Automating Fermentation and Paperless Operations

by Jim Cahill

At the recent Interphex Pharmaceutical Manufacturing Conference, Emerson's Todd Ham presented on the subject of automating fermentation. Todd acknowledged that Christie Deitz, whom we've featured in several other posts, had a large hand in the development of this presentation and work on the project discussed.

The presentation discussed a recent project done on a large-scale, multi-product biopharmaceutical complex. This project was so successful it recently won the Facility of the Year Award Winner in Project Execution. One of the keys to success was a clear design philosophy established up front. Elements of this philosophy included:

  • Fully automated
  • Paperless, dock-to-dock using electronic records, operator handheld devices, and barcode scanning
  • Consistency for operators based on industry standards like ISA-88 (S88), ISA-95 (S95), and digital bus technologies
  • Focus on fermentation as a key process area for the project

A key to success in the project was the close working relationship between the manufacturer and the Emerson Life Sciences project team on the up front requirements and design, and the subsequent module-level and integration-level testing.

The upfront design considered not only the fermentation and recovery processes, but also the full automation required for paperless operations. This design included recipe-level batch control, warehouse management, electronic signatures, and a complete electronic batch record, including the manual processes. These manufacturing processes included material management, container management, filter management and sampling.

The project team applied the S88 standard to control modules looking to identify the common modules and instances for things like motors and valves. At the S88 equipment module level, the team created project wide module templates, area specific module templates, and unique, one-time use equipment modules.

The sampling system and sparger control are examples of project-wide templates. Fermentation agitator control and dissolved oxygen control are examples of area-specific equipment modules. Transfer panels and valve assemblies are examples of unique equipment modules.

At the S88 unit level, the team designed classes and instances based on physical similarity and phases that they use such as batch media, inoculate, ferment, etc. This led to various unit classes for fermentation vessels including seed fermenters, production fermenters, and feed vessels.

From a recipe standpoint, the design grouped phases into operations, then grouped operations into unit procedures, and finally grouped unit procedures into procedures, all again following the S88 standard.

Todd shared some lessons learned from the team. With regard to the modular design approach, the team learned to keep process units the same as much as possible. With similar units, it is also important to make sure the operations are also as uniform as possible. The team cautioned about the overuse of aliases, which reference pieces of physical equipment like valves and motors, in phase logic. By not overusing aliases, but rather relying on equipment modules to handle physical differences, the phase logic could be generically written to handle multiple pieces of similar equipment like process tanks.

Other lessons learned were to plan for the extra documentation required for high levels of modularity and dock-to-dock automation. Like other members of the Life Sciences team have counseled in earlier posts, time spent upfront in planning and testing saves a lot of project backend effort.

The benefits of a complete electronic batch record vs. a paper-based process in terms of faster release of products are pretty clear. It's important to assemble the project team and begin the planning and design early to prepare for the additional effort commensurate with the increased automation required for a successful project.

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May 2, 2007 in Fermentation, in Life Sciences, in Project Services | Comments (0)

Greg McMillan’s Thoughts on PAT and Advanced Control Technology

by Jim Cahill

Recently I discovered in my PAT RSS persistent search feed an article in Pharmaceutical Processing magazine entitled, PAT Solutions-Eight advanced process control technologies worth considering. This article was written by Rick Rys, President, at R2 Controls, and Janice Abel, Director, Global Pharmaceutical and Biotech Industries, at Invensys.

Since ModelingAndControl.com's Greg McMillan recently co-authored a book New Directions in Bioprocess Modeling and Control-Maximizing Process Analytical Technology Benefits and recently had an article published entitled Maximizing PAT Benefits from Bioprocess Modeling and Control in the November 2006 issue of Pharmaceutical Technology IT Innovations, I had to ask for his thoughts.

Greg sent me a great email which I'll pass along with my edits to insert hyperlinks:

The DeltaV systems offers the advanced control technologies mentioned in the PAT article, such as synthetic analyzers, feedforward and predictive control, dead time compensation, and model predictive control in its standard integrated graphical configuration studio that uses Fieldbus function blocks. The synthetic analyzers not only include online regression models such as Neural Networks but also embedded first principal models. Furthermore, innovative analytics, control systems, and models can be prototyped faster than real time in a virtual plant on a desktop or laptop PC anywhere. The virtual plant uses an exact duplicate rather than an emulation or simulation of the control system in the control room. Advanced technologies in the virtual plant can be developed and tested from the high speed play back of historical data from existing systems used to automate bench top fermentors. This includes a new adaptive control technology that identifies process dynamics and indicates the relative improvement possible from better control. The high speed virtual experimentation capability of the virtual plant is a key feature and may be the only way to provide enough historical data particularly on "what if' scenarios since a fermentor batch for most new bioprocesses takes 14-17 days.

The same virtual plant can be used for education of operations and technical support by the dynamic restore and high speed playback of instructive periods of operation.

The technologies can be connected to the bench top system for evaluation, verification, and adaptation of models early on in the commercialization process.

The uses and advantages of the synergistic environment of the virtual plant are explored in the book New Directions in Bioprocess Modeling and Control, the article "Maximizing PAT Benefits from Bioprocess Modeling and Control" in the November 2006 issue of Pharmaceutical Technology IT Innovations, and in the lectures on the Control Insights website. The important practical implications of the extremely slow one direction integrating response of biomass and product concentration on modeling and control are also discussed in the book, article, and website and on the companion Modeling and Control blog site.

The integration and knowledge management of a diversity of technologies in DeltaV addresses the essence of the PAT initiative as expressed in the following statements by the FDA:

Process Analytical Technology:

  • It is important to note that the term analytical in PAT is viewed broadly to include chemical, physical, microbiological, mathematical, and risk analysis conducted in an integrated manner.

Process Analytical Technology Tools:

  • Multivariate data acquisition and analysis tools
  • Process and endpoint monitoring and control tools
  • Continuous improvement and knowledge management tools
  • An appropriate combination of some, or all, of these tools may be applicable to a single-unit operation, or to an entire manufacturing process and its quality assurance.

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April 19, 2007 in Control Strategies, in Fermentation, in Life Sciences | Comments (0)

Virtual Experimentation Helps Implement Online Batch Analytics and MPC

by Jim Cahill

Pharmaceutical Technology Europe has a recent article entitled, Artificial intelligence the key to process understanding. It discusses the opportunity to enhance the FDA's Process Analytical Technologies (PAT) initiative using artificial intelligence based tools like neural networks, fuzzy logic and genetic algorithms. I shared this article with Greg McMillan who has been quite immersed with advanced control as it applies to bioprocesses.

I received this response which I'll share in total (I've inserted some context-sensitive hyperlinks to his work on Process Control Insights):

There are opportunities to improve plant performance in the front end of the process where most of the product qualities are set by the use of online process models, batch analytics, and Model Predictive Control (MPC). Online process models based on first principals offer a significant source of knowledge discovery for both the process and the control system. The models are part of a virtual plant that enables virtual experimentation for the exploration of "what if scenarios".

This is important for the next steps of implementing online batch analytics and MPC. Since fermentation batches take days to weeks to complete and the cost of wasted batches is considerable, the virtual plant can provide data on various degrees of adverse operating conditions that would be infeasible to obtain from the actual plant in terms of time and cost.

The virtual plant facilitates the development of techniques for the proper unfolding and alignment of batch data and more advanced analysis techniques such as super model based Principal Component Analysis. Neural networks can be employed to provide reaction rates when information on the kinetics is insufficient.

Fuzzy logic rules can be formularized and tested for a wide variety of scenarios. Inferential measurements can be developed for viable mass growth rates and product formation rates to fill in the blanks between lab measurements for MPC applications to improve batch consistency and yield and to reduce batch cycle time.

In summary, the virtual plant offers a synergistic environment for the application of online batch analytics, artificial intelligence, and advanced control. These opportunities and others are discussed in the book New Directions in Bioprocess Modeling and Control published and in the lectures on the Process Control Insights website.

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January 31, 2007 in Fermentation, in Life Sciences, in Process Optimization, in Simulation | Comments (0) | Trackback (0)