Water–sediment separation efficiency prediction of gill-piece separation device
As the key piece of equipment of a micro-irrigation system, the filter can prevent clogging of the emitter and ensure normal operation of the micro-irrigation system. A gill-piece separation device is used for the removal of viscous sediment, which helps to reduce the sediment concentration and decr...
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IWA Publishing
2021
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oai:doaj.org-article:5fb0cc8fd51948ee83a9e8aaa8bfc2b72021-11-06T10:08:09ZWater–sediment separation efficiency prediction of gill-piece separation device1606-97491607-079810.2166/ws.2021.079https://doaj.org/article/5fb0cc8fd51948ee83a9e8aaa8bfc2b72021-09-01T00:00:00Zhttp://ws.iwaponline.com/content/21/6/2837https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798As the key piece of equipment of a micro-irrigation system, the filter can prevent clogging of the emitter and ensure normal operation of the micro-irrigation system. A gill-piece separation device is used for the removal of viscous sediment, which helps to reduce the sediment concentration and decrease the burden on the filter. In this study, using the water–sediment separation efficiency as an evaluation index, a uniform orthogonal experiment was conducted to study the flow rate, sediment concentration, and gill-piece spacing using a physical model. Based on the experimental results of the physical model, multiple linear regression and projection pursuit regression were used for analysis. The results showed that the order of the factors affecting the water–sediment separation efficiency was as follows: flow rate at muddy water inlet > gill-piece spacing > sediment concentration. The correlation coefficients of the water–sediment separation efficiency models established using multiple linear regression and projection pursuit regression were 0.93 and 0.98, respectively. Both models could predict the water–sediment separation efficiency and determine the optimal working conditions of the gill-piece separation device. HIGHLIGHTS A prediction model for water–sediment separation efficiency of the gill-piece separation device was established under the conditions of sediment concentration of 2–12 kg/m3, flow rate of 0.3–1.1 m3/h, and gill-piece spacing of 5–11 cm.; The order of influence of each factor on the water–sediment separation efficiency is as follows: flow rate at muddy water inlet > gill-piece spacing > sediment concentration.;Hongfei TaoYang ZhouMahemujiang AihemaitiQiao LiWenxin YangYouwei JiangZijing WuIWA Publishingarticlefiltergill-piece separation devicemodelmultiple linear regressionprojection pursuit regressionwater–sediment separation efficiencyWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 6, Pp 2837-2849 (2021) |
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topic |
filter gill-piece separation device model multiple linear regression projection pursuit regression water–sediment separation efficiency Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 |
spellingShingle |
filter gill-piece separation device model multiple linear regression projection pursuit regression water–sediment separation efficiency Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Hongfei Tao Yang Zhou Mahemujiang Aihemaiti Qiao Li Wenxin Yang Youwei Jiang Zijing Wu Water–sediment separation efficiency prediction of gill-piece separation device |
description |
As the key piece of equipment of a micro-irrigation system, the filter can prevent clogging of the emitter and ensure normal operation of the micro-irrigation system. A gill-piece separation device is used for the removal of viscous sediment, which helps to reduce the sediment concentration and decrease the burden on the filter. In this study, using the water–sediment separation efficiency as an evaluation index, a uniform orthogonal experiment was conducted to study the flow rate, sediment concentration, and gill-piece spacing using a physical model. Based on the experimental results of the physical model, multiple linear regression and projection pursuit regression were used for analysis. The results showed that the order of the factors affecting the water–sediment separation efficiency was as follows: flow rate at muddy water inlet > gill-piece spacing > sediment concentration. The correlation coefficients of the water–sediment separation efficiency models established using multiple linear regression and projection pursuit regression were 0.93 and 0.98, respectively. Both models could predict the water–sediment separation efficiency and determine the optimal working conditions of the gill-piece separation device. HIGHLIGHTS
A prediction model for water–sediment separation efficiency of the gill-piece separation device was established under the conditions of sediment concentration of 2–12 kg/m3, flow rate of 0.3–1.1 m3/h, and gill-piece spacing of 5–11 cm.;
The order of influence of each factor on the water–sediment separation efficiency is as follows: flow rate at muddy water inlet > gill-piece spacing > sediment concentration.; |
format |
article |
author |
Hongfei Tao Yang Zhou Mahemujiang Aihemaiti Qiao Li Wenxin Yang Youwei Jiang Zijing Wu |
author_facet |
Hongfei Tao Yang Zhou Mahemujiang Aihemaiti Qiao Li Wenxin Yang Youwei Jiang Zijing Wu |
author_sort |
Hongfei Tao |
title |
Water–sediment separation efficiency prediction of gill-piece separation device |
title_short |
Water–sediment separation efficiency prediction of gill-piece separation device |
title_full |
Water–sediment separation efficiency prediction of gill-piece separation device |
title_fullStr |
Water–sediment separation efficiency prediction of gill-piece separation device |
title_full_unstemmed |
Water–sediment separation efficiency prediction of gill-piece separation device |
title_sort |
water–sediment separation efficiency prediction of gill-piece separation device |
publisher |
IWA Publishing |
publishDate |
2021 |
url |
https://doaj.org/article/5fb0cc8fd51948ee83a9e8aaa8bfc2b7 |
work_keys_str_mv |
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1718443796555890688 |