{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:48:25Z","timestamp":1782402505893,"version":"3.54.5"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T00:00:00Z","timestamp":1629244800000},"content-version":"vor","delay-in-days":229,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["5207041692"],"award-info":[{"award-number":["5207041692"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of multiple control parameters, the cubic spline interpolation fitting model was used to realize the data integration of multiple detection periods. The large time delay of the blast furnace iron making process was analyzed. Moreover, Spearman analysis was combined with the weighted moving average method to optimize the data set of silicon content prediction. Aiming at the problem of low prediction accuracy of the ordinary neural network model, genetic algorithm was used to optimize parameters on the BP neural network model to improve the convergence speed of the model to achieve global optimization. Combined with the autocorrelation analysis of the hot metal silicon content, a modified model for the prediction of hot metal silicon content based on error analysis was proposed to further improve the accuracy of the prediction. The model comprehensively considers problems such as data detection inconsistency, large time delay, and inaccuracy of prediction results. Its average absolute error is 0.05009, which can be used in actual production.<\/jats:p>","DOI":"10.1155\/2021\/1767308","type":"journal-article","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T18:22:20Z","timestamp":1629310940000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["[Retracted] Prediction Model of Hot Metal Silicon Content Based on Improved GA\u2010BPNN"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7560-2610","authenticated-orcid":false,"given":"Zeqian","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3501-1610","authenticated-orcid":false,"given":"Yang","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1841-2698","authenticated-orcid":false,"given":"Chaomeng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0184-0234","authenticated-orcid":false,"given":"Yafeng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4716-2376","authenticated-orcid":false,"given":"Mansheng","family":"Chu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"e_1_2_8_1_2","first-page":"51","article-title":"Intelligent prediction of silicon content in hot metal of blast furnace based on neural network time series model","volume":"45","author":"Cui Z.","year":"2021","journal-title":"Metallurgical Industry Automation"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/03019233.2020.1807288"},{"key":"e_1_2_8_3_2","volume-title":"The Analysis on Blast Furnace Smelting Process and Research on Hot Metal Silicon Content Prediction model","author":"Wu J.","year":"2016"},{"key":"e_1_2_8_4_2","first-page":"87","article-title":"Analysis of stability of silicon content in hot metal based on variable neighborhood particle swarm optimization algorithm","volume":"29","author":"Yang K.","year":"2017","journal-title":"Journal of Iron and Steel Research"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2021.112875"},{"key":"e_1_2_8_6_2","first-page":"729","article-title":"Application of improved EMD-Elman neural network to predict silicon content in hot metal","volume":"67","author":"Song J.","year":"2016","journal-title":"Computers & Industrial Engineering-Journal"},{"key":"e_1_2_8_7_2","article-title":"Period-aware content attention RNNs for time series forecasting with missing values","volume":"312","author":"Gizem C. 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