Predictive intelligence
DSM uses Emersons PlantWeb digital plant architecture to cut maintenance time in half

Today pharmaceutical and biotech companies face a formidable array of challenges: Improve time-to-market, manufacture cost-effective product for delivery worldwide, meet global regulatory requirements, minimize manufacturing and automation risks and improve return on assets. The fast-changing worlds of compliance and technology have created the need for a partner who understands both their unique process requirements and the technology best suited to ensure consistent quality.

DSM Anti-Infectives, based in Delft, the Netherlands, has reported significant operational savings by using PlantWeb digital plant architecture from Emerson Process Management. Savings have been made by utilizing the predictive capabilities of the intelligent field devices networked throughout the plant. The information that is provided enables a move towards predictive maintenance, a more effective maintenance strategy that makes it possible to limit the potential for unplanned shutdowns and extend the time between planned shutdowns. Further savings have been made because the integrated modular software, one of the key elements of PlantWeb, is based on open technologies such as Windows; this has reduced time spent on operator training because operators are familiar with many of the commands and menu structures.
DSM is a leading worldwide producer of life science products, performance materials and industrial chemicals. They installed PlantWeb towards the end of 1999 to automate a new and complex pharmaceutical plant that produces raw materials, intermediates and active products for use in anti-infectives such as antibiotics (for combating bacterial infections) and antifungals (for combating fungal infections). The PlantWeb architecture consists of a DeltaV digital automations system, AMS predictive maintenance software and Rosemount pressure, temperature and flow transmitters.
“We use PlantWeb’s AMS to perform maintenance activities on our instrumentation”, said Arthur Hulst, the DSM Anti-Infectives maintenance supervisor, Electrical & Instrumentation. “This has brought us significant savings. Before our technicians go out on site to look at a transmitter they perform diagnostic checks from the ASM operator station. This
means that they know whether the transmitter in question is faulty and what exactly is wrong with it. I believe this ability enables us to spend 50 per cent less on maintenance than we would have done had we been operation a more traditional plant.”
“The Emerson project team was involved very early in the project cycle”, said
Feico van Dantzig, Emerson project manager. “Our early involvement gave the project team the opportunity to understand DSM’s uptime and maintenance requirements. A result of this mutual understanding was that the project team was able to advise DSM in their selection of the most suitable instrumentation for their purpose. This contributed to the substantial maintenance savings.”

Built under FDA validation guidelines
The PlantWeb installation was built under FDA validation guidelines, using Good Automated Manufacturing Practices (GAMP) as the project execution methodology. GAMP are an independent forum with an interest in the validation of automated systems in pharmaceutical manufacturing. AMS was used to streamline the validation process. Loop testing can be performed by one engineer using AMS, instead of the traditional team approach with one control room engineer and one or more field engineers. In addition, AMS automatically generates and stores the evidence that the loop test has been performed, which helps to speed up the validation process.
“Because the DeltaV software is based on Windows technology our operators were familiar with many of the commands, key strokes and menu structures as they have used them on other programs. This saved time on operator training”, continued Hulst. “Based on my experience from more traditional control systems, I believe we saved as much as one month of training per operator, and we have forty operators. This saving in time enabled us to reach full production sooner and, in addition, saved us training costs.