The aim of this project is to develop a process for recycling this glycerol waste in terms of the circular economy on a laboratory scale. By adding further separation steps, the efficiency of the entire G2P process is to be further increased by reducing the waste streams and increasing the number of recyclable products, i.e. purified, unconverted glycerol and salts. To ensure the optimal operation of the glycerol waste process in view of the highly fluctuating feedstocks, a digital twin for this process will be created, which will be linked to machine learning methods. The latter are expected to significantly improve the robustness and efficiency of the glycerine waste recycling process by recognising the respective feedstock and reacting to it in the operating mode of the glycerine waste plant. reacting to it in the operating mode of the glycerine waste plant, with the aim of landfilling as little waste as possible and producing as many recyclable products as possible. Proof of the savings will be provided in the project by working out concrete, commercially relevant use cases.
|Effective start/end date||1/01/23 → 31/12/25|
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