
Artificial Intelligence
Artificial intelligence is transforming the industry
Optimized Recycling Technology
Starting in 2025, our mobile recycling lines will be controlled by artificial intelligence, bringing revolutionary changes to the way waste is processed.
Digital Twin as the Key to Innovation
The newly designed recycling lines will also feature a digital twin. This advanced tool will not only assist in design and operator training but will also enable the deployment of innovative control algorithms utilizing artificial intelligence, machine learning, and model-based control (MBC). The digital twin will gather data from the running line and from a global cloud system connected to other recycling line installations. This ensures that control algorithms are optimally set and adaptively adjusted to different waste variations, ensuring consistent output quality.
Advantages of Data Integration
This system will allow for the sharing of data and knowledge gathered from different lines, leading to improved efficiency and process optimization. A predictive maintenance algorithm will monitor the entire system, identify potential faults based on data from other installations, and provide alerts, increasing the reliability and safety of the entire solution.
Our new solution will bring not only technological advancement but also enhance the efficiency and sustainability of recycling processes.
Technology and Automation
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Process Phase Decomposition: Implementation of PLC control systems, control panels, frequency converters, and sensors.
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Process Data Collection: Gathering data on granulation, moisture, quantity, and quality of input and output materials, technological values (e.g., mill rotations, motor speeds), and energy consumption.
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Parameter Optimization: Continuous improvement of process parameters for maximum efficiency.
Data Processing and Presentation
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Data Collection: The Industrial Internet of Things (IIoT) will enable continuous data acquisition and archiving.
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Efficiency Evaluation: Static analysis of process data and energy balance will provide an overview of the system's performance.
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Cloud Solution: Ensuring data management and service provision via cloud platforms.
Modern Methods and Simulations
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Process Simulation: Grinding and separation simulations for better understanding and optimization of processes.
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Modeling: Use of mathematical tools, machine learning, and artificial intelligence for data-driven modeling.
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Optimization: Predictive control of technology based on the characteristics of input materials and optimization of the resulting granulate.
Evaluation and Integration
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Technology Limit Evaluation: Identifying possibilities and limitations in technology control.
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Statistical Evaluation: Use of advanced mathematical tools for analysis and optimization.
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Integration: Data transfer between services, data preparation for machine processing, and integration of results into PLC systems.