Monitoring and optimization of production processes and plants

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The research group "Process Control and Data Analysis" at the IOSB develops concepts and software solutions to monitor and optimize production plants and production processes in a holistic way. The aim is to detect or predict errors and anomalies in plants and processes at an early stage (condition monitoring) and to optimize process control. This is intended to increase the availability of the plants, minimize rejects and improve product quality.

All available information sources are used for this purpose: These are firstly measurement data and parameters from the production process and the plants, secondly physically motivated models of the process, and thirdly the expert knowledge of the plant operators. These information sources are linked with methods of machine learning and artificial intelligence.

In our research projects we work out concrete solutions with partners from industry, which are evaluated in practical use.  Application domains are for example process engineering processes (e.g. foaming processes, glass drawing processes, bioprocesses) or condition monitoring of wind turbines.



Machine Learning for Production – ML4P

In the ML4P lead project, six Fraunhofer Institutes led by the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB in Karlsruhe are researching the development of a tool-supported process model and the realization of corresponding interoperable software tools in order to systematically tap the optimization potential in production plants through the use of machine learning methods.


Data analysis platform for SME: Industry 4.0 MonOpt

By means of the developed and integrated data management and above all thanks to the advanced data evaluation, it is possible to carry out systematic data evaluation as well as plant monitoring, thus increasing the efficiency of the production process.


Data-driven error localization in process engineering

Alarm management helps to detect those process variables that are closest to the cause of the fault. The plant operator's attention can thus be focused on these variables and the cause of the fault efficiently localized.


Condition monitoring of wind turbines (ISO.Wind)

In order to keep the total operating costs of wind turbines (WTGs) competitive, the risk of failure must be minimized, maintenance costs reduced, and system availability and energy efficiency increased. This goal is achieved by introducing the most efficient, automated multi-sensory online monitoring and diagnostic systems - so-called Condition Monitoring Systems (CMS) - whose economic importance is increasingly recognized by wind farm operators, manufacturers and insurers. Numerous other CMS are already available on the market, but their monitoring and fault detection is almost exclusively limited to the WTG drive train.


Optimization of bioprocesses

The production of bulk and fine chemicals on the basis of renewable raw materials has become increasingly important in recent years as "white biotechnology". Surfactants, which are currently produced industrially to a large extent from petrochemical starting materials, are a potential product for the use of biotechnological production processes.