Research to Business

Hunting down particles

Professor Dr Hermann Nirschl and Dr Mathias J. Krause are digitising particle currents in process engineering with the aid of numerical simulation and are helping to optimise industrial processes.

Dr. Mathias J. Krause and Prof. Dr. Hermann Nirschl (from left to right) digitize particle streams in industrial process engineering. (Image: Markus Breig / KIT)

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“There are good and bad particles,” Professor Dr Hermann Nirschl, Director of the Institute for Mechanical Process Engineering and Mechanics (MVM) explains right at the beginning. Particulate matter or microplastic are current examples of the negative occurrence of particles. They are publicly criticised because as yet, no adequate solutions have been devised to reduce the presence of these minute, environmentally harmful particles. “At the Institute, we deal with the good particles in most of the projects – i.e. those particles that are immediately beneficial, such as colour pigments or chemical industry products, drugs, food or also batteries. Good particles occur almost everywhere, and they may be based on metal oxides, metals or biological products and food,” Professor Nirschl continues. Scientific interest above all focuses on process engineering relating to the particles.

Research at the Institute covers the entire process of particle production and processing in liquids: from the synthesis of various solid particles through the separation, mixing or even agglomeration of several particles to the measurement engineering or image producing examination of the particle currents. For this purpose, process engineering students have a wide range of small and medium-sized experimental plants and commercial filters, centrifuges and mixers and their disposal for experiments. “In order to be able to understand and describe the processes that are underway in the machines, we additionally use simulation methods. Here, we work together closely with our applied mathematics colleagues,” process engineer Professor Nirschl explains. Mathematics supplies the corresponding algorithms enabling simulations of particle currents in very different types of plant.

As a rule, currents loaded with solid particles are analysed with the aid of small and medium-sized plants such as this filtering equipment at the Institute for Mechanical Process Engineering and Mechanics (MVM). Applied mathematics supplements process engineering with numerical simulation of the expected particle currents and facilitates process analysis and optimisation. (Image: Markus Breig / KIT)

Digital process engineering

The process engineering equipment is usually made of opaque steel inside of which the processes take place at an amazing speed. Often, it is impossible to directly follow the particle flows and draw conclusions regarding process efficiency. In industry, this problem is usually solved by controlling the end product in the laboratory and deducing process modifications from this that are implemented step by step. Explaining the advantages of digitisation, Dr Mathias J. Krause, Director of the Lattice Boltzmann Research Group (LBRG), an interdisciplinary group of junior scientist at KIT, says: “Numerical simulation now gives us the opportunity to have a closer look at the complex dynamics of this process. Using the CAD models of the plant and reference data of the process, we can predict how the parameters behave under the influence of different parameters,” Dr Krause explains.

“My specialty is the Lattice-Boltzmann method, which is based on strongly simplified particle microdynamics and makes transporting processes predictable. It is particularly suitable for the calculation of multi-phase currents in complex geometries of the apparatuses.” Parameters such as speed, raw material and energy input can be finely adjusted in the simulation, so that ultimately, the perfect process parameters for many applications can be individually decoded.

Industry also welcomes such digital process forecasting, so that numerous application-oriented projects are being prepared. Professor Nirschl emphasises: “This is not only about observing processes but also about optimising them. Companies are grateful for our being able to visualise their processes.” A wide range of options suggest themselves for entrepreneurs to delve into the particle-guiding processes in the real-life apparatuses. Professor Nirschl adds: “Such projects are a win-win situation. While we are participating via application-oriented research, industrial partners can benefit from the deep insights into the particle currents and from sound recommendations on process optimisation.” The achievable increase in efficiency or possible economising with optimised production processes are clear advantages in competitive markets.

In the refrigerated lorry shown above, heat currents inside and outside vacuum insulation panels are visualised. Such 3D simulations become even more tangible with the aid of virtual reality. (Image: Institute for Mechanical Process Engineering and Mechanics, edited by Karola Janz / KIT)

Particles in practice

The tremendous variety of particles is matched by the wide range of different fields in which the researchers are examining the particle currents. One classic application is that of separating particles on the basis of defined properties, which is referred to as classification. One of its elements is that of applying centrifugal forces, for example in order to sort particles according to their size. In this manner, a class with a certain size of particles can be extracted and processed – for example, sifting out very fine minerals used to create the colour of white. To obtain a uniform colour image, the particles have to be virtually identical. Here, process engineers are able to find out how the adjustable centrifugal forces can be employed to achieve the desired distribution of sizes.

In contrast, maintaining the purity of particle currents can also be considered, for example in paint lines in the automobile industry. As Professor Nirschl describes, “the most minute residues of previous manufacturing step, for example welding beads, can get into the paint tank full of paint liquid in spite of the car body having been cleaned. If painting is to finish without any optical flaws resulting from these impurities, the welding beads have to be filtered out of the paint. For this purpose, magnetic separators are used that continuously remove the metal particles from the paint liquid.” An optimum, efficient design of such plants is what process engineering aims for.

The influence of the occurrence of particle flows on dependent processes such as thermic energy currents is demonstrated with the example of vacuum insulation panels (VIP). The highly efficient heat insulation panels are filled with particles. Conditioned by a vacuum created in the panels, the air particles are prevented from touching each other, which causes low thermal conductivity. However, efficient thermal insulation can only be achieved if the particle conglomerates within the insulation panels have suitably chosen and processed. “With the aid of simulations, we can identify the optimum parameters for the particle system, the manufacturing process and the resulting heat currents in the insulating system,” mathematician Dr Krause explains.

In the magnetic separator, a static magnetic field is generated by permanent magnets (red, green). When a liquid flows through the separator, the metallic particles in the cylinder are separated by the magnetic forces. (Image: Institute for Mechanical Process Engineering and Mechanics / KIT)

Perfect process prediction

It is not only in these examples that digital process engineering contributes to getting the most out of a process: e.g. a high throughput with a minimum raw material or energy input. At the moment, the respective sub-steps of an overall process are analysed and calculated for simulation. As yet, the technical framework does not allow the process in its entirety to be analysed. All the steps in a process are immediately connected and mutually influence themselves – changes in one sub-step have an impact on the successive steps, and changes in the initial context influence several sub-steps simultaneously. Thus it is not enough to merely optimise individual steps in isolation.

Looking ahead towards the future of Industry 4.0, Professor Nirschl notes: “Our vision is to create integrated virtual process engineering. With the aid of a digital image, the ‘digital twin’, the overall process in a plant can be analysed during production, and in parallel, simulation calculates predictions, establishes the optimum process parameters as a forecast and ultimately intervenes in the real process to control and, above all, to optimise.”

At the Institute, Professor Nirschl and Dr Krause are working together to prepare the employment of augmented reality in particle simulation and are advancing new technologies. Clarifying his teaching philosophy, Institute Director Nirschl states: “Our study programmes impart the necessary basic knowledge of process engineering and pave the way to industry with collaborative ventures. In the large number of development projects that we are running, it is not rare for our students to forge important contacts for their future careers. We practise knowledge transfer via individuals, and in the long run, we get important impulses from industry for new research topics.”

Simulation of the classification of crushed minerals in a decanter centrifuge. Particles of a defined size are specifically sorted out, for example limestone for use as white pigmentation. (Image: Institute for Mechanical Process Engineering and Mechanics / KIT)

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Bilder v.o.n.u: Markus Breig / KIT Markus Breig / KIT Institute for Mechanical Process Engineering and Mechanics, edited by Karola Janz / KIT Institute for Mechanical Process Engineering and Mechanics / KIT Institute for Mechanical Process Engineering and Mechanics / KIT

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