Abstract: Presently various computational techniques are used
in modeling and analyzing environmental engineering data. In the
present study, an intra-comparison of polynomial and radial basis
kernel functions based on Support Vector Regression and, in turn, an
inter-comparison with Multi Linear Regression has been attempted in
modeling mass transfer capacity of vertical (θ = 90O) and inclined (θ
multiple plunging jets (varying from 1 to 16 numbers). The data set
used in this study consists of four input parameters with a total of
eighty eight cases, forty four each for vertical and inclined multiple
plunging jets. For testing, tenfold cross validation was used.
Correlation coefficient values of 0.971 and 0.981 along with
corresponding root mean square error values of 0.0025 and 0.0020
were achieved by using polynomial and radial basis kernel functions
based Support Vector Regression respectively. An intra-comparison
suggests improved performance by radial basis function in
comparison to polynomial kernel based Support Vector Regression.
Further, an inter-comparison with Multi Linear Regression
(correlation coefficient = 0.973 and root mean square error = 0.0024)
reveals that radial basis kernel functions based Support Vector
Regression performs better in modeling and estimating mass transfer
by multiple plunging jets.
Abstract: The paper aims to compare the performance of vertical and inclined multiple plunging jets and to model and predict their mass transfer capacity by multi-linear regression based approach. The multiple vertical plunging jets have jet impact angle of θ = 90O; whereas, multiple inclined plunging jets have jet impact angle of θ = 60O. The results of the study suggests that mass transfer is higher for multiple jets, and inclined multiple plunging jets have up to 1.6 times higher mass transfer than vertical multiple plunging jets under similar conditions. The derived relationship, based on multi-linear regression approach, has successfully predicted the volumetric mass transfer coefficient (KLa) from operational parameters of multiple plunging jets with a correlation coefficient of 0.973, root mean square error of 0.002 and coefficient of determination of 0.946. The results suggests that predicted overall mass transfer coefficient is in good agreement with actual experimental values; thereby, suggesting the utility of derived relationship based on multi-linear regression based approach and can be successfully employed in modeling mass transfer by multiple plunging jets.
Abstract: There has been a growing interest in the oxygenation
by plunging water jets in the last few years due to their inherent
advantages, like energy-efficient, low operation cost, etc. Though a
lot of work has been reported on the oxygen-transfer by single
plunging water jets but very few studies have been carried out using
multiple plunging jets. In this paper, volumetric oxygen-transfer
coefficient and oxygen-transfer efficiency has been studied
experimentally for multiple inclined plunging jets (having jet plunge
angle of 60 0 ) in a pool of water for different configurations, in
terms of varying number of jets and jet diameters. This research
suggests that the volumetric oxygen-transfer coefficient and oxygentransfer
efficiency of the multiple inclined plunging jets for air-water
system are significantly higher than those of a single vertical as well
as inclined plunging jet for same flow area and other similar
conditions. The study also reveals that the oxygen-transfer increase
with increase in number of multiple jets under similar conditions,
which will be most advantageous and energy-efficient in practical
situations when large volumes of wastewaters are to be treated. A
relationship between volumetric oxygen-transfer coefficient and jet
parameters is also proposed. The suggested relationship predicts the
volumetric oxygen-transfer coefficient for multiple inclined plunging
jet(s) within a scatter of ±15 percent. The relationship will be quite
useful in scale-up and in deciding optimum configuration of multiple
inclined plunging jet aeration system.
Abstract: The paper investigates the potential of support vector
machines and Gaussian process based regression approaches to
model the oxygen–transfer capacity from experimental data of
multiple plunging jets oxygenation systems. The results suggest the
utility of both the modeling techniques in the prediction of the
overall volumetric oxygen transfer coefficient (KLa) from operational
parameters of multiple plunging jets oxygenation system. The
correlation coefficient root mean square error and coefficient of
determination values of 0.971, 0.002 and 0.945 respectively were
achieved by support vector machine in comparison to values of
0.960, 0.002 and 0.920 respectively achieved by Gaussian process
regression. Further, the performances of both these regression
approaches in predicting the overall volumetric oxygen transfer
coefficient was compared with the empirical relationship for multiple
plunging jets. A comparison of results suggests that support vector
machines approach works well in comparison to both empirical
relationship and Gaussian process approaches, and could successfully
be employed in modeling oxygen-transfer.