Real-time information on key process variables is a prerequisite to greater manufacturing efficiency and improved process control. Without this information, quality and efficiency are hampered, resulting in product loss, rework and higher manufacturing costs.
In order to control quality and process variables, however, one must accurately and continuously measure process conditions. In theory, conventional analytical hardware can provide this information, but existing sensors cannot measure some types of data online in real time. Even sensors that work online can be unreliable, and readings can “drift.”
“Soft” or “virtual” sensors offer a way around the limitations of analytical hardware. As their name implies, these are pieces of software code, developed by taking process data readings and modeling the process.
Most drug manufacturers have not yet explored virtual sensing, just as they have not yet harnessed advanced process control technologies such as distributed digital control systems or advanced control and optimization algorithms. Technologies using smaller and more powerful microchips and faster computers have already improved the manufacturing efficiency for the petrochemicals, cement, steel, automotive and other industries. Most drug makers today still rely on off-line quality testing in the laboratory, leading to delays, high cycle times and production costs.
FDA’s process analytical technologies (PAT) framework  allows drug manufacturers to use more advanced process control strategies to achieve “quality by design” for their products and processes. Virtual sensors, programmed using process measurements, allow drug makers to understand, manage and control all critical sources of process and product variability, thus achieving PAT’s primary goal.
This article will examine virtual sensing and highlight its application in the drug manufacturing industry by providing relevant and practical examples from other industries.
Applying virtual sensors
Virtual sensors estimate the value of primary variables that are impractical, or impossible, to measure online, such as particle size distribution, melt-flow index, composition or flooding in a distillation column. The sensors use temperature, pressure, flow rate and other variables that can be reliably and inexpensively measured online, to derive these values.
Virtual sensors thus provide a solution where process parameters are difficult to measure in real time, or where process conditions would damage hardware (for example, highly corrosive atmospheres). They can also be used in situations where an analytical device cannot be linked with real-time data acquisition and control systems.
By continuously estimating critical process parameters from continuous data readings, virtual sensors allow for online “measurement” of these parameters, which can be used as feedback for timely and effective process control. Once developed and tuned, virtual sensors can be applied for online control, process monitoring and fault diagnostics.
Figure 1, below, illustrates the manner in which a virtual sensor might be used for online applications. The application runs in real time, parallel to the process. The virtual sensor is subject to the same changes and inputs that take place in the actual process. Any information that is not available online, such as data on feed-composition changes, can still be fed into the virtual sensor through the user interfaces provided in the application.
|Figure 1: Fundamental working of virtual sensor.
Thus, the model in the virtual sensor receives live data from the plant, and continuously computes all relevant data points. Outputs are stored in the same real-time database and are available, along with signals from field sensors, for feedback control or any other monitoring purposes.
Once a virtual sensor has been developed and validated, it can be used for various purposes such as online monitoring and diagnostics, or feedback in advanced process control. Figure 2, below, demonstrates the application of virtual sensors in process control.
|Figure 2: Virtual (soft) sensors and process control.
At the core of any successful virtual sensor application is a well-designed and well-tuned model of the underlying process — a model that presupposes in-depth knowledge of the physical phenomena underlying the process.
Different types and combinations of models can be used for different applications, depending on the application and the complexity of the process. The most common models used for the development of virtual sensors include:
- (first principle-based);
- Univariate or multivariate statistical analysis techniques such as regression-based, principal component analysis (PCA), and partial least squares (PLS);
- Artificial neural networks, of either feedforward or recurrent variety;
- Advanced signal processing techniques such as wavelets and wavenets;