Soft sensor for the prediction of oxygen content in boiler flue gas using neural networks and extreme gradient boosting

Research article (Neural Computing and Applications, 2022) · cited 21× · AI/ML
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Soft sensor for the prediction of oxygen content in boiler flue gas using neural networks and extreme gradient boosting

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Soft sensor for the prediction of oxygen content in boiler flue gas using neural networks and extreme gradient boosting is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Soft sensor for the prediction of oxygen content in boiler flue gas using neural networks and extreme gradient boosting. Retrieved May 24, 2026, from https://4ort.xyz/entity/soft-sensor-for-the-prediction-of-oxygen-content-in-boiler-flue-gas-using-neural-networks-and-extreme-gradient-boosting
MLA “Soft sensor for the prediction of oxygen content in boiler flue gas using neural networks and extreme gradient boosting.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/soft-sensor-for-the-prediction-of-oxygen-content-in-boiler-flue-gas-using-neural-networks-and-extreme-gradient-boosting.
BibTeX @misc{4ortxyz_soft-sensor-for-the-prediction-of-oxygen-content-in-boiler-flue-gas-using-neural-networks-and-extreme-gradient-boosting_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Soft sensor for the prediction of oxygen content in boiler flue gas using neural networks and extreme gradient boosting}}, year = {2026}, url = {https://4ort.xyz/entity/soft-sensor-for-the-prediction-of-oxygen-content-in-boiler-flue-gas-using-neural-networks-and-extreme-gradient-boosting}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Soft sensor for the prediction of oxygen content in boiler flue gas using neural networks and extreme gradient boosting — https://4ort.xyz/entity/soft-sensor-for-the-prediction-of-oxygen-content-in-boiler-flue-gas-using-neural-networks-and-extreme-gradient-boosting (retrieved 2026-05-24)

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