Since the company start-up in 2000, Cybernetica has systematically developed its technology, resulting in unique solutions for model-based control, supervision and optimization of industrial processes. Before that, in the 1990’s, research activities at SINTEF and the Norwegian University of Science and Technology (NTNU) led to the company spin-out.
Striving to stay in the forefront, the staff continuously improves the technology through research and development, in parallel with installation work. Cybernetica maintains a significant volume of R&D activities, and has an extensive network with national and international universities and other research organizations.
Involvement in research projects enables business expansion through the development of first-of-a-kind applications for new processes. At present, Cybernetica is engaged in several research programmes.
- Control and Real-Time Optimisation of Intensive Polymerisation Processes (COOPOL) – a European research project aiming at scientific breakthroughs in the area of advanced control and optimisation of polymerisation processes, allowing the COOPOL partners to achieve a significant increase in product quality of polymerisation reactions by employing the novel process control approach to intensified semi-batch and ‘smart-scale’ continuous polymerisation processes. See also the COOPOL project website and the European Commission’s featured article “Process optimisation for streamlined polymerisation”.
- Cross-sectorial real-time sensing, advanced control and optimisation of batch processes saving energy and raw materials (RECOBA) – a European research project with the objective to maximise the efficiency of batch processes (regarding quality, energy, raw materials, costs). In many aspects, batch processes are superior to continuous ones. RECOBA will therefore take advantage of recent progress in sensor technologies, modelling and control to develop a new paradigm for the design and operation of batch processes: Operation at maximum efficiency; Dynamic, quality driven process trajectories rather than fixed schedules; Detailed analysis and tracking of all relevant process and product variables. Read more at the RECOBA project website.
- Model-based optimisation for efficient use of resources and energy (MORSE) – a European research project with the objective to develop advanced digital tools for optimizing processes and enhance the overall performance of the European steel industry. The project brings together steel producers, researchers and software providers, developing advanced process models and software tools. The new software tools will be designed to optimize process yields and reduce losses, leading to improved steel quality, reduced energy and raw materials consumption. Cybernetica is responsible for developing nonlinear model-predictive control applications, for different steel refining process units. The applications will be demonstrated in cooperation with plant owning partners. Read more at the MORSE project website.
Norwegian research initiatives:
- Fault tolerant model predictive control (SAFE-MPC) – a research project with the objective to develop methodology, algorithms, software modules and work procedures facilitating the efficient development of applications for model predictive control and real time optimization which are tolerant to faults in instrumentation, process equipment and process operation; and to demonstrate this technology through pilot implementations on industrial processes.
- Accelerate learning through technology (ALTT) – a research project with the objective to enable process operators rapidly to master the art of controlling highly complex processes, such as the electrolysis of aluminium. The enabler for radically accelerated learning will is an environment using a dynamic process model in a combination of process simulation, gamification and gaming.
- Demonstration of Optimal Control of Post-Combustion Carbon Capture Processes (DOCPCC) – a research project with the objective to demonstrate reduced energy costs through use of advanced process control (NMPC) in amine-based CO2 capture processes. Read more.