Many tools are available for PAT. This article focuses on the knowledge discovery available from the creation and operation of a virtual plant environment. Such an environment can involve the synergistic use of model predictive control, first-principal models, neural networks, and principal component analysis, as described in New Directions in Bioprocess Modeling and Control (1).

Advantages of a virtual plant

Figure 1: Virtual plant with imported configuration from actual plant and embedded advanced control tools and process simulation.

Most dynamic, high-fidelity, process-simulation software allows users to build a basic control strategy or sequence inside the simulation environment. Nonetheless, simulation developers tend to have a process rather than a control background and focus. It is unrealistic to expect the process and batch-control capability offered by simulation software to be in the same realm as the control capability of a distributed control system's (DCS) software developed from hundreds of years of experience by process-control experts. The overall control functionality in process simulators is primitive compared with the capabilities offered in the modern DCS, which offers capabilities such as sequential function charts and basic function blocks, a batch manager, and advanced control tools such as multivariable model predictive control. These are almost nonexistent in simulators. Duplicating even a simplified version of a control system in a dynamic simulation is a large effort. At best, a user may end up with two control systems with no assurance of how well they match up and with no way to automatically manage changes between them.