dynamic nature of these systems demand sophisticated strategies for real-time monitoring, analysis, and control. Artificial intelligence (AI) may offer a promising solution to address these challenges by providing machine learning (ML)machine learning algorithms and real-time analytics for data-driven decision-making. Attempt has been made to explore the integration of AI into perfusion bioreactorperfusion bioreactor technology, focusing on its potential to enhance process efficiency, product quality, and overall system performance. AI can analyze vast amounts of process data to identify patterns, trends, and correlations by integrating machine learningML algorithms. This information can be used to develop predictive models for cell growth, metabolite production, and product formation, enabling proactive optimization of culture conditions. Furthermore, AI-powered control systems can adapt to changing process dynamics, ensuring optimal performance and reducing the risk of deviations from target set points. This chapter contains an insight to the application of AI in various aspects of perfusion bioreactorperfusion bioreactor technology, highlighting its potential to revolutionize biopharmaceutical manufacturing.
Chapters in this book
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
Chapters in this book
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
