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引用本文:陶洪飞,李 琦,周 洋,等.基于PPR和NSGA-Ⅱ的泵前微压过滤器 水力与过滤性能研究[J].灌溉排水学报,2024,43(5):30-37.
TAO Hongfei,LI Qi,ZHOU Yang,et al.基于PPR和NSGA-Ⅱ的泵前微压过滤器 水力与过滤性能研究[J].灌溉排水学报,2024,43(5):30-37.
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基于PPR和NSGA-Ⅱ的泵前微压过滤器 水力与过滤性能研究
陶洪飞,李 琦,周 洋,马合木江·艾合买提,李 巧,姜有为
1.新疆农业大学 水利与土木工程学院,乌鲁木齐 830052;2.新疆水利工程安全与水灾害 防治重点实验室,乌鲁木齐 830052;3.新疆水利水电科学研究院,乌鲁木齐 830049
摘要:
【目的】探究泵前微压过滤器的性能。【方法】开展5组流量(2~8 m3/h)、5组含沙量(0.5~2.0 g/L)、3组滤网过滤面积(1 105、1 582、2 060 cm2)和4组分水器型式(不加、1型、2型、3型)的物理模型试验,采用投影寻踪回归分析法(PPR)、多目标遗传算法(NSGA-Ⅱ),建立水头损失、截沙质量和总过滤效率的预测模型,探究各指标的影响因素排序,确定泵前微压过滤器的最佳运行工况。【结果】影响泵前微压过滤器水头损失的因素排序为进水流量?含沙量?滤网过滤面积;影响截沙质量的因素排序为含沙量?滤网过滤面积?进水流量;影响总过滤效率的因素排序为滤网过滤面积?含沙量?进水流量;以相对误差≤10%作为判定标准,建立的截沙质量和总过滤效率PPR预测模型合格率为100%,模型精度较高,但水头损失PPR预测模型合格率仅为70%,模型不可靠。本试验范围下泵前微压过滤器的最佳运行工况为:含沙量2 g/L、进水流量7 m3/h、滤网过滤面积2 060 cm2。【结论】PPR预测模型对截沙质量和总过滤效率的预测精度较高,对水头损失的预测误差较大,在后期可用量纲分析与多元回归相结合预测水头损失、截沙质量和总过滤效率。
关键词:  过滤器;水头损失;模型;水力性能;过滤性能
DOI:10.13522/j.cnki.ggps.2023480
分类号:
基金项目:
Efficiency and hydraulic performance of the micro-pressure filter in front of the pump studied using PPR and NSGA-II
TAO Hongfei, LI Qi, ZHOU Yang, Mahemujiang·Aihemaiti, LI Qiao, JIANG Youwei
1. College of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China; 2. Xinjiang Key Laboratory of Water Conservancy Engineering Safety and Water Disaster Control, Urumqi 830052, China; 3. Xinjiang Institute of Water Resources and Hydropower Research, Urumqi 830049, China
Abstract:
【Objective】Pump often has a filter installed in the front of it to filter sediments and debris. This paper studied its efficiency and performance.【Method】The study was based on physical model, with flow rate being 2-8 m3/h, sediment content being 0.5-2.0 g/L. The area of the filter varied from 1 105 to 2 060 cm2, and water separator type was Type 1, Type 2, Type 3. Without a separator was the control. A prediction model was used to evaluate sediment interception and total filtration efficiency. Based on these measurements, we determined the optimal operating conditions for the pump.【Result】The factors that influenced water head loss across the filter were ranked in the order of inlet flow > sediment content > filter area; the factors that affected the quality of sediment interception were ranked in the order of sediment content > filter area > inlet flow; the factors impacting the total filtration efficiency were ranked in the order of filter area > sediment content > inlet flow. The accuracy of the PPR model for predicting sediment interception quality and total filtration efficiency was 100%, with a relative error less than 10%, while its accuracy for predicting water head loss across the filter was 70%, which needs further improvement. The optimal operating conditions for the filter were sand content 2 g/L, inlet water flow rate 7 m3/h, and filter area 2 060 cm2.【Conclusion】The PPR prediction model was accurate for sediment interception and total filtration efficiency, but it resulted in errors for calculating water head loss across the filter. Dimensional analysis and multiple regression can be used as an alternative to predict the water head loss.
Key words:  filter; head loss; model; hydraulic performance; filtration performance