Figure 6: General peak data analysis used to monitor the PCC performance, left) the first two switches of the PCC together with automatic peak finding, right) PCC peak heights after about 40 hour operation.
Model based simulation, monitoring and control
The concept of digital twins in real-time applications means to use digital representations and mathematical models for monitoring and control of the processing system. The architecture of the proposed system with Orbit supervisory controllers and network of Orbit controllers allows the user to implement, study, design and validate these real-time applications of digital solutions.
The generation and validation of mathematical models is as important as it is time consuming, requiring a lot of engineering resources, see Nilsson and Andersson 2017. An attractive idea is to automatically generate the process model and to have automatic methods to calibrate and validate the model to experimental data. One such idea was studied and presented in Tallvod et al (2022A). In Orbit, every object in the digital configuration of the physical system has a mathematical representation. It means that a complete model of the setup is automatically generated based on the described configuration. Many of the objects can be described quite well, because the behavior is dominated by measurable physical attributes, like volume, flow rate, concentrations etc. Other objects need to be calibrated to experimental data and therefore needs a design of experiments to generate high quality data for parameter estimation. Off-line experiment plans are straightforward to perform together with computational extensions for parameter estimations of different models, see Tallvod et al (2022A). During continuous operation it is much harder to do experiments for parameter estimations and model calibration, but limited disturbances are often allowed if the control system can handle the disturbances.
One example of real-time usage of a mathematical model is in Kalman filter based monitoring. Extended Kalman filter tools are implemented as computational extensions in Orbit, with the possibility to use the build in automatic model generation. A simplified example is illustrated in Figure 7, which is a size exclusion chromatographic separation of two components with vary large difference in size. This case is actually linear, which simplifies the Kalman filter design in the case, but it is still a dynamic filter with covariance update algorithm. In Figure 8, column concentration profiles are estimated using the Kalman filter at four different time instances. The Kalman filter predicts the profile based on the model and corrects their value using the measurement signal. The example illustrates the power of these kinds of real-time algorithms for soft sensing, parameter estimation and on-line prediction.