Prediction of trapping efficiency of vortex tube ejector. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Tiwari, N. K.; Sihag, Parveen; Kumar, Sanjeev; Ranjan, Subodh
• Is Part Of: ISH journal of hydraulic engineering. Volume 26:Issue 1(2020)
• Page Start: 59
• Page End: 67
• Publication Date: 2020-01-02
• Subjects: Adaptive neuro-fuzzy inference system -- artificial neural network -- trapping efficiency -- vortex tube ejector; Hydraulic engineering -- Periodicals; Hydraulic engineering -- India -- Periodicals; Hydraulic engineering; India; Periodicals; 627
• DOI: 10.1080/09715010.2018.1441752
• Languages: English
• ISSNs: 0971-5010

Abstract

Abstract: Vortex tube ejector is employed to extract sediments from canal. It consists of a duct laid across whole bed of the canal with a slit along its top edge and compared to the other alternative sediment-extraction devices, it is very efficient and economical. In this study, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) approaches were employed to predict the trapping efficiency of vortex tube ejector. Data-set as many as 144 was obtained by conducting experiments on vortex ejector. Out of 144 data-set, 100 data selected randomly were used for training whereas remaining 44 were used for testing the models. Input data-set consists of sediment size (mm), concentration of sediment (ppm), ratio of slit thickness and diameter of tube, (t/d) and extraction ratio (%) whereas trapping efficiency (%) was considered as output. Three membership's functions, i.e. triangular, generalized bell-shaped, and Gaussian were used with ANFIS. A comparison of results suggests that Gaussian membership function-based ANFIS model performs well in comparison to other membership functions-based ANFIS models, ANN and predictive equations proposed by previous researchers. Sensitivity analyses suggest that extraction ratio is the most important parameter in estimating trapping efficiency of vortex ejector.