Dynamic processes play a key role in many industrial applications such as in the automotive, aerospace, pharmaceutical, and chemical process industry. Knowledge about how to build, simulate, and analyze mathematical models of such processes is crucial to be able to optimize performance, design control systems, or make highly reliable predictions about process behavior.
FCC provides key competence throughout the whole chain of modeling, simulation, analysis, and control of dynamic processes covering a wide range of application areas. We apply and develop tools for system identification, i.e., building mathematical models using measurement data, model based signal processing, and prediction of physical quantities from indirect measurements.
Our goal is to provide clients and partners with the tools and methods required to support their domain expertise for improving and optimizing their products and processes with respect to performance, cost, and quality.
Knowledge discovery and data mining, i.e., analysis and extraction of information from large data sets, such as process sensor data, data acquired for quality control, or internet data feeds require advanced knowledge in mathematical statistics, classification and machine learning, and clustering techniques. We have this competence and combine standard and in-house developed software to conduct projects in this area.