Development Of Multiple Regression Models Research Articles

Development of Multiple Regression Model for Assessment of Coal Calorific Value in Indian Opencast Mines

Development of Multiple Regression Model for Assessment of Coal Calorific Value in Indian Opencast Mines

Investigating the Water Jet Erosion Performance of HVOF-Sprayed WC-10Co Coatings on 35CrMo Steel Utilizing Design of Experiments

To enhance the surface of a material with the desired qualities for diverse applications in service, a variety of thermal and thermo-chemical surface treatment processes are used. Due to the high-velocity impact inherent in the process, high-velocity oxy-fuel (HVOF) spray is now frequently employed in industrial applications for its ability to generate a high-quality coating with appropriate hardness and low oxide content. In this investigation, a high-velocity oxy-fuel (HVOF) thermal spraying process was utilized to coat WC-10Co powders on a 35CrMo steel substrate. A water jet erosion test was also used to examine the substrate and coated samples’ erosion behavior. The erosion rate was systematically investigated using water jet variables such as the angle of impingement, water jet velocity, standoff distance, and erodent discharge. For the development of multiple regression models, experiments were performed utilizing the central composite rotatable design and the response surface methodology. The angle of impingement had the most impact on the rate of coating erosion, leading to the water jet velocity, standoff distance, and erodent discharge.

Parameters optimization and development of multiple regression models for wire electrical discharge machining of aluminium composites

The demand for lightweight materials are increasing nowadays due to emerging industrial applications. AA 2024 is an aluminium alloy, most commonly employed material for various engineering applications such as aircraft structural and automobile applications. The materials added to increase the desired properties are called as reinforcement materials such as Al2O3 and BN, abrasive and leads to more wear on cutting tool and meagre quality in machining surface. Wire Electrical Discharge Machining (WEDM) is an advanced method of material removal which is conceived from the concept of Spark Erosion Machining process. WEDM is mainly employed for machining of hard materials and also especially used for making complex shapes on any electrically conductive work materials. In this present study, an experimental analysis has been performed on WEDM of aluminium alloy based Metal Matrix Composites (AMMC). Peak current (A) Pulse on time (‘Pon’) (µs), Pulse off time (‘Poff’) (µs) are considered as input process parameters in this present analysis. Material Removal Rate (MRR) and Surface Roughness (SR) are considered as desired performance measures that are need to be optimized. Taguchi’s methodology has been implemented for designing and analyzing the experimental runs. An L27 Orthogonal Array (OA) was employed for conducting the experimental runs. The effect of process parameters on desired outputs such as material removal rate (MRR) and surface roughness were investigated by Taguchi’s analysis and ANOVA analysis. Multiple regression equations have been developed for correlating the relationship between the selected input process variable and output variables. Contour plot analysis also performed for disclosing the combined effect of input variables on performance measures.

Analysis of tool vibration and surface roughness with tool wear progression in hard turning: An experimental and statistical approach

The machined surface quality and dimensional accuracy obtained during hard turning is prominently gets affected due to tool wear and cutting tool vibrations. With this view, the results of tool wear progression on surface quality and acceleration amplitude is presented while machining AISI 52100 hard steel. Central Composite Rotatable Design (CCRD) is employed to develop experimental plan. The results reported that vibration signals sensed in a tangential direction (Vz) are most sensitive and found higher than the vibrations in the feed direction (Vx) and depth of cut direction (Vy). The acceleration signals in all three directions are observed to increase with the advancement of tool wear and good surface finish is observed as tool wear progresses up-to 0.136mm. The vibration amplitude is discovered high in the range 3 kHz – 10 kHz within selected cutting parameter range (cutting speed 60-180mm/min, feed 0.1-0.5mm/rev, depth of cut 0.1-0.5mm). The investigation is extended for the development of multiple regression models with regression coefficients value 0.9. These models found statically significant and give dependable estimates between a tool vibrations and cutting parameters.

Development of statistical models for trihalomethane (THM) removal in drinking water sources using carbon nanotubes (CNTs)

This research developed models using the multiple linear regression technique for prediction of trihalomethane (THM) removal from chlorinated drinking water sources through a combination of a coagulation process with carbon nanotubes (CNTs). Terkos Lake water (TLW), Buyukçekmece Lake water (BLW) and Ulutan Lake water (ULW) samples were coagulated by a conventional coagulant (alum) and increasing doses of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) with the addition of alum. Also, chlorination experiments were conducted with water reservoirs from TLW, BLW and ULW, with different water quality regarding bromide concentration and organic matter content. The factors studied affecting THM removal were contact time, chlorine dose, coagulation process, total organic carbon (TOC), and specific ultraviolet absorbance (SUVA). Statistical analysis of the results focused on the development of multiple regression models, as Models 1 and 2, for predicting total trihalomethane (TTHM) based on the use of contact time, SWCNTs and MWCNTs doses, chlorine dose and TOC. When the two models were compared, Model 1 proved best suited to describe THM removal for the three water sources. The developed models provided satisfactory estimations of THM removal; the model regression coefficients for Models 1 and 2 were 0.88 and 0.77, respectively. Furthermore, the root-mean-square error (RMSE) values of 0.083 and 0.126 confirm the reliability of the two models. The results show that THM removal can be simply predicted by using the multiple linear regression technique in chlorinated drinking water sources.

Mathematical Modeling and Analysis of Spark Erosion Machining Parameters of Hastelloy C-276 Using Multiple Regression Analysis

Electrical discharge machining has the capability of machining complicated shapes in electrically conductive materials independent of hardness of the work materials. This present article details the development of multiple regression models for envisaging the material removal rate and roughness of machined surface in electrical discharge machining of Hastelloy C276. The experimental runs are devised as per Taguchi’s principles and empirical relations are established using multiple regression analysis. Taguchi’s methodology can be applied as a single aspects optimization technique for attaining the best set of possible process parameter for material removal rate and roughness of the machined surface. A statistical tool called analysis of variance is employed for determining the significance of input process variables that influences the desired performance measures such as material removal rate and roughness of the electrically machined surface. The developed multiple regression models are flexible, competent and precise in prediction of desired performance measures. The developed regression models were validated and the predicted results from the evolved regression models are closer with the experimental outcomes.

Development of Multiple Regression Models to Predict Sources of Fecal Pollution

This study assessed the usefulness of multivariate statistical tools to characterize watershed dynamics and prioritize streams for remediation. Three multiple regression models were developed using water quality data collected from Sinking Creek in the Watauga River watershed in Northeast Tennessee. Model 1 included all water quality parameters, model 2 included parameters identified by stepwise regression, and model 3 was developed using canonical discriminant analysis. Models were evaluated in seven creeks to determine if they correctly classified land use and level of fecal pollution. At the watershed level, the models were statistically significant (p < 0.001) but with low r2 values (Model 1 r2 = 0.02, Model 2 r2 = 0.01, Model 3 r2 = 0.35). Model 3 correctly classified land use in five of seven creeks. These results suggest this approach can be used to set priorities and identify pollution sources, but may be limited when applied across entire watersheds.

The Impact of Targeted Therapies for Pulmonary Hypertension on Pediatric Intraoperative Morbidity or Mortality

Pulmonary hypertension (PHT) is a significant risk factor for major adverse events during anesthesia, with a reported incidence of 5% to 7%, secondary to acute pulmonary hypertensive crises or right ventricular ischemia. Newer therapies for treating PHT have reduced mortality. In this single-center study, we investigated the frequency of major and minor events during anesthesia under the current strategies to manage PHT. We reviewed the records of children with PHT who underwent noncardiopulmonary bypass procedures from 2008 to 2012. Clinically important symptoms, physical signs, and results of investigations present before anesthesia were recorded. The incidence and type of intraoperative complications and death (up to 7 days) were collected. Data were collected for 122 patients undergoing 284 procedures. Minor (3.9%) and major (3.2%) complication rates were unchanged from previous publications. The etiology of PHT was not significant for complications (P = 0.14). Disease-modifying agents were not associated with reduced complications: 4.1% in treated versus 8.6% untreated (all P > 0.14). Patients receiving home oxygen had more complications (P = 0.02). Multiple logistic regression identified age and degree of PHT as significant predictors of complications (all P ≤ 0.03). The risk for adverse events during anesthesia in patients with PHT remains high, despite newer disease-modifying treatments. Risk factors for complications include age and severity of PHT.

Prediction of water temperature metrics using spatial modelling in the Eastern and Western Cape, South Africa

Key aspects of a river’s temperature regime are described by magnitudes, timing and durations of thermal events, and frequencies of extreme exceedance events. To understand alterations to thermal regimes, it is necessary to describe thermal time series based on these statistics. Classification of sites based on their thermal metrics, and understanding of spatial patterns of these thermal statistics, provides a powerful approach for comparing study sites against reference sites. Water temperature regime dynamics should be viewed regionally, where regional divisions have an inherent underpinning by an understanding of natural thermal variability. The aim of this research was to link key water temperature metrics to readily-mapped environmental surrogates, and to produce spatial images of temperature metrics: 37 temperature metrics were derived for 12 months of sub-daily water temperatures at 90 sites in the Eastern Cape and Western Cape provinces, South Africa. These metrics were correlated with 16 environmental variables. Correlations enabled development of multiple regression models which facilitated mapping of temperature metrics over the study area. This approach has the potential to be applied at a national scale as more thermal time series are collected nationally. It is argued that the appropriateness of management decisions in rivers can be improved by including guidelines for thermal metrics at a regional scale. Such maps could facilitate incorporation of a temperature component into management guidelines for water resources.

Multiple linear regression modeling of disinfection by-products formation in Istanbul drinking water reservoirs

Oxidation of raw water with chlorine results in formation of trihalomethanes (THM) and haloacetic acids (HAA). Factors affecting their concentrations have been found to be organic matter type and concentration, pH, temperature, chlorine dose, contact time and bromide concentration, but the mechanisms of their formation are still under investigation. Within this scope, chlorination experiments have been conducted with water reservoirs from Terkos, Buyukcekmece and Omerli lakes, Istanbul, with different water quality regarding bromide concentration and organic matter content. The factors studied were pH, contact time, chlorine dose, and specific ultraviolet absorbance (SUVA). The determination of disinfection by-products (DBP) was carried out by gas chromatography techniques. Statistical analysis of the results was focused on the development of multiple regression models for predicting the concentrations of total THM and total HAA based on the use of pH, contact time, chlorine dose, and SUVA. The developed models provided satisfactory estimations of the concentrations of the DBP and the model regression coefficients of THM and HAA are 0.88 and 0.61, respectively. Further, the Durbin-Watson values confirm the reliability of the two models. The results indicate that under these experimental conditions which indicate the variations of pH, chlorine dosages, contact time, and SUVA values, the formation of THM and HAA in water can be described by the multiple linear regression technique.

MRI T2 lesion burden in multiple sclerosis

Previous studies have shown only modest correlation between multiple sclerosis (MS) lesions on MRI and clinical disability. To investigate the relationship between proton density/T2-weighted (T2) burden of disease (BOD) quantitatively measured on MRI scans and clinical determinants including disability. Using the Sylvia Lawry Centre for Multiple Sclerosis Research (SLCMSR) database, the authors studied baseline T2 BOD data from a pooled subsample of 1,312 placebo MS patients from 11 randomized controlled trials. Univariate comparisons guided development of multiple regression models incorporating the most important clinical predictors. Significant, although weak to moderate, correlations were found between T2 BOD and age at disease onset, disease duration, disease course, disability (as measured by the Expanded Disability Status Scale [EDSS]), relapse rate, certain presenting symptoms, and gadolinium enhancement. An unexpected but key finding that persisted in the multiple regression analyses was a plateauing relationship between T2 BOD and disability for EDSS values above 4.5. This study confirmed the limited correlation between clinical manifestations and T2 burden of disease (BOD) but revealed an important plateauing relationship between T2 BOD and disability.

Stand off's indirect estimation in GMAW

This work was aimed at developing a methodology for the estimation of the contact tip-to-workpiece distance (stand off) in the gas metal arc welding (GMAW) process. The proposed methodology was based on the measurement of the electrical resistance between the contact tip and the workpiece during the short circuits in the dip transfer mode of metal transfer (dip resistance). The time periods in which the short-circuits were occurring, were identified based on the positive cycles of the time derivative of the welding current signal, together with the analysis of the welding voltage. The experimental part of this work was carried out in three basic parts: (a) determination of the working envelop for the welding parameters; (b) acquisition of welding data from controlled welding trials and the development of multiple regression models; and (c) validation of the chosen model. The weld beads were laid using an AWS A5.18/79 welding wire of 0.8 mm diameter, C25 shielding gas (Ar + 25% CO 2) on 1/4 in. (6.25 mm) mild steel plate workpieces (ABNT 1020). A multiple linear regression model was developed having as independent variables the wire feed speed, the average welding current (measured on-line), the average dip-resistance (measured on-line) and some second order interactions between these variables, and the predicted stand-off as the dependant variable. The standoff obtained from the validation weld runs were in good agreement with the predicted values. Errors around 4% were observed.

Modeling the formation of chlorination by-products in river waters with different quality

Water chlorination results in formation of a variety of organic compounds, known as chlorination by-products (CBPs), mainly trihalomethanes (THMs) and haloacetic acids (HAAs). Factors affecting their concentrations have been found to be organic matter content of water, pH, temperature, chlorine dose, contact time and bromide concentration, but the mechanisms of their formation are still under investigation. Within this scope, chlorination experiments have been conducted with river waters from Lesvos island, Greece, with different water quality regarding bromide concentration and organic matter content. The factors studied were pH, time and chlorine dose. The determination of CBPs was carried out by gas chromatography techniques. Statistical analysis of the results was focused on the development of multiple regression models for predicting the concentrations of total trihalomethanes and total HAAs based on the use of pH, reaction time and chlorine dose. The developed models, although providing satisfactory estimations of the concentrations of the CBPs, showed lower correlation coefficients than the multiple regression models developed for THMs only during previous study. It seems that the different water quality characteristics of the two river waters in the present study is responsible for this phenomenon. The results indicate that under these conditions the formation of THMs and HAAs in water has a more stochastic character, which is difficult to be described by the conventional regression techniques.

일별 암모니아성 질소(NH3-N)농도 예측을 위한 다중회귀모형 개발

일별 암모니아성 질소(NH₃-N)농도 예측을 위한 다중회귀모형 개발

Characterization parameters of brown coal hydrogenation reactivity from 1H n.m.r. measurements

The kinetic behaviour of 22 brown coals was investigated in direct hydroliquefaction. Yield data of the pseudocomponent formation were obtained from 0.11 microautoclave experiments carried out at 450 °C and reaction times from 0 to 60 min in the presence of tetralin as vehicle and iron sulphate as catalyst under a hydrogen blanket. The computer simulation of the hydrogenation kinetics was used to calculate rate constants, which were used as time independent characteristics of hydroliquefaction reactivity. Data on hydrogen distribution of the oils formed during hydrogenation were obtained by 1H n.m.r. spectroscopy of oils from ‘dry’ hydrogenation. Structural parameters calculated from n.m.r. measurements were used as independent variables for the development of statistical models. Using a stepwise variable selection procedure for the development of multiple regression models of the rate constants, the influences of sulphur and the aromaticity of coal oils were found. These and other minor influencing factors of reactivity such as ash content were discussed in terms of their effect on mechanism.

Effects of Livestock Grazing on Infiltration and Erosion Rates Measured on Chained and Unchained Pinyon-Juniper Sites in Southeastern Utah

Effects of Livestock Grazing on Infiltration and Erosion Rates Measured on Chained and Unchained Pinyon-Juniper Sites in Southeastern Utah