Hack the raw materials industry with us!
Enjoy free lunch and dinner with your teammates
Refuel yourself with drinks and coffee during the hackathon
Join Germany's Original Raw Materials Hackathon! Are you ready to tackle Industrial IoT and Engineering challenges, ignite your entrepreneurial spirit, and collaborate with bright minds from diverse fields? Join us in Aachen from Friday to Sunday, January 10-12, 2025, for an exciting weekend of innovation and creativity! Whether you're studying engineering, computing, natural sciences, business, or even philosophy, this is your chance to make a difference and unleash your potential. Participants from previous hackathons found job opportunities at companies or the university—so this could be your gateway to new career paths! Attendance is completely free—so gather your ideas and register now for a challenging and fun experience!
The Hack Mining!-Hackathon admittance starts at 16:00.
Location: Wüllnerstraße 2, 52056 Aachen, Bergbaugebäude RWTH, AMT Institute
Directions: Link Google Maps
If you have any organizational questions, please do not hesitate to get in touch with Pablo.
January 10 - 12, 2025
Kick-off: Friday, January 10 @ 16:00
Bergbaugebäude (Be114)
RWTH AMT Institute,
Wüllnerstraße 2, 52056 Aachen
Everyone is welcome!
Absolutely free! Lunch, dinner, and drinks provided.
Your laptop + peripherals & Machergeist.
WiFi and additional hardware will be provided.
January 8, 2025, 23:59:59
Check the prizes above!
Take a look at the challenges here!
Secure your spot at our three-day hackathon using the form below. Your registration will be confirmed shortly.
If you have any questions before the event, feel free to reach out to Pablo.
Company: Innomotics GmbH
In conventional raw material extraction plants, there are many kilometers between the extraction site (e.g. mine for copper ore) and the processing site (e.g. ore processing or port facility). Transport routes (e.g. Chile, Australia) are often built to connect the extraction site and the processing site, which are made up of many individual components (crushers, bunkers, feeders, belt conveyors, etc.). These individual components consist of many sub-components (motors, gearboxes, conveyed goods, bearings, idlers, etc.). A large number and variety of components distributed over several kilometres.
Many of these transport systems form the interface between the discontinuous extraction process and the continuous processing of the raw material. Redundancies are not common for cost reasons. The downtime of a processing plant, for example for copper ore, can mean losses of ~ US$450,000 / h for the operator. For this reason, there are usually raw material storage facilities upstream of the processing plant that ensure the continued operation of the processing plant for several days if the transport system is shut down. This makes it possible to carry out planned repairs or maintenance work on the transport system during defined periods.
The uninterrupted and plannable operation of a transport system is a prerequisite for the optimal extraction and further processing of raw materials. Due to the length of the transport system and the large number of components, continuous condition monitoring is not possible. Any problem recognised in good time can safeguard the operation of the plant and possibly save considerable costs.
Idea1: The task is to use the available data to develop appropriate correlations, trends or derivations that indicate a deterioration in the transport process or herald unplanned interruptions. This can relate to the condition of individual components (idlers, bearings, conveyor belt, etc.) or to process-related problems (overloading, chute blockages, etc.).
Company: DMT GmbH & Co. KG
This challenge involves developing a scalable model to predict rock density in drill cores by combining X-ray Fluorescence elemental data with hyperspectral mineral maps with the goal of producing accurate density predictions, which are crucial for resource estimation and extraction processes in mineral exploration. Participants will integrate the provided different data types and apply machine learning models to build the prediction model, which will be evaluated using an unknown test dataset.
Determining rock density is essential for optimizing extractive processes as it directly impacts resource estimation and rock mechanics which play a crucial role in mineral exploration and production. Rock density apart from other factors is controlled by the mineralogical and elemental composition in rock samples. The goal of the challenge is to integrate elemental data from X-ray Fluorescence (XRF) and mineralogical information from hyperspectral imaging to accurately predict density values in drill cores. Based on the data sets provided, a scalable model is to be developed that can accurately predict (within 5% tolerance) based on the available measurement data. The data will be evaluated against an unknown test set to check the robustness of the produced models.
Company: Andritz
Andritz peeler centrifuges are used for the solid-liquid separations in a broad scope of applications. The task of the centrifuge is always to separate solids from liquid, using the centrifugal force resulting from the rotational speed of the basket as driving force. The operation of the centrifuges is discontinuous which means that certain cycles are repeated after each other . The total cycle time should be kept as short as possible to optimize the throughput. Different variables influence the performance of the machine. Some of these variables can be set by the PLC parameters of the centrifuge and consequently changed to optimize the throughput. Other variables like product quality cannot be influenced. Depending on the settings and conditions the centrifuge can have higher imbalances due to an inhomogeneous distribution of solids in the basket. Since this can lead to a shorter lifetime due to mechanical damages, it is also important that the imbalances do not exceed a certain range. Target is to develop an algorithm which use existing centrifuge data with the different variables stored in an excel file to create a machine learning model to optimize the throughput of the centrifuge with minimum imbalances. The AI should be trained using these existing data and make a prediction for the optimum PLC parameters to achieve the best performance.
Company: Becker Mining Systems AG
Stay tuned! Details for this challenge will be announced soon.
Innomotics is a German electric motor group based in Nuremberg. The company was formed on 1 July 2023 through the spin-off of the corresponding business division of the Siemens Group in Germany. This also includes the previous direct Siemens subsidiaries Sykatec and Weiss Spindeltechnologie. According to Siemens, the global spin-off was to be completed by October 2023. In May 2024, Siemens announced the sale of Innomotics to the US company KPS Capital Partners. The sale was completed on 1 October 2024 at a value of 3.5 billion euros. The company produces low- and high-voltage motors, medium-voltage converters, geared motors and generators and offers customer service and complete solutions in two other business areas. s is a sample line with the explanation of the company This is a sample line with the explanation of the company This is a sample line with the explanation of the company This is a sample line with the explanation of the company
Andritz Today's globally active technology Group ANDRITZ developed from a small iron foundry founded in 1852 by Hungarian entrepreneur Josef Körösi in the Graz suburb of Andritz. With its approximately 1,200 highly qualified employees, ANDRITZ in Graz supplies all business areas with technologies and services. In close coordination with customers, new technologies are advanced and continuously developed in our R&D facilities and pilot plants. In the local workshop, components for approximately 90 different products for the Hydro, Pulp & Paper, Separation, and Metals business areas are manufactured on a production area of 95,000 m2. In addition, repairs, conversions and assemblies are carried out on site. ANDRITZ trains young skilled workers in its in-house apprentice workshop. ANDRITZ contributes significantly to local economic value creation and is of utmost importance for the local business location by creating or securing regional jobs within the company itself and indirectly through its cooperation with countless local suppliers. The company is strongly networked with research institutions and is a member of industrial and environmental clusters. For example, ANDRITZ recently joined the Green Tech Cluster, thus strengthening Styria as a hotspot for innovative energy and environmental technology. ANDRITZ wants to push digital initiatives and cooperation with young companies in the cluster.
DMT provides engineering services and consulting, globally and with 280 years of experience. With 13 engineering and consulting companies at 30 locations around the globe, we focus on the markets of plant engineering & process technology, infrastructure & construction, energy, and mining. Excellence and innovation in everything we do – that is our claim as an independent global engineering services and consulting company. Sustainable value creation for our clients is the goal. Knowledge, digitisation and globalisation are the keys to this. The group's headquarters (DMT GmbH & Co. KG) is located in Essen, Germany. We lead the engineering division of TÜV NORD GROUP, have numerous state-approved experts and accredited testing laboratories, and employ more than 100 recognised experts in this area alone. We are involved in more than 40 innovation projects, the majority at international level, and work on the digitisation of existing and new business models, services and products for ourselves and our customers. Furthermore, we act in partnership, in a spirit of trust, solution-oriented, sustainable and ethical – without exception and for the benefit of our customers. This is what we call engineering performance, and this is our attitude and our commitment to the good of a future that we all work to develop positively at all times.
Becker Mining Systems AG - Natural resources are limited and their extraction challenging. Our 1,500 employees worldwide support our customers in their transformation to climate-friendly resource extraction that increases customer productivity and production reliability while reducing their environmental footprint. Our high-quality, consistently reliable yet easy-to-integrate products secure satisfaction and loyalty in the mining industry worldwide.
The Institute for Advanced Mining Technologies of RWTH Aachen University develops technologies for the automation and digitalization of mining machines and processes to enable safe, efficient and responsible raw material extraction. An important core area of the institute is the development of sensor technologies and corresponding algorithms for the generation, acquisition and processing of data and information along the entire data-information-value-chain. The challenging environmental conditions in mines place particularly high demands on people, machines, sensors and algorithms and require robust, connected and intelligent systems and solutions.
The VDMA represents over 3,200 mainly small and medium size member companies in the engineering industry, making it one of the largest and most important industrial associations in Europe. With an export quota amounting to 96 per cent, mining technology is one of the most export-oriented branches of the German engineering industry. VDMA Mining represents well-known, mainly medium-sized companies from the sectors open cast mining/materials handling, underground mining, mining processing technology and consulting, research and development. 145 companies merged in VDMA Mining representing more than 90 per cent of the entire trade volume.