Fraunhofer Institute for Industrial Mathematics ITWM
Fraunhofer Institute for Industrial Mathematics ITWM
Stammwerk der BASF Gruppe
© BASF SE
Das Herz der BASF-Gruppe ist die BASF SE mit ihrem Stammwerk in Ludwigshafen am Rhein. Mit über 160 chemischen Produktionsbetrieben, vielen hundert Labors, Technika, Werkstätten und Büros ist es der größte zusammenhängende Industriekomplex Europas. Abdruck honorarfrei. Copyright by BASF. The heart of the BASF Group is BASF SE headquartered in Ludwigshafen, Germany. With its over 160 chemical production plants, several hundred laboratories, technical centers, workshops and offices, it is the largest integrated industrial complex in Europe. Print free of charge. Copyright by BASF.

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BASF group's headquarters are located in Ludwigshafen. With more than 160 chemical production sites, many hundreds of labatories, workshops and offices it's one of the largest industry complexes in Europe.

© BASF SE

Process Optimization in the Chemical Industry

Within process engineering, a number of different objectives and restrictions has to be taken into account. For example, operating and fix costs are to be minimized, while simultaneously the quality of the final products should be maximized. Additionally, health and environmental issues play an important role.

To find a good compromise between these different requirements, the process engineer not only has to compare the various tunings of a given layout, but also a number of several devices, this is a challenging task. So far, this layout process is done with the help of computer-based simulations, where good solutions are found empirically. Generally, however, due to the complexity of the problem, these solutions are not the best compromises achievable.

A New Approach for Planning Processes

In this project, a new approach to the design of chemical production plants is being developed. Only those solutions are considered and analyzed further which constitute best compromises between the different objectives, while respecting the restrictions. This set of best solutions is generated automatically for an interesting parameter range and then presented graphically to the engineer. Thus the engineer can make a rational decision based on the knowledge of the complete range of best solutions.

In the examples analyzed so far, this procedure revealed parameter ranges which have not been taken into account before within the empirical optimization. Solutions in these ranges are much better than those found empirically. Additionally, the planning time has been reduced significantly.

 

Activities in Chemical Engineering
© Fraunhofer ITWM
Activities in Chemical Engineering

The precise description of real-world appliances requires complex, often highly parameterized models. The solution of optimization problems based on these models generally requires a great amount of function evaluations which in many cases are obtained from expensive simulations. This leads to a significant numerical complexity, especially when an overview over the entire decision space is desired. A solution to this are the so-called Shortcut-Methods.

INES – Interface between Experiments and Simulation

The goal of INES (Interface between experiments and simulation) is the development of software tools to facilitate the data-supported modeling and simulation of chemical plants. The aim is to obtain optimally parameterized models. Therefore, reliable data should be selected from an existing database in order to realize meaningful model adjustments.

This adjusted model then is available for optimizing the process design:

  • No outliers
  • Stationary intervals
  • Mass balances satisfied

Removal of outliers is done interactively by defining a window around the median of a data series proportional to the median absolute deviation. This allows the user to define an outlier-detection according to the working context (accuracy of the instrumentation, known errors). Stationary intervals are found by a heuristic segmentation of the data series. In this approach, break points are set such the averages between neighbouring time-intervals differ significantly. For these intervals, a statistical analysis for stationarity can be realized.

Mass balances are checked by an interactive configuration of a data reconciliation. For control volumes defined by the user, mass balances are calculated if data redundancy is given; furthermore, a componentwise data reconciliation is done. In the case of non-redundant data, the software makes suggestions which quantities have to be measured in order to achieve redundancy. The results of the data reconciliation are used to detect possible gross errors in the data.

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