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As a complicated and slow control process, there are several control objectives of the grate cooler, which are determined by design parameters. To compute the time delay of the design parameters automatically, we propose an improved long short-term memory with adaptive computation time (ACT-LSTM) model for objective prediction. An improved multi-objective optimization algorithm named bounded stable non-dominated sorting genetic algorithm II (BS-NSGA-II) is proposed to solve the optimal solutions. With the proposed methods, the average electricity consumption is reduced by 13.2%, the secondary air temperature is increased, the clinker outlet temperature is stabilized in a reasonable range, and the design parameters change smoothly. The experiment results have indicated that the proposed method is effective in the optimization of objectives and the stability operation of the equipment.<\/jats:p>","DOI":"10.1177\/01423312211023017","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T02:51:45Z","timestamp":1624848705000},"page":"3399-3412","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-objective optimization design of cement grate cooler control system based on improved long short-term memory network"],"prefix":"10.1177","volume":"43","author":[{"given":"Wang","family":"Hongyi","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Control of Electrical Equipment in Tianjin, Tiangong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Kun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Control of Electrical Equipment in Tianjin, Tiangong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhu","family":"Xinjun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Control of Electrical Equipment in Tianjin, Tiangong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1753-5151","authenticated-orcid":false,"given":"Song","family":"Limei","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Control of Electrical Equipment in Tianjin, Tiangong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8478-8928","authenticated-orcid":false,"given":"Dong","family":"Feng","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Process Measurement and Control, School of Electrical and Information Engineering, Tianjin University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2021,6,27]]},"reference":[{"key":"bibr1-01423312211023017","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2012.06.074"},{"key":"bibr2-01423312211023017","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2015.08.044"},{"key":"bibr3-01423312211023017","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2012.03.060"},{"key":"bibr4-01423312211023017","doi-asserted-by":"publisher","DOI":"10.1109\/CITCON.2005.1516348"},{"key":"bibr5-01423312211023017","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2017.01.083"},{"key":"bibr6-01423312211023017","doi-asserted-by":"publisher","DOI":"10.1016\/S1359-4311(98)00012-X"},{"key":"bibr7-01423312211023017","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.02.141"},{"key":"bibr8-01423312211023017","unstructured":"Graves A. and Google DeepMind (2016) Adaptive computation time for recurrent neural networks. 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