The need for advanced control strategies for bioprocessing and biomanufacturing is growing, and several manufacturers already have leveraged new automation and software solutions for bioprocess monitoring and control. Authors discuss new approaches for process control that make use of increased computational power; advanced sensors, probes, and sampling technologies; and new software systems that can be applied to a wide range of operational modes (e.g., perfusion, continuous, and multicolumn platforms). Such technologies can be applied with single-use systems and within PAT and “machine learning” applications.

Introduction: Advanced Control Strategies at Biotech Week Boston
BioProcess International’s managing editor, Maribel Rios, reviews the advanced control sessions from the BioProcess International Conference and Exhibition, part of Biotech Week Boston 2017.
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Integrated PAT Automated Feedback Control of Critical Process Parameters Using Modern In Situ Analytics
New bioprocess market dynamics and modalities are driving automation forward. A number of technologies and adaptations are now available for bioprocessing that are designed to help enable automation and enable online analytics for real-time monitoring. Measuring critical process parameters with highly specific and scalable technologies that are adaptable to all bioreactor formats creates a strong foundation for advanced automation in bioprocessing.
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Accelerating Process Development Through Flexible Automated Workflows
Hardware for laboratory automation is exceptionally good, but control over it can be complex and time consuming. The author describes a software solution designed “to bring together biology and the digital world.” The technology helps make precise control over laboratory equipment intuitive, helps users perform complicated experiments, and enables full digital integration.
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Application of Model Predictive Control Methods for Forecasting and Optimization of Biological Processes
The automation hierarchy in biomanufacturing consists of a regulatory layer, process analytical technology (PAT) layer, and (potentially) a top-level model-predictive or supervisory layer. Moving from PAT to supervisory control with model predictive control goes beyond process capability and into product quality and process optimization. The author describes approaches to moving from descriptive and diagnostic analytics to predictive analytics.
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CO2, O2, and Biomass Monitoring in Escherichia coli Shake-Flask Culture Following Glucose–Glycerin Diauxie Online
In microbial cultures, carbon dioxide (CO2) can inhibit or stimulate growth under certain conditions. The authors monitored Escherichia coli diauxie growth phases online and focused on dissolved CO2 (dCO2) and oxygen readings. They also determined pH, substrate concentrations, and OD420 offline and compared oxygen and CO2 levels to measurements in identical conditions taken with a gas analyzer for shake flasks.
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