Abstract
This study illustrates an approach to provide an easily manageable tool for end-users to predict the biogas production rate to ease the transition to a flexible and proactive operation of digesters at both biogas plants as well as at wastewater treatment plants. To provide a high flexibility to end-users but keep BioTOOL easily manageable, the approach uses a pre-trained artificial neural networks library consisting of different variable combinations. In BioTOOL’s workflow, end-user will be asked for current measurements disregarding the combination of those. BioTOOL uses a seek-and-implement method to find the correct stored network for the entered input. This approach results in predictions similar to the optimized pre-trained network. It has been demonstrated that the idea of a flexible prediction tool could be fully realized.
| Original language | English |
|---|---|
| Pages (from-to) | 87-94 |
| Number of pages | 8 |
| Journal | Environmental Modeling and Assessment |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| State | Published - 1 Feb 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 6 Clean Water and Sanitation
Keywords
- Artificial neural network
- Biogas prediction tool
- Power generation prediction
- Wastewater treatment
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