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Application of multiple linear regression (MLR) analysis for concentration of chromite tailings by the flotation

Deniz Vedat

Physicochemical Problems of Mineral Processing

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2020

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Vol. 56, iss. 4

579--589

EN

In this study, the concentration of chromite minerals by amine flotation of a chromite tailing (slime) with content 23.84% Cr2O3 from Yeşilova-Burdur (Turkey) was investigated. In experimental studies, firstly, some operating parameters of the rougher flotation observed for a low-grade chromite tailing were investigated. Secondly, multiple linear regression (MLR) analyses were performed to determine the effects of some operating parameters on the performance of the rougher amine flotation. From the experimental results, multiple linear regression equations were developed to predict the recovery and grade of the chromite concentrate, and the regression coefficients between experimental and predicted values were found to be quite good (R2 values of 0.772 and 0.917, respectively). Additionally, it was found that the conditioning time and low pH value using H2SO4 showed an important effect on the recovery and the grade of the chromite concentrate.

2

Statistical yielding models of some irrigated vegetable crops in dependence on water use and heat supply

Vozhehova Raisa, Kokovikhin Sergii, Lykhovyd Pavlo V., Balashova Halyna, Lavrynenko Yuriy, Biliaieva Iryna, Markovska Olena

Journal of Water and Land Development

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2020

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no. 45

190--197

EN

Statistical analysis is helpful for better understanding of the processes which take place in agricultural ecosystems. Particular attention should be paid to the processes of crops’ productivity formation under the influence of natural and anthropogenic factors. The goal of our study was to provide new theoretical knowledge about the dependence of vegetable crops’ productivity on water supply and heat income. The study was conducted in the irrigated conditions of the semi-arid cold Steppe zone on the fields of the Institute of Irrigated Agriculture of NAAS, Kherson, Ukraine. We studied the historical data of productivity of three most common in the region vegetable crops: potato, tomato, onion. The crops were cultivated by using the generally accepted in the region agrotechnology. Historical yielding and meteorological data of the period 1990–2016 were used to develop the models of the vegetable crops’ productivity. We used two approaches: development of pair linear models in three categories (“yield – water use”, “yield – sum of the effective air temperatures above 10°C”); development of complex linear regression models taking into account such factors as total water use, and temperature regime during the crops’ vegetation. Pair linear models of the crops’ productivity showed that the highest effect on the yields of potato and onion has the water use index (R2 of 0.9350 and 0.9689, respectively), and on the yield of tomato – temperature regime (R2 of 0.9573). The results of pair analysis were proved by the multiple regression analysis that revealed the same tendencies in the crop yield formation depending on the studied factors.

3

Development of empirical models for prediction of weld bead geometry in robotic - GMAW

Thamilarasi P., Ragunathan S., Mohankumar E.

Journal of Achievements in Materials and Manufacturing Engineering

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2014

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Vol. 67, nr 2

72--85

EN

Purpose: The objective of this paper is to determine the input-output relationship of robotic gas metal arc welding process using linear as well as second order linear regression analysis. Design/methodology/approach: Taguchi’s L27, 3 level 4 parameter orthogonal array design of experiments and multiple regression techniques has been utilized for the development of empirical model. Arc current, stick-out, arc voltage and travel speed is taken as input parameters and bead geometry has been taken as output responses. The effects and interaction terms on different responses of these selected welding parameters have been analyzed using ANOVA. Findings: Both techniques results were compared and concluding remarks have been made. The developed empirical model has been found good agreement with the experiment results and predicted error for second order polynomial regression equations lies between 0.58% to 14.86% for bead height, 0.93% to 9.44% for bead width and 0.34% to 2.56% for bead penetration using with actual experimental results. Research limitations/implications: It was noticed that interaction effects have considerable influence on the formation of weld bead geometry, so it cannot be ignored. Originality/value: In this present work, an effort has been made to carry out both first as well as second order linear regression analyses on robotic GMAW by L27, Taguchi’s design of experiments.