Greg: The job of the process controller is to transfer variability from the process variable (PV) to the manipulated variables (MV), inputs to the process, which in this case is air, oxygen, and carbon dioxide flows. If the process controller does a great job, changes in the process are not seen in the PV but are seen in the MV. When a process controller output goes directly to a final control element (no cascade control and no flow measurement), the relationship between the MV and the PV has too much nonlinearity and uncertainty principally due to pressure changes to be used for statistical or first principle modeling. The story is in the controller output but the picture is much clearer if there is a flow measurement.
Scott: The data historian is such a great tool. The trend in PID outputs tells us so much more, which has led to significant improvements in product quality. Each large scale production run takes 6 weeks and 30 people in a manufacturing plant, not counting delays for trying to solve and document continuing problems. Consequently, the time besides the cost benefits of preventing rejected batches are huge.
Stan: What do contract manufacturing organizations (CMO) think of the results of these lab optimized automation systems?
Scott: The relationship between the biopharmaceutical company and the CMO, which can be contentious at times, has greatly improved when these systems are used. The BioNet/DeltaV equipped PD Lab can develop large amounts of well organized and analyzed data. The basic control strategy can be worked out in the PD lab well ahead of the first meeting with the CMO. Repetitive PD bench top runs give their data statistical significance and this greatly reduces the risks involved with the scale up process. There is much less risk of a "surprise" at the 6000 L scale. Everyone wins if risk is reduced. The technology transfer to the CMO is accomplished with less cost and much improved time-to–project completion. This makes both the Biopharma client and their CMO much happier about the project.
Greg: Besides knowing the flows of nutrients and gases, the real time measurement of cells, key components in the culture, and product concentration provide the knowledge that could be used not only for online optimization but for dynamic modeling. The resulting virtual plant can be run faster than 500 times real time to develop and test CMO automation systems.
Scott: An essential part of our lab optimized system is an integrated, multiplexed, at-line Nova BioProfile Flex analyzer with an automated sample system that can measure 14 parameters in a sample including cell count, size, viability, and osmolality, substrate components glucose and glutamine, an inhibiting components such as ammonia and lactate. Most recently, the technology has been developed to automatically fill and freeze 5 cc vials for measurement of product concentration and quality. This product analysis is done offline with liquid chromatography due to analysis complexity.. The sample volume taken per day is about the same as the substrate and nutrients added so there is no appreciable loss of bioreactor culture, which is critical for the 1 liter size.
Stan: What do the scientists think of these sophisticated control and automation systems?
Scott: They love them. The automation frees them up to do real science rather than mundane experimental procedures. Plus, with ease of the remote connectivity, they can monitor experiments and tweak something using their home laptop or even their iPhone so they don't have to come into the lab during the night or on weekends to tweak a parameter to get the most out of a run. There is less stress. The scientists can also be more innovative with the many automation features that are pre-configured and hence selectable and adjustable via graphics optimized for their PD Lab.
Greg: I expect the benefits in terms of batch repeatability are similar to what we see whenever we automate manual operations in industrial plants due to limitations in human predictability and monitoring and adjustment scope and timing. The automation in the PD lab may translate to even greater benefits due to the complexity of the decisions and interactions and the incredibly long time horizon before the consequences are seen. We have a two way street and a synergy. The best of automation technologies and techniques from industrial production can be moved into the PD lab that can be translated into more advanced control systems to be transferred to the CMO. Furthermore the capability of a CMO can be evaluated for CMO selection and improvement by the use of a virtual plant developed from the prolific PD lab data. We bring it home with a list of reasons to use an optimized DCS for your BBQ.
Top Ten Reasons to Use an Optimized DCS for Your BBQ
(10) Automated recipes
(9) Predicted BBQ times
(8) Five-course meal no problem
(7) Don't have to watch cooking shows
(6) Feed-forward control
(5) Process control comes home
(4) Children want to become automation engineers
(3) Spouse finally appreciates your expertise
(2) Griller not grilled
(1) More time to drink beer