Cybernetica’s Batch Optimisation applications are suitable for batch and semi-batch processes. We apply a real time optimisation (RTO) approach in order to determine optimal process operation before and during batch operation.
Benefits of RTO applications
- Increased productivity
- Improved quality control
- Increased yield
- Increased safety
Typical control objectives
- Optimise product quality
- Optimise trajectory with terminal constraints
- Maximise productivity by minimising batch cycle time
- Comply with safety margins
- Maximise yield
- Reoptimisation to unknown disturbances and changing process conditions
RTO with Cybernetica CENIT
- Better control of processes with nonlinear characteristics
- Optimal trajectory tracking with NMPC
- Batch-to-batch learning through advanced estimation
- Built-in constraint handling for the entire batch
Real Time Optimisation (RTO)
RTO is typically performed based on economic criteria, weighting batch cycle time against the cost of consumables (expensive raw materials, energy) and product yields. The degrees of freedom in the optimisation will vary for each application, but typically involve raw material feed rates, temperature profiles, pressure profiles, and other operating conditions. We work closely with our customers to carefully define and tune the optimisation problem to ensure that the overall objective is met.
Cybernetica’s RTO applications are based on the Cybernetica CENIT software suite. The RTO algorithm performs an initial optimisation of the batch and reoptimises the remaining part of the batch at regular intervals throughout the batch.
Communication with Cybernetica CENIT Model Predictive Controller
RTO applications are typically delivered together with Nonlinear Model Predictive Control (NMPC). The RTO layer communicates optimised batch parameters directly to the NMPC controller, which performs closed loop control of the process accordingly. In this two-level approach, the MPC level typically runs on a shorter time scale compared to the RTO level which looks at the entire remaining part of the batch. This separation of layers is crucial to meet real time requirements for complex optimisation problems. It also facilitates the use of different estimators at each level, enabling efficient disturbance rejection at the lower level and intelligent batch-to-batch learning on the RTO level.
Model and application development
The process model for RTO is implemented in a Cybernetica Model and Application Component, which is linked into the CENIT system. This separation allows for very specific tailoring of the application to meet the customers need and efficient deployment of tailored online solutions.