PAT: The Value of Better Control

Model Predictive Control (MPC) and better sensor technologies will allow pharmaceutical companies to better control their processes. Pavilion Technologies’ Eric Unrau and Michael Tay discuss the challenges and benefits of implementing MPC for pharmaceutical processes.

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By Eric Unrau and Michael Tay, Pavilion Technologies

Self-reverence, self-knowledge, self-control. These three alone
lead life to sovereign power. -- Lord Tennyson
The FDA Process Analytical Technology (PAT) initiative has at its basis the objective to understand specific pharmaceutical processes more thoroughly using a variety of analysis tools, including not only new sensor technology but also statistical, fundamental and artificial neural network mathematical modeling. It has been widely published in literature that, given appropriate mathematical models of the relationships that govern process behavior, engineers can achieve new insights into their processes and further leverage these models to achieve higher quality, higher efficiency and a process that is in control. The PAT-based projects attempt to apply new sensor and analytic technologies to improve our process knowledge and achieve an intelligently controlled process.

We can control processes not within static recipes, but based on a thorough understanding of the achievement of good quality products.

The FDA has in multiple forums (ISPE Washington Conference, June 9-10, 2004; ISPE Annual Meeting, October 25, 2004; Pharmaceutical Quality by Design: Improving Emphasis on Manufacturing Science in the 21st Century, August 5, 2004) reinforced this intent with statements that the objective of PAT is to improve process understanding, whereas one result is the potential to reduce regulatory oversight and burden on companies who have demonstrated good practices and a better understanding of their process through proper PAT implementation.

Once a process is better understood, the next step is to directly use that understanding as an enhancement to basic level control and directly control quality and the end-product objective. When you control directly to analyzed, measured or understood quality, you are enforcing a good, in-control process —instead of controlling and documenting that the process is maintained within static operating limits.

There are two classical technologies to regulate process operations: regulatory control (PID) and model-predictive control. Better analytics enable the usage of more robust control technologies.

Regulatory Control

As analytic technologies are applied to our processes, we can leverage the better understanding that results to achieve better control. Regulatory control, generally PID, is the workhorse of the control technologies used in some form on almost every control loop.

In general, control is managed to a target for temperature, level, pressure or flow, and an actuator is adjusted to maintain the process condition at target. This is the simplest model-based controller that includes tuning constants to react to proportional changes in target error, integrated error that avoids long-term offset, and derivative action that responds to the speed of the control response. Given a dynamic process model and a controller objective, IMC (internal model control) tuning rules can be used to calculate an optimal PID tuning. These rules are the basis of most regulatory controller tuning packages.

The results of analytic tools enable a pairing of each control target (pressure, temperature or flow) with the best actuator. With an understanding of the relative influence of each actuator on each target objective, called relative gain analysis (RGA), in control terms the mathematical model can be used to design the optimal regulatory control strategy. In many cases the regulatory control loops are straight-forward and intuitive, but for complex systems or challenging single controllers, RGA resulting from an improved multivariable data analysis (PAT application) improves control significantly.

Predictive control is about knowing what is ahead, not what you just hit.

Model-Predictive Control

Model Predictive Control (MPC) was developed to solve control challenges of complex dynamics, multivariate interactions and direct quality control. (To access a sample MPC diagram, click the "Download Now" button at the end of this page.) For example, an MPC solution was very early industrially deployed to solve challenges in on-line optimization projects in hydrocarbon plants. The plant operator agreed that the mathematically calculated optimum was probably the best operating condition, but that he could not get the plant from where it was to the optimum. His challenge was the large number of operating constraints and trip limits that prohibited success. Let us investigate each control challenge solved by MPC individually.

Some processes have long, complex and/or mixed dynamic interactions. As an example, most spray dryers are controlled rapidly by adjusting feed rate of concentrate to the dryer. These fast dynamics are significantly different than the slow response required to stably adjust heated drying air. For this reason, most dryers are controlled with regulatory temperature control by adjusting the dryer feed rate (the fast response). Because the desired or ideal control includes both fast (pump speed) and slow (temperature) relationships, MPC is required for the best dryer control.

Other challenging dynamic problems occur in very slow processes, where the process responds hours or even days after a control change is made. MPC mathematics includes a long memory of the past process dynamics and is designed to manage a process in cascade to regulatory control. Very slow processes can be managed at an appropriate frequency.

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