Estimation of aerator air demand by an embedded multi-gene genetic programming

A spillway discharging a high-speed flow is susceptible to cavitation damages. As a countermeasure, an aerator is often used to artificially entrain air into the flow. Its air demand is of relevance to cavitation reduction and requires accurate estimations. The main contribution of this study is to...

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Autores principales:	Shicheng Li, James Yang, Wei Liu
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	air demand empirical correlation multi-gene genetic programming solution optimization spillway aerator Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/9355167b1b7a48c9afbad83448b98153
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id	oai:doaj.org-article:9355167b1b7a48c9afbad83448b98153
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spelling	oai:doaj.org-article:9355167b1b7a48c9afbad83448b981532021-11-05T17:51:18ZEstimation of aerator air demand by an embedded multi-gene genetic programming1464-71411465-173410.2166/hydro.2021.037https://doaj.org/article/9355167b1b7a48c9afbad83448b981532021-09-01T00:00:00Zhttp://jh.iwaponline.com/content/23/5/1000https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734A spillway discharging a high-speed flow is susceptible to cavitation damages. As a countermeasure, an aerator is often used to artificially entrain air into the flow. Its air demand is of relevance to cavitation reduction and requires accurate estimations. The main contribution of this study is to establish an embedded multi-gene genetic programming (EMGGP) model for improved prediction of air demand. It is an MGGP-based framework coupled with the gene expression programming acting as a pre-processing technique for input determination and the Pareto front serving as a post-processing measure for solution optimization. Experimental data from a spillway aerator are used to develop and validate the proposed technique. Its performance is statistically evaluated by the coefficient of determination (CD), Nash–Sutcliffe coefficient (NSC), root-mean-square error (RMSE) and mean absolute error (MAE). Satisfactory predictions are yielded with CD = 0.95, NSC = 0.94, RMSE = 0.17 m3/s and MAE = 0.12 m3/s. Compared with the best empirical formula, the EMGGP approach enhances the fitness (CD and NSC) by 23% and reduces the errors (RMSE and MAE) by 48%. It also exhibits higher prediction accuracy and a simpler expressional form than the genetic programming solution. This study provides a procedure for the establishment of parameter relationships for similar hydraulic issues. HIGHLIGHTS An embedded multi-gene genetic programming is developed.; An explicit and simple correlation is proposed for air demand estimation.; Existing empirical models are assessed and recalibrated.; The proposed model improves the prediction accuracy significantly.;Shicheng LiJames YangWei LiuIWA Publishingarticleair demandempirical correlationmulti-gene genetic programmingsolution optimizationspillway aeratorInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 5, Pp 1000-1013 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	air demand empirical correlation multi-gene genetic programming solution optimization spillway aerator Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	air demand empirical correlation multi-gene genetic programming solution optimization spillway aerator Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Shicheng Li James Yang Wei Liu Estimation of aerator air demand by an embedded multi-gene genetic programming
description	A spillway discharging a high-speed flow is susceptible to cavitation damages. As a countermeasure, an aerator is often used to artificially entrain air into the flow. Its air demand is of relevance to cavitation reduction and requires accurate estimations. The main contribution of this study is to establish an embedded multi-gene genetic programming (EMGGP) model for improved prediction of air demand. It is an MGGP-based framework coupled with the gene expression programming acting as a pre-processing technique for input determination and the Pareto front serving as a post-processing measure for solution optimization. Experimental data from a spillway aerator are used to develop and validate the proposed technique. Its performance is statistically evaluated by the coefficient of determination (CD), Nash–Sutcliffe coefficient (NSC), root-mean-square error (RMSE) and mean absolute error (MAE). Satisfactory predictions are yielded with CD = 0.95, NSC = 0.94, RMSE = 0.17 m3/s and MAE = 0.12 m3/s. Compared with the best empirical formula, the EMGGP approach enhances the fitness (CD and NSC) by 23% and reduces the errors (RMSE and MAE) by 48%. It also exhibits higher prediction accuracy and a simpler expressional form than the genetic programming solution. This study provides a procedure for the establishment of parameter relationships for similar hydraulic issues. HIGHLIGHTS An embedded multi-gene genetic programming is developed.; An explicit and simple correlation is proposed for air demand estimation.; Existing empirical models are assessed and recalibrated.; The proposed model improves the prediction accuracy significantly.;
format	article
author	Shicheng Li James Yang Wei Liu
author_facet	Shicheng Li James Yang Wei Liu
author_sort	Shicheng Li
title	Estimation of aerator air demand by an embedded multi-gene genetic programming
title_short	Estimation of aerator air demand by an embedded multi-gene genetic programming
title_full	Estimation of aerator air demand by an embedded multi-gene genetic programming
title_fullStr	Estimation of aerator air demand by an embedded multi-gene genetic programming
title_full_unstemmed	Estimation of aerator air demand by an embedded multi-gene genetic programming
title_sort	estimation of aerator air demand by an embedded multi-gene genetic programming
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/9355167b1b7a48c9afbad83448b98153
work_keys_str_mv	AT shichengli estimationofaeratorairdemandbyanembeddedmultigenegeneticprogramming AT jamesyang estimationofaeratorairdemandbyanembeddedmultigenegeneticprogramming AT weiliu estimationofaeratorairdemandbyanembeddedmultigenegeneticprogramming
_version_	1718444113603330048

Estimation of aerator air demand by an embedded multi-gene genetic programming

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