Process Analytical Technology and Process Control in Solid-Dosage Manufacturing - Pharmaceutical Technology

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Process Analytical Technology and Process Control in Solid-Dosage Manufacturing
Industry is moving toward closed-loop control of continuous processing.

Pharmaceutical Technology
Volume 37, Issue 4, pp. 56-61

Integrating PAT and process control

The next step after integrating PAT into the continuous process as a monitoring tool is to incorporate the data from PAT into process control. In a feed-forward/feed-backward or closed-loop control system, critical process parameters (CPPs) are dynamically adapted and fed to the controller to keep the process within specification. In today’s batch processes, unit operations are operated as “islands of automation,” but the goal of continuous processing is to control multiple, single units as one unit by using an overarching process-control system.

Closed-loop control has been used successfully for many years in other process industries. “The frightening part for the pharmaceutical industry is moving from manual control with people making decisions to advanced process control, in which process data and modeling software automatically control the process,” comments Jonathon Thompson, senior manager of Compliance Services Consulting at Invensys. “PAT can give you a lot of data, which you need to turn into information about the process and whether it is within the CQA parameters,” he explains. “Process-modeling software compares real-time data to an ideal or ‘golden’ batch profile, identifies what parameters need to be changed to meet the ideal, and feeds this back into the control system.”

Researchers have been addressing several issues while integrating process control. One issue is that although some process equipment (e.g., newer feeders) is typically already instrumented for control, other equipment may require retrofitting. The tubular blender used at C-SOPS, for example, had a simple motor with a tachometer to control the speed, and C-SOPS engineers added instrumentation to allow more sophisticated control of the blender speed. Integration of instrument software with plant equipment is a challenge. Some installations that require simple endpoint or on/off control could use simple, analog 4–20 mA connections, says Hammond, but the most valuable applications generally require more sophisticated control that involves developing either direct communication with the manufacturing equipment’s control systems or communication with plant supervisory control and data acquisition (SCADA) systems.

“Ten years ago these communication links were custom developed for each piece of equipment. In recent years the development of “open architecture” software has simplified this aspect of instrument integration,” explains Hammond. Open-architecture software (e.g., OPC) uses open standards that enable connectivity. This software meets the need for communication protocols to enable analytical instruments to communicate with control systems, says Hammond, who notes that, after working with instrument vendors and control-system companies over the past five years, most of Pfizer’s vendors’ instruments now communicate via OPC.

Another challenge has been ensuring that data from PAT flows into the process-control system quickly enough to enable meaningful control of process fluctuations to keep CQAs within specification. “If the measurement takes longer than the residence time of the material—for example, 30–45 seconds of residence time in the blender—then the measurement can not be used for process control,” explains Hausner. He says that available NIR instruments are adequate for some applications, but faster analysis may be needed for other applications, such as smaller doses or lower percentages of API, which would necessitate more scans to obtain a measurement. C-SOPS continues to investigate solutions for NIR measurement in its production-scale Continuous Pharmaceutical Advanced Manufacturing Laboratory (CpAML). Analysis costs are part of the equation, notes Hausner. For example, a current CpAML project is comparing the use of one, fast (i.e., expensive) spectrometer with an array of slower but more economical spectrometers. An alternative method is inferential sensing, in which offline laboratory data is used with known correlations to give the control system the data it needs, adds Thompson.

Tracking material flow through the system is a crucial aspect of process control. A SCADA system tracks the location of a “product plug” as it moves through the process and adds this context to the PAT data in the process-control modeling software, explains Ivo Backx, manager of business and project development for the pharmaceutical industry at Siemens Industry Automation Division. “In tablet production, for example, multiple variables control the CQAs and you need to know what these attributes are and the correlation between them at specific points in the process,” says Backx. “For example, you need to know that the material in the tablet press now had a certain content uniformity at the blender.” Material tracking through the system is linked to requirements for traceability, notes Backx.

Figure 2: A continuous, high-shear granulation and drying system in operation at the GEA test center (ConsiGma, GEA Pharma Systems).
Traceability is simple in a batch system because it is assumed that all the product from one batch is the same. “For a continuous operation, traceability becomes much more important because not all the product is submitted to the same process at the same time. Using a first-in/first-out (FIFO) principle is, therefore, very important,” says Schoeters, who notes that GEA has adapted the unit operations of mixing, granulation, drying, compression, and coating in such a way that the product is traceable throughout the production line and back-mixing is limited as much as possible. The ConsiGma system, such as the line shown in Figure 2, continuously monitors CPPs to keep them in control and maintain product quality. This includes measurements using PAT (e.g., particle size, moisture, uniformity), but primarily involves monitoring of and control-feedback loops on machine parameters. Torque of the granulator screws, for example, is continuously monitored, and a deviation from the set value triggers actions and alarms.


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