Eight advanced process control technologies worth considering

By Rick Rys, President, R2 Controls, and Janice Abel, Director, Global Pharmaceutical and Biotech Industries, Invensys

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The U.S. Food and Drug Administration (FDA) Process Analytical Technologies (PAT) initiative allows pharmaceutical manufacturers to optimize the way they use their plant assets to produce specific drugs, ultimately permitting them to reduce the price that the consumer pays for their products. PAT also allows pharmaceutical manufacturers to apply advanced process control (APC), even to such challenging cases as biopharmaceutical processes. Successful use of APC in biopharmaceutical processes simply requires fine-tuning, constant readjustments and updates. These changes were impossible before PAT, when simply retuning a single process-control loop could be considered a “significant process change.”

By encouraging drug manufacturers to broaden their focus from compliance and time to market, PAT allows drug makers to strive for “operational excellence,” a term that also includes the goals of improved efficiency and quality and lower manufacturing cost. This article outlines eight APC and optimization technologies that PAT allows drug manufacturers to implement.

PAT solutions are possible wherever measurements of key process variables can be used in feedback control systems. The Proportional, Integral and Derivative (PID) concept of feedback control is still solid, flexible, robust, and easy to implement and maintain.

The Synthetic Analyzer

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Creating a synthetic analytical measurement from regression variables and using it to control process variables and optimize product quality is common practice in many industries. Pharmaceutical plants can now use synthetic analyzers on the enormous volumes of data they gather and store.

Consider a biopharmaceutical facility whose quality control department must optimize and regulate cell growth. They take process samples from a bioreactor three times a day and send them to the lab for various analyses. During sampling, the plant control system stores historical data such as the dissolved O2, pH, temperature, heat removal/addition rates, and ingredient addition rates.

Increasing the rate of sampling and analysis to once every hour would be a far too expensive and impractical way to improve process control. Correlating lab data with operating data, could however, provide a very good regression model. Even a linear regression model using coefficients that have been optimized to match operating data would be effective.

Simply implementing an equation in the control system provides a synthetic analyzer that eliminates the wait for laboratory results and could also provide measurement to a new controller. When integrated with a lab bias update, this system can also synchronize, constantly, the regression model to the more accurate lab results. The advantage is faster response than waiting for the lab; the disadvantage is that you need enough historical data for a usable regression.

Feedforward or Predictive Controls

Figure 3. An example of simple
feedforward control

Predictive controls can be very effective in countering upsets. In an integrating process such as level or pressure, for example, there might be an upset in the inflow. Immediately adjusting the outflow by the same amount as the inflow upset will regulate the level or pressure. Many plants already have the assets to do this and some PID blocks have a bias input, making implementing feedforward control as simple as an adding to a feedback loop.

This provides better response to upsets, but does add complexity for the operator and the need to keep the feedforward in calibration.

Smith Predictors and Dahlin Controllers

These special versions of feedback control systems are optimized for dead time dominant processes, such as conveyor belt mixing for example. Some automation system vendors offer these directly as special programs or as options for the PID. This technology is most effective when process dead time can be estimated accurately - i.e. when the control system accurately knows the time delay of the conveyor belt. The ability of the controller to change or hold setpoints is improved when properly applied and tuned.

Expert Systems

These programs apply Artificial Intelligence to process control - for example, adding meaning to a basic “low flow” alarm, so that the operator would know the reason for the flow condition (e.g. “the flow’s low because the supply tank is empty.”)

More complex expert systems can emulate the actions taken by expert operators. To construct an expert system, one must first determine the rules that guide a decision, and then implement them in a program. The rules must be based on data available to the control system.

In one blending installation, we used a Simplex Algorithm to find the optimal solution of a system of linear equations to develop an online optimizer. After reviewing possible operating constraints, the optimizer would compute the least expensive way to perform the task.

At times, the optimizer would complain that the task was “infeasible” and that no operating conditions could achieve the result without violating the constraints. However, by using intermediate calculations, we developed an expert system that would list possible suggestions - for example, relieving specific constraints or changing the problem setup. This guides the operator in handling special problems, but can require extensive custom code for each little solution.

Model Predictive Multivariable Controls

Figure 4. Model predictive multivariable
control workflow.
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This technology was developed by the petroleum and petrochemicals industry to improve on PID-based controls for interactive processes. To develop model predictive control (MPC) systems, engineers must first develop a detailed process model, using the process and associated instrumentation and valves. Then, they must decide on the feedforward (FF), controlled (CV) and manipulated (MV) variables, and conduct a series of step tests that cover all control system outputs (MV’s and FF’s), while recording related measurements (CV’s).

The resulting vector files can then be used to create the controller directly. These Matrix-based model predictive control algorithms integrate feedback and feedforward in a single control arrangement. The controls can be very effective at regulating and optimizing such highly interactive processes as reactors.

Model predictive multivariable controls can lead to dramatic performance improvements, but are far from fool-proof. If any critical measurements fail, the controller can fail, so a backup control strategy is essential. In some situations, PID, cascade, ratio, and decoupling controls can provide many of the same benefits as MPC, so these options should be tested before considering MPC. And, even under PAT guidelines, there will be some validation challenges with MPC at this stage of development.

Scientific Simulation and Security Concerns

Simulation allows manufacturers to test drive a process, or run through “what if” scenarios. Understanding the process is a critical first step. In the chemical manufacturing and petroleum refining industries, engineering and construction (E&C) firms often use a flowsheet simulator to construct a process model. The resulting heat and material balances are developed, complete with models of each piece of equipment.

Complex reactors are the hardest to model, since they may require kinetic and equilibrium models. Once a model is sufficiently developed, and heat and material balances closely match the fundamental knowledge of the scientists who developed the product, then the instruments, pipes, valves, and various other process equipment can be specified systematically.

Such technology is conspicuously absent in the pharmaceutical and biotechnology industry today. A key concern is, of course, the value of the intellectual property contained in such models, since the owner of any new drug has invested heavily in creating that product and the means to produce it for clinical trials.

Just how much information does the E&C firm need to specify the instruments, the regulatory control requirements, and develop measurements for key manufacturing variables? Wide dissemination of details may improve process design and control, but may jeopardize security of critical intellectual property.

Simpler, lower-cost simulations that are directionally correct rather than rigorous can assist in control system testing and operator training without the embedded Intellectual property. As simulator systems tend to die from lack of support, as they outlive their usefulness, it may be best to build a cheaper throw away simulation.

Neural Networks

Neural Networks are a regression technology that can be useful to compute virtual measurements. The math has been done by others so they are very easy to use. They can be amazing for determining quantitative relationships between plant operating variables. he resulting statistical models can be highly accurate if sufficient operating data is available. The resulting statistical models can be used predict “product quality” from real time data as discussed with virtual analyzers. One caution is the models will likely have difficulty extrapolating predictions for operating points that are outside the range of data used to "train" or regress the model. With the right data, and neural network software, you can generate “black box” models very quickly and you are likely to learn something useful about operating variables affect product quality.

On-line analytics

Real-time monitoring of composition and other material properties holds much promise for pharmaceutical manufacturers. Spectroscopy, in the form of Near Infrared (NIR), Fourier Transform Infrared (FT-IR), and Raman, is being widely applied in pharmaceutical PAT installations, but other technologies are also being used.

Nuclear magnetic resonance (NMR), for example, reads the magnetic properties of atomic nuclei. Nuclei placed in a strong magnetic field, change orientation in measurable ways, revealing a wealth of information about composition and chemical structure of a compound. Most notable in medical diagnostics, in the form of MRI scanning, NMR has been used in petroleum refining, and is now being considered for a variety of pharmaceutical applications, including clean in place (CIP) and check weighing.

Thermal effusivity sensing is another advanced analytical technique that is now migrating into pharmaceutical applications. One new sensor from Mathis Instruments, for example, precisely measures the moisture endpoint in a fluidized bed dryer while minimizing the characterization time that is required when compared to competing methods. Because this process is based on scientific data, it can reduce product characterization times from days to minutes, which would be especially valuable for production applications involving frequent product switchovers or multiple products.

Wireless technology opens yet another realm of possibility. Deploying wireless sensors on skids, for example, could reduce downtime and costs by eliminating the need to connect instruments and computers to field networks. Wireless transmission of data to pre-built templates, for example, improves operational efficiencies by eliminating downtime normally required to run and connect field wiring.

The Need for Enterprise Collaboration and Control

A comprehensive audit of current procedures can help determine which control solution will help achieve the optimal balance between asset availability and utilization. A critical part of the process is return-on-investment (ROI) analysis, incorporating business strategy, implementation and monitoring. Although true ROI from PAT can only be realized through automation, FDA has been quick to point out that PAT should not be viewed as a single type of analytical technology or a sensor but as a complete regulated solution that includes designing, analyzing and controlling timely measurements, critical quality and performance attributes, raw and in-process materials and processes.

Advanced pharmaceutical process control will continue to evolve with advances in process technology, automation and communications. However, the PAT initiative shows that regulations, and regulator mind sets, can also evolve. PAT provides a gateway through which pharmaceutical and biopharmaceutical manufacturers can implement advanced controls to achieve operational excellence. Advanced control gives them one more set of tools to help deliver pharmaceutical products of the best possible quality at the lowest possible cost.