Smart waste management
Municipal environmental protection associations (GVUs), such as Scheibbs and Melk, are striving to reduce contaminants in organic waste. Together with their DIHOST node partner, the St. Pölten University of Applied Sciences, a concept is being developed to motivate citizens to improve their waste separation practices and to implement this process among key stakeholders (waste producers/citizens, waste collectors, waste transporters, composting and biogas plants, and GVUs) using digital technologies and/or mobile applications.
Project goal: Development of a technical solution, e.g., an app, for operational implementation, enabling the GVU to provide owners with information based on clear evidence (photography or unambiguous measurement) – such as explanation, labeling, incentives, penalties, or further training – depending on reports from waste collectors or detection by the detection system described above. The overall process must be designed to minimize or eliminate any additional workload for the waste collectors.
To define the criteria that are important for the waste collectors as well as for other stakeholders (waste management companies, transporters, recyclers) involved in such systems, an initial workshop will be conducted (Design Thinking / Data-User-Task Analysis). Generally, existing markings, sensors, and measuring devices already installed in the waste bins and collection vehicles/systems should be utilized. Examples include a numerical code on the plastic lid of the organic waste bin, current GPS signal from a smartphone, and NFC chips in the bins that detect the emptying process.
This will allow for the identification of the owners and their behavior, and the recording of the waste separation performance at the point of collection.
All solutions (hardware and software) will be made available to the community as open source.
Fewer contaminants in organic waste!
Less organic waste in residual waste!
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Phase 1 [Workshop]: Design Thinking workshop with all stakeholders, especially including the commissioning body. Data User Task Analysis/Requirements Engineering (defining input and output), process analysis, defining framework conditions.
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Phase 2 [Desktop research]: Current state of (industrial) research and technology.
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Phase 3 [Implementation]: Prototypical implementation in iterative steps (including interim testing with stakeholders on-site).
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Phase 4 [Dissemination]: Publish the open-source solution, e.g., on GitHub. Dissemination and use via the GVU-Scheibbs channels (possibly with a specialist event).
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Phase 5 [not included]: "Commercial" operation including maintenance.
We will report on the project after completion!