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Estimation of the depth of penetration in a plunging hollow jet using artificial intelligence techniques

Bodana D., Tiwari N. M., Ranjan S., Ghanekar U.

Archives of Materials Science and Engineering

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2020

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Vol. 103, nr 2

49--61

EN

Purpose: Experimental investigations assessment and comparison of different classical models and machine learning models employed with Gaussian process regression (GPR) and artificial neural network (ANN) in the estimation of the depth of penetration (Hp) of plunging hollow jets. Design/methodology/approach: In this analysis, a set of data of 72 observations is derived from laboratory tests of plunging hollow jets which impinges into the water pool of tank. The jets parameters like jet length, discharge per unit water depth and volumetric oxygen transfer coefficient (Kla20) are varied corresponding to the depth of penetration (Hp) are estimated. The digital image processing techniques is used to estimate the depth of penetration. The Multiple nonlinear regression is used to establish an empirical relation representing the depth of penetration in terms of jet parameters of the plunging hollow jets which is further compared with the classical equations used in the previous research. The efficiency of MNLR and classical models is compared with the machine learning models (ANN and GPR). Models generated from the training data set (48 observations) are validated on the testing data set (24 observations) for the efficiency comparison. Sensitivity assessment is carried out to evaluate the impact of jet variables on the depth of penetration of the plunging hollow jet. Findings: The experimental performance of machine learning models is far better than classical models however, MNLR for predicting the depth of penetration of the hollow jets. Jet length is the most influential jet variable which affects the Hp. Research limitations/implications: The outcomes of the models efficiency are based on actual laboratory conditions and the evaluation capability of the regression models may vary beyond the availability of the existing data range. Practical implications: The depth of penetration of plunging hollow jets can be used in the industries as well as in environmental situations like pouring and filling containers with liquids (e.g. molten glass, molten plastics, molten metals, paints etc.), chemical and floatation process, wastewater treatment processes and gas absorption in gas liquid reactors. Originality/value: The comprehensive analyses of the depth of penetration through the plunging hollow jet using machine learning and classical models is carried out in this study. In past research, researchers were used the predictive modelling techniques to simulate the depth of penetration for the plunging solid jets only whereas this research simulate the depth of penetration for the plunging hollow jets with different jet variables.

2

Kernel function based regression approaches for estimating the oxygen transfer performance of plunging hollow jet aerator

Kumar M., Tiwari N. K., Ranjan S.

Journal of Achievements in Materials and Manufacturing Engineering

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2019

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Vol. 95, nr 2

74--84

EN

Purpose: To evaluate the capability of various kernels employed with support vector regression (SVR) and Gaussian process regression (GPR) techniques in estimating the volumetric oxygen transfer coefficient of plunging hollow jets. Design/methodology/approach: In this study, a data set of 81 observations is acquired from laboratory experiments of hollow jets plunging on the surface of water in the tank. The jet variables: jet velocity, jet thickness, jet length, and water depth are varied accordingly and the values of volumetric oxygen transfer coefficient is computed. An empirical relationship expressing the oxygenation performance of plunging hollow jet aerator in terms of jet variables is formulated using multiple nonlinear regression. The performance of this nonlinear relationship is compared with various kernel function based SVR and GPR models. Models developed with the training data set (51 observations) are checked on testing data set (24 observations) for performance comparison. Sensitivity analysis is carried out to examine the influence of jet variables in effecting the oxygen transfer capabilities of plunging hollow jet aerator. Findings: The overall comparison of kernels yielded good estimation performance of Radial Basis Function kernel (RBF) and Pearson VII Function kernel (PUK) using the SVR technique which is followed by nonlinear regression, and other kernel function based regression models. Research limitations/implications: The results of the study pertaining to the performance of kernels are based on the current experimental conditions and the estimation potential of the regression models may fluctuate beyond the selection of current data range due to datadependant learning of the soft computing models. Practical implications: Volumetric oxygen transfer coefficient of plunging hollow jets can be predicted precisely using SVR model by employing RBF as kernel function as compared to empirical correlation and other kernel function based regression models. Originality/value: The comparative analysis of kernel functions is conducted in this study. In previous studies, the predictive modelling approaches are implemented in simulating the aeration properties of cylindrical solid jets only, while this paper simulates the volumetric oxygen transfer coefficient of diverging hollow jets with the jet variables by utilizing polynomial, normalized polynomial, PUK, and RBF kernels in SVR and GPR.

3

Spatio-temporal variation of rainfall over Bihar State, India

Warwade P., Tiwari S., Ranjan S., Chandniha S. K., Adamowski J.

Journal of Water and Land Development

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2018

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no. 36

183--197

EN

This study detected, for the first time, the long term annual and seasonal rainfall trends over Bihar state, India, between 1901 and 2002. The shift change point was identified with the cumulative deviation test (cumulative sum – CUSUM), and linear regression. After the shift change point was detected, the time series was subdivided into two groups: before and after the change point. Arc-Map 10.3 was used to evaluate the spatial distribution of the trends. It was found that annual and monsoon rainfall trends decreased significantly; no significant trends were observed in pre-monsoon, monsoon, post-monsoon and winter rainfall. The average decline in rainfall rate was –2.17 mm·year–1 and –2.13 mm·year–1 for the annual and monsoon periods. The probable change point was 1956. The number of negative extreme events were higher in the later period (1957–2002) than the earlier period (1901–1956).

PL

W badaniach prezentowanych w niniejszej pracy wykryto po raz pierwszy długookresowe trendy rocznych i sezonowych wartości opadów w indyjskim stanie Bihar w latach 1901–2002. Stosując kumulatywny test odchyleń (CUSUM – ang. cumulative deviation test) i regresję liniową zidentyfikowano punkt zwrotny. Następnie serie czasowe zostały podzielone na dwie grupy: przed i po punkcie zwrotnym. Do oceny przestrzennego rozkładu trendów zastosowano program Arc-Map 10.3. Stwierdzono, że trendy rocznych i monsunowych opadów znacząco malały. Nie zaobserwowano istotnych trendów w odniesieniu do opadów przed monsunem, po monsunie i w okresie zimowym. Średnie zmniejszenie ilości opadów wynosiło 2,17 mm·rok–1 i 2,13 mm ·rok–1 odpowiednio dla opadów rocznych i monsunowych. Prawdopodobnym punktem zwrotnym był rok 1956. Liczba skrajnych negatywnych zjawisk była większa w okresie 1957–2002 niż w okresie 1901–1956.