Prevention of material accumulations at risk of collapse

MVV Industriepark Gersthofen GmbH: Ensuring safe detection and demand-based control of waste streams to avoid the risk of material collapse in the RDF cogeneration plant through LiDAR-based volume monitoring

概要

MVV Industriepark Gersthofen GmbH operates a complex chemical production and service site in Gersthofen, north of Augsburg. To best serve the eleven resident companies, a specialized infrastructure has been established. A crucial component of this infrastructure is the Refuse Derived Fuel (RDF) cogeneration plant, essential for providing process steam to the industrial park. Here, waste is thermally treated and converted into energy. Ensuring optimal operation of the facility requires knowledge of the location and quantity of waste in the waste bunker. This is also critical for safety reasons, as undiscovered material accumulations on the bunker walls can pose a significant safety risk to operations.

To minimize this risk and optimize waste distribution, MVV Industriepark Gersthofen employs Blickfeld’s Volume Monitoring solution, incorporating Smart LiDAR sensors and a data dashboard. This provides the company with precise volume data and real-time warnings about bunker contents, enabling improved inventory control.

課題

Previously, bunker materials were manually calculated by repeatedly lowering the waste grabber and crane scale, resulting in inaccurate, point-specific data and increased operational effort. This method also increased crane wear, as it required more trips for height measurement than for withdrawal for the power plant feed.

Additionally, the stored bunker material consists primarily of pre-sorted commercial waste from the construction industry, often containing larger, heavier, irregularly shaped objects. This poses the risk of material accumulations at the edges, known as “cornices”, which can damage the crane grabber if unstable or collapsing, potentially leading to operational downtime with far-reaching consequences. The fixed position of the crane grabber encourages the formation of cornices. To address this, the company previously relied on cameras, which, however, did not provide precise height information.

The goal was thus to install a bunker volume and weight detection technology that, under harsh environmental conditions, not only provides reliable inventory data for operations but also detects cornices early to prevent damage or failures.

ソリューション

For volume monitoring, three Blickfeld Cube 1 units are deployed, providing an overview of the entire bunker and generating a precise 3D point cloud of stored waste. The associated perception software establishes six detection zones where volume is calculated based on bulk density. A threshold is defined for each zone, triggering material relocation upon exceeding it to avoid safety risks.

Volume data dashboard for safe waste stream management — Data accumulated in the customized MVV dashboard

The captured data and bunker point cloud are visualized in real-time on a dashboard, significantly reducing operational effort. MVV Industriepark Gersthofen now has access to current inventory data, allowing precise bunker space planning and fuel allocation for the power plant. Concurrently, effective prevention of material accumulations is facilitated through alarm logic.

In the past, the fill level of the bunker was determined by lowering a waste gripper multiple times, while cameras were used to get a look inside. However, the data collected was often inaccurate, and, in particular, accumulations of material at the walls, the so-called cornices, sometimes remained undetected due to the harsh environmental conditions. Thanks to the introduction of LiDAR-based Volume Monitoring, we receive not only precise data but also a 3D visualization of the bunker content in real-time. This allows us to plan more effectively and react proactively to such potential problems.

Patrick Schwegler

Project Engineer Energy Supply at MVV Industriepark Gersthofen GmbH

展望

MVV Industriepark Gersthofen plans to automate crane control for material feed to combustion based on LiDAR-captured data, further reducing manual effort and optimizing processes.

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