{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T18:23:23Z","timestamp":1776536603800,"version":"3.51.2"},"reference-count":30,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2015,9]]},"abstract":"<jats:p> A common step in most of water treatment plants is the chemical coagulation. The chemical coagulation is the process of destabilizing the colloidal particles suspended in raw water by the addition of coagulants. Generally, the determination of the quantity of coagulant to be added to water is made manually by jar tests. However, the manual control has slow response to changes of raw water and it requires intensive laboratory analysis. To reduce the manual effort and to improve the response to change in raw water quality, this work proposes the determination of the coagulant dosage using dynamic neural network modeling using the available sensors as input of the model. The case of study is a large scale water treatment plant in Cear\u00e1, Brazil, where the kinds of coagulants added to water are the aluminum sulphate (AS) and poly aluminum chloride (PAC). Several dynamic neural network models with different combinations of the input variables have been evaluated. The best solution found is composed by a nonlinear autoregressive with exogenous input (NARX) model having three input variables, the pH in raw and coagulated water, and the turbidity in the coagulated water, with coefficient of determination of R<jats:sup>2<\/jats:sup> = 0.95 and R<jats:sup>2<\/jats:sup> = 0.91 for the AS and PAC dosage prediction, respectively. <\/jats:p>","DOI":"10.1142\/s1469026815500133","type":"journal-article","created":{"date-parts":[[2015,9,15]],"date-time":"2015-09-15T21:28:40Z","timestamp":1442352520000},"page":"1550013","source":"Crossref","is-referenced-by-count":13,"title":["Coagulant Dosage Determination in a Water Treatment Plant Using Dynamic Neural Network Models"],"prefix":"10.1142","volume":"14","author":[{"given":"Leonaldo Silva","family":"Gomes","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Cear\u00e1, Cear\u00e1, Brazil"},{"name":"Research Group for Automation and Robotics, Federal University of Cear\u00e1, Cear\u00e1, Brazil"}]},{"given":"Francisco Alexandre A.","family":"Souza","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Cear\u00e1, Cear\u00e1, Brazil"},{"name":"Research Group for Automation and Robotics, Federal University of Cear\u00e1, Cear\u00e1, Brazil"}]},{"given":"Ricardo Silva Th\u00e9","family":"Pontes","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Cear\u00e1, Cear\u00e1, Brazil"},{"name":"Research Group for Automation and Robotics, Federal University of Cear\u00e1, Cear\u00e1, Brazil"}]},{"given":"Tobias R.","family":"Fernandes Neto","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Cear\u00e1, Cear\u00e1, Brazil"},{"name":"Research Group for Automation and Robotics, Federal University of Cear\u00e1, Cear\u00e1, Brazil"}]},{"given":"Rui Alexandre M.","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Coimbra, P\u00f3lo II Coimbra, Portugal"},{"name":"Institute of Systems and Robotics, University of Coimbra, P\u00f3lo II, Coimbra, Portugal"}]}],"member":"219","published-online":{"date-parts":[[2015,9,15]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2004.843217"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.1.151"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1016\/S0954-1810(00)00012-1"},{"key":"rf4","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop C. 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