Applying Advanced Control in Batch Applications
by Jim Cahill
Continuous manufacturing processes have long benefited from the application of advanced process control (APC) in their processes to improve upon their regulatory control. Batch manufacturing processes have recently been able to take advantage of these technologies. I received an email the other from Lou Heavner, part of Emerson's Advanced Applied Technologies team. We've featured Lou's work here a few times in the past.
I'll summarize a few of these applications with the hopes that it might spark some ideas for application in your batch manufacturing process.
A manufacturer of sweeteners was having scheduling problems caused by the unpredictability of batch cycle times. End of batch could vary between six and twelve-plus hours. The operators could determine when end of batch was reached but not predict when this would occur. The APC consultants worked with this manufacturer to apply neural network technology as an inferential estimator to predict the end of batch time. The model can successfully predict the end of batch plus or minus ten minutes up to four hours before the completion of the batch. Scheduling downstream equipment is more manageable given these accurate predictions.
A second example Lou mentioned was again around batch cycle time, but in this case poor distillation control, which resulted in longer batches. Model Predictive Control was used in this pharmaceutical manufacturing process to control the batch distillation, specifically the reflux. Distillation time was reduced with the overall batch cycle time reduced by more than three hours per batch on average. The net effect of this improved control performance was a five-plus percent increase in production capacity. The quality of the product produced was also improved.
A third example is in a specialty chemical manufacturer's semi-continuous fluid bed hydrogenation reactor. In this process, cold solids are added to the top batch-wise based on level in the vertical reactor. Heated feedstock (gases) enters the bottom to provide the fluidizing medium and heat to drive the reaction. The reactor was a bottleneck, limited by temperature control and high temperature constraint. Adding model predictive control around the reactor provided more stable temperature control. The controller reduced temperature variability and allowed target to be moved closer to constraint limit with fewer high-temperature trips.
I thought these were great examples of advanced control technologies combined with people like Lou with process and APC application knowledge that are solving process problems and improving process efficiency. Perhaps these ideas will spark some ideas for improvement in your operations.
Tags: batch manufacturing
| APC
| advanced control
| advanced process control
| neural network
| batch cycle time
| MPC
| model predictive control
| batch distillation
| hydrogenation reactor
|
March 7, 2008 in Distillation Column, in Food & Beverage, in Life Sciences, in Process Optimization | Comments (0)
Better Decisions through Process Data Migration and Consolidation
by Jim Cahill
As competitive pressures drive process manufacturers to run their processes more efficiently, a key area of focus is to improve the management of data from various sources. Better, more timely decisions come from better data.
I spoke with one of our Life Sciences/Food & Beverage industry senior manufacturing consultants and data management experts, Gary Silverman about this need to consolidate and migrate data. He cited several reasons for this:
- Updating a historian and/or operating system because it's no longer supported by the original supplier
- Needing to consolidate data from multiple process automation system platforms and other data sources into a single enterprise historian
- Changing business needs requiring broader dissemination of information from the manufacturing process to plant and corporate personnel with web browser-based technologies.
One example Gary cited was a DeltaV system upgrade project where an AIM/Bile Historian with 9 years of process data collected from a PROVOX system needed to move to an OSIsoft PI historian. The finished solution collected data from the new DeltaV system, PROVOX and utilities programmable controllers. Emerson is an OSIsoft Platinum Partner as a provider of data management and integration services.
The data management team had developed automated tools and methods to extract the AIM tag database, create the PI tag database and migrate this vast amount of data. The team also built a Process Module Database to streamline the implementation of the OSIsoft RtPortal/WebParts technology. The Portal allows operators, aupervisors or engineers to quickly spot problems and then use ProcessBook and/or DataLink to drill down for in-depth analysis. The Portal also provides a central repository for Shift Logs, Operator Logs, etc.
Another key need was being able to perform batch-to-batch analysis with data from over 70 reactors and make comparisons of critical process parameters to discover any deviations from the best or "Golden Batch." PI Batch configuration and the PI Batch Client Tools provided the customer with a means to do this. They were also able to monitor and improve cycle times as a result of this analysis. In the end the project achieved its objectives to modernize the existing technology disseminate information more broadly and provide critical data in Batch context for continuous improvement.
Given the high interest in having better information to help plants run more efficiently, I'll be checking back with Gary and other data management experts from time to time.
Tags: Data Management
| OSIsoft
| Life Sciences
| Food
| Beverage
|
March 3, 2006 in Data Management, in Food & Beverage, in Life Sciences | Comments (0) | Trackback (1)
