Waste Water Treatment Plants and the Smart Grid
Bibliographic record
Abstract
Denmark's political ambitions of a fossil fuel free energy system by 2050 calls for more renewable energy sources such as wind and solar. These green energy resources fluctuate and the transition to a green energy system requires a Smart Grid with flexible consumers that balance the fluctuating power production. The energy-heavy processes for waste water transport and treatment could potentially provide a flexible operation with storage capabilities and be a valuable asset to a Smart Grid. In order to enable Waste Water Treatment Plants (WWTPs) as flexible prosumers in the future Smart Grid, we must update their process control system to model based predictive control that monitors the changed flexible operation and plans ahead. The primary aim of a WWTP is to treat the incoming waste water as much as possible to ensure a sufficient effluent water quality and protect the environment of the recipient. The secondary aim is to treat the waste water using as little energy as possible. In the future waste water will be considered an energy resource, that contains valuable nutrients convertible to green biogas and in turn electricity and heat. In a Smart Grid consuming or producing energy at the right time is key to both lower plant electricity costs and actively help to balance the energy system. Predictions of the WWTP and sewer system operation could help a model based controller to adapt power consumption and production according to the energy system flexibility needs; incentivized through energy markets and prices. We are in the process of upgrading the current control system to prepare a flexible operation and Smart Grid market integration. The prototype system will be tested online at a plant in Denmark, that in the current market could save up to 300.000 DKK/year in electricity costs. The solution is based on existing available online plant sensors and is expected to be part of Krüger’s advanced process control software STAR control® already used at plants worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".