@InProceedings{8553433,
  author = {A. Alexiou and J. Schenk},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Species Related Gas Tracking in Distribution Grids},
  year = {2018},
  pages = {321-325},
  abstract = {Due to a wider diversification of gas sources, today tracking gas in distribution grids is of great interest for gas grid operators to provide fair invoicing of gas customers. Substitute natural gas (SNG), e.g. derived from raw biogas, injected concurrently into natural gas grids may differ in its calorific value Hs compared to fossil natural gas in the grid. This is manifesting in deviating chemical compositions of injected grid gases. Remarkably, the chemical fractions of SNGs fluctuate significantly over time exhibiting time-dependent signatures. Sampling over relevant features of injected gases, e.g. the chemical species concentrations at standard temperature and pressure, by means of calibrated sensors, provides time-dependent signals which can be taken for gas tracking purposes. To that end, we present an accurate technique to estimate the transit times of gas between nodes, e.g. from an entry to an exit point. As a result, calorific value extrapolation from one gas grid node to a downstream node, with an accuracy sufficient for gas customer invoicing, is feasible. In an experimental section we show a normalized root-mean-square deviation (NRMSD) with respect to calorific value estimation.},
  keywords = {biofuel;calibration;fuel gasification;invoicing;mean square error methods;natural gas technology;distribution grids;natural gas grids;calorific value extrapolation;gas customer invoicing;calorific value estimation;biogas;substitute natural gas;chemical fractions;normalized root-mean-square deviation;Natural gas;Chemicals;Gas detectors;Indexes;Viterbi algorithm;Time series analysis;Viterbi algorithm;dynamic time warping;gas tracking;calorific value tracking;distribution grids},
  doi = {10.23919/EUSIPCO.2018.8553433},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437545.pdf},
}

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This is manifesting in deviating chemical compositions of injected grid gases. Remarkably, the chemical fractions of SNGs fluctuate significantly over time exhibiting time-dependent signatures. Sampling over relevant features of injected gases, e.g. the chemical species concentrations at standard temperature and pressure, by means of calibrated sensors, provides time-dependent signals which can be taken for gas tracking purposes. To that end, we present an accurate technique to estimate the transit times of gas between nodes, e.g. from an entry to an exit point. As a result, calorific value extrapolation from one gas grid node to a downstream node, with an accuracy sufficient for gas customer invoicing, is feasible. In an experimental section we show a normalized root-mean-square deviation (NRMSD) with respect to calorific value estimation.","keywords":"biofuel;calibration;fuel gasification;invoicing;mean square error methods;natural gas technology;distribution grids;natural gas grids;calorific value extrapolation;gas customer invoicing;calorific value estimation;biogas;substitute natural gas;chemical fractions;normalized root-mean-square deviation;Natural gas;Chemicals;Gas detectors;Indexes;Viterbi algorithm;Time series analysis;Viterbi algorithm;dynamic time warping;gas tracking;calorific value tracking;distribution grids","doi":"10.23919/EUSIPCO.2018.8553433","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437545.pdf","bibtex":"@InProceedings{8553433,\n author = {A. Alexiou and J. Schenk},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Species Related Gas Tracking in Distribution Grids},\n year = {2018},\n pages = {321-325},\n abstract = {Due to a wider diversification of gas sources, today tracking gas in distribution grids is of great interest for gas grid operators to provide fair invoicing of gas customers. Substitute natural gas (SNG), e.g. derived from raw biogas, injected concurrently into natural gas grids may differ in its calorific value Hs compared to fossil natural gas in the grid. This is manifesting in deviating chemical compositions of injected grid gases. Remarkably, the chemical fractions of SNGs fluctuate significantly over time exhibiting time-dependent signatures. Sampling over relevant features of injected gases, e.g. the chemical species concentrations at standard temperature and pressure, by means of calibrated sensors, provides time-dependent signals which can be taken for gas tracking purposes. To that end, we present an accurate technique to estimate the transit times of gas between nodes, e.g. from an entry to an exit point. As a result, calorific value extrapolation from one gas grid node to a downstream node, with an accuracy sufficient for gas customer invoicing, is feasible. In an experimental section we show a normalized root-mean-square deviation (NRMSD) with respect to calorific value estimation.},\n keywords = {biofuel;calibration;fuel gasification;invoicing;mean square error methods;natural gas technology;distribution grids;natural gas grids;calorific value extrapolation;gas customer invoicing;calorific value estimation;biogas;substitute natural gas;chemical fractions;normalized root-mean-square deviation;Natural gas;Chemicals;Gas detectors;Indexes;Viterbi algorithm;Time series analysis;Viterbi algorithm;dynamic time warping;gas tracking;calorific value tracking;distribution grids},\n doi = {10.23919/EUSIPCO.2018.8553433},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437545.pdf},\n}\n\n","author_short":["Alexiou, A.","Schenk, J."],"key":"8553433","id":"8553433","bibbaseid":"alexiou-schenk-speciesrelatedgastrackingindistributiongrids-2018","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437545.pdf"},"keyword":["biofuel;calibration;fuel gasification;invoicing;mean square error methods;natural gas technology;distribution grids;natural gas grids;calorific value extrapolation;gas customer invoicing;calorific value estimation;biogas;substitute natural gas;chemical fractions;normalized root-mean-square deviation;Natural gas;Chemicals;Gas detectors;Indexes;Viterbi algorithm;Time series analysis;Viterbi algorithm;dynamic time warping;gas tracking;calorific value tracking;distribution grids"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2018url.bib","creationDate":"2021-02-13T15:38:40.427Z","downloads":0,"keywords":["biofuel;calibration;fuel gasification;invoicing;mean square error methods;natural gas technology;distribution grids;natural gas grids;calorific value extrapolation;gas customer invoicing;calorific value estimation;biogas;substitute natural gas;chemical fractions;normalized root-mean-square deviation;natural gas;chemicals;gas detectors;indexes;viterbi algorithm;time series analysis;viterbi algorithm;dynamic time warping;gas tracking;calorific value tracking;distribution grids"],"search_terms":["species","related","gas","tracking","distribution","grids","alexiou","schenk"],"title":"Species Related Gas Tracking in Distribution Grids","year":2018,"dataSources":["yiZioZximP7hphDpY","iuBeKSmaES2fHcEE9"]}