{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:04:59Z","timestamp":1753887899426,"version":"3.41.2"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T00:00:00Z","timestamp":1628467200000},"content-version":"vor","delay-in-days":220,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Burning of coal in power plants produces excessive nitrogen oxide (NO<jats:sub>x<\/jats:sub>) emissions, which endanger people\u2019s health. Proven and effective methods are highly needed to reduce NO<jats:sub>x<\/jats:sub> emissions. This paper constructs an echo state network (ESN) model of the interaction between NO<jats:sub>x<\/jats:sub> emissions and the operational parameters in terms of real historical data. The grey wolf optimization (GWO) algorithm is employed to improve the ESN model accuracy. The operational parameters are subsequently optimized via the GWO algorithm to finally cut down the NO<jats:sub>x<\/jats:sub> emissions. The experimental results show that the ESN model of the NO<jats:sub>x<\/jats:sub> emissions is more accurate than both of the LSTM and ELM models. The simulation results show NO<jats:sub>x<\/jats:sub> emission reduction in three selected cases by 16.5%, 15.6%, and 10.2%, respectively.<\/jats:p>","DOI":"10.1155\/2021\/9958972","type":"journal-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T21:06:15Z","timestamp":1628543175000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lowering Nitrogen Oxide Emissions in a Coal\u2010Powered 1000\u2010MW 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