{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:18:57Z","timestamp":1753881537391,"version":"3.41.2"},"reference-count":43,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["Grant No. F2017201208"],"award-info":[{"award-number":["Grant No. F2017201208"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:p> The production of nitrogen oxides (NO<jats:sub>x<\/jats:sub>) in coal-fired boiler combustion has been found as a significant source of environmental pollution. Flue gas denitrification is a standard NO<jats:sub>x<\/jats:sub> control technology for small- and medium-sized coal-fired boilers. Achieving steady-state control in flue gas denitrification can be challenging since coal-fired boiler systems have complexity and significant delay. A model based on a learning-based K-nearest neighbor (KNN) query mechanism created for NO<jats:sub>x<\/jats:sub> output soft prediction is proposed in this study. First, a knowledge base in the proposed model is established through spatial division in accordance with the previous combustion parameters. Moreover, the clusters are established based on the output NO<jats:sub>x<\/jats:sub> values. Next, the domain of values of combustion parameters for the respective cluster is obtained. Second, the optimal cluster is selected using the knowledge base for an input vector q with new combustion parameters ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]. Lastly, the K tuples in the cluster the closest to the values of the input vector q are adopted to predict the output NO<jats:sub>x<\/jats:sub> value of q. The predicted NO<jats:sub>x<\/jats:sub> value can serve as a feedforward signal to control the output of the reductant for accurate denitrification. As revealed by the experimental results, the proposed practical model, capable of conducting the prediction in a sub-second time, is highly competitive with existing techniques. Furthermore, a deep learning algorithm (DLA) is designed, whereas it underperforms the KNN model. <\/jats:p>","DOI":"10.1142\/s0218001422510144","type":"journal-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T04:46:04Z","timestamp":1660279564000},"source":"Crossref","is-referenced-by-count":2,"title":["Modeling and Prediction of NO<sub><i>x<\/i><\/sub> Emission of a Coal-Fired Boiler by a Learning-Based <i>K<\/i>NN Mechanism"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3209-3232","authenticated-orcid":false,"given":"Xin","family":"Song","sequence":"first","affiliation":[{"name":"School of Cyber Security and Computer Science, Hebei University, Baoding, Hebei 071002, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Cyber Security and Computer Science, Hebei University, Baoding, Hebei 071002, P. R. 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