Central Composite Rotatable Design Method Research Articles

Improving Crude Oil Flow Using Graphene Flakes under an Applied Electric Field

Graphene flakes (GF) have been prepared and assessed as a material for improving flow in oil pipelines under the effect of an electric field. In particular, different amounts of GFs have been considered in order to determine the optimal flow conditions. The GFs were prepared from graphite foam, derived from the dehydration of sugar with a particle size of 500–600 μm, which was dispersed in ethanol and exfoliated in a ball mill under a shear force. After 15 h of exfoliation, sonication, and subsequent high-speed centrifugation at 3000 rpm, irregular-shaped GFs of 50–140 nm were produced and characterized using scanning electron microscopy, X-ray diffractometry, atomic force microscopy, and Raman spectroscopy. The prepared graphene sheets have been found to display excellent morphology and good graphitic structure. Experiments on flow improvement were conducted using the central composite rotatable design method for three parameters: stimulation time (15, 30, 45, and 60 s), applied voltage (150, 170, 200, and 220 V), and concentration of the GFs (0, 100, 200, and 400 mg/L). The optimal conditions for improved crude oil flow were then determined using the STATISTICA and WinQSB software packages. The results have confirmed the effectiveness of the use of the prepared GFs as a flow improver for crude oil, where the flow improvement is essentially a result of a reduction in viscosity and suppression of friction in the crude oil system.

H2O2/UV catalytic degradation of furacilin by Fe-Ni oxyhydroxides synthesized via coprecipitation

The article considers the structural and photocatalytic properties of nickel–iron double oxyhydroxide synthesized by co-precipitation. X-ray phase analysis of the powder and scanning electron microscopy showed that the NiOOH-FeOOH nanostructures have a crystalline structure and a scaly-layered structure.UV spectroscopy confirmed the photocatalytic activity of nanostructures in the degradation of furacilin under the action of UV light. The process of photocatalytic decomposition of furacilin in the presence of a synthesized photocatalyst is considered. The statistical model was developed using central composite rotatable design method. The calculated values of the degree of degradation of furacilin are confirmed by experimental results. The optimal process parameters were also determined, namely the concentration of the photocatalyst, hydrogen peroxide and processing time.

Effect of Laser Welding Parameters on Weld Bowing Distortion of Thin Plates

AbstractWeld bowing distortions are detrimental to the assembly process, where laser process parameters such as laser power, welding speed, defocusing distance and gas flow rate play a significant role in determining the weld bowing distortion. Herein, weld bowing distortions in 1-mm-thick AA5052 aluminum were measured by the digital image correlation technique following laser welding. Two mathematical response models were developed to predict the laser weld bowing distortion according to the central composite rotatable design method. The optimized process parameters for minimum bowing distortion were obtained, and the influence of the laser process parameters on the weld bowing distortions was found.

Determination of Optimal Process Parameters of Friction Stir Welding to Join Dissimilar Aluminum Alloys Using Artificial Bee Colony Algorithm

This paper presents the efforts of joining dissimilar aluminum alloys (AA6351-T6 and AA6061-T6) by friction stir welding (FSW) process. FSW experiments are conducted according to the three factors five level central composite rotatable design method, and the response surface methodology was used to establish the empirical relationship between FSW process parameters such as tool rotational speed (N), tool traverse speed (S) and axial force (F), and the response variables such as ultimate tensile strength, yield strength, and percentage of elongation. The developed empirical models’ adequacies are estimated using the analysis of variance technique. This paper also presents the application of the artificial bee colony algorithm to estimate the optimal process parameters to achieve good mechanical properties of FS weld joints. Results suggest that the estimations of the algorithm are in good agreement with the experimental findings.

Mathematical Model Development to Predict Wear Rate of Al6061/TiC<sub>p</sub> Composites under Lubricant

Aluminium matrix composites are mostly used in aerospace and automobile industries due to their improved properties such as hardness, strength, wear resistance combined with considerable weight savings over unreinforced alloys. This paper reports the lubricated wear behavior of Al6061composites reinforced with titanium carbide particles fabricated by stir casting route. The experiments were conducted using central composite rotatable design method. The wear test was performed on the pin on disc machine where aluminium composite pins were rubbed against EN31 steel discs in the presence lubricant. A mathematical model has been developed by response surface methodology. The validity of the developed model was checked by Analysis of variance method. The experimental results revealed that the addition of lubricant at the contact surface reduced wear rate of the composite.

Experimental Study on Laser Transmission Joining between PA66GF and AISI304

Joining between polyamides (PA) and metals has potential applications in the industry. Based on PA66GF and AISI304, the experimental study of laser transmission joining of the dissimilar materials is conducted in this article. Firstly, comprehensive experiments of laser transmission joining between PA66GF and AISI304 are carried out by Nd: YAG pulsed laser source according to the central composite rotatable design method (CCRD). Secondly, the effects of the process parameters like voltage, joining speed and stand-off distance on the joint quality are analyzed by response surface methodology (RSM). Finally, mathematical models between the process parameters and the lap-shear strength and the joint width are established and based on which, the optimization of the joining process is done and the optimized results are predicted. The results show that the process parameters of laser transmission joining play a significant role in determining the joint quality; the predicted data of the models are in good agreement with the experimental results and can be of great help for the optimization of the joining process. As a result, this study could provide an effective instruction about how to choose the reasonable process parameters for enhancing the joining efficiency.

Response surface methodology for optimization of fermentation process parameters for improving apple wine quality

Apple wine is a fermented alcoholic beverage made from apple juice. To ensure optimal apple wine processing performance and premium final product quality, an optimized selection of various parameters for apple wine fermentation process is imperative. This study is aimed at optimizing the fermentation process based on three primary fermentation parameters, including fermentation temperature, initial pH value and inoculum volume, to improve apple wine quality using response surface methodology (RSM) coupled with central composite rotatable design method (CCRD). Based on the experimental data analysis a second order response surface model was developed to describe the relationship between fermentation temperature, initial pH value, and inoculum volume with respect to the sensory score of apple wine. The results of analysis of variance (ANOVA) showed that the model established by response surface quadratic regression was adequate for predicting apple wine quality. It was observed that the quality of apple wine was significantly influenced by fermentation temperature (p ≤ 0.01), followed by initial pH value (p ≤ 0.01) and inoculum volume (p ≤ 0.01). The temperature of 20 °C, initial pH value of 3.6 and inoculum volume of 9 % were found to be the optimal fermentation condition for ensuring premium quality of apple wine.

Application of design of experiments and artificial neural network in optimization of ultrasonic energy-assisted transesterification of Sardinella longiceps fish oil to biodiesel

The competent and efficient utilization of feedstocks is highly essential in the transesterification of biodiesel from sardine fish oil. The identification of optimal reaction parameters is of high importance to maximize the yield of biodiesel produced from sardine fish oil at low cost. Application of ultrasonic energy-assisted biodiesel production from sardine fish oil catalyzed by KOH catalyst has been studied under different conditions. Response surface methodology (RSM) based on central composite rotatable design (CCRD) was employed to optimize the three important process parameters: methanol/oil molar ratio (X1), KOH catalyst concentration (X2), and reaction time (X3) for transesterification of sardine fish oil using ultrasonic energy. Artificial neural network (ANN) models with two feed-forward back-propagation neural network architecture, multilayer perceptron networks and radial basis function networks have been developed to obtain a good correlation between the input variables responsible for the input reaction parameters and the output parameter yield of fatty acid methyl ester (FAME) from sardine fish oil to biodiesel. The developed ANN models were trained and tested with the experimental data obtained from the RSM–CCRD method. The developed ANN models’ performances were compared with experimental data and were statistically compared by the coefficient of determination (R2), root-mean-square error, and mean absolute error. From the statistical analysis, it was found that the estimated yield of FAME from both RSM and ANN models was able to predict the FAME yield, and the results showed that the ANN model is much more accurate in the prediction of FAME yield as compared to the RSM model.

Modeling and Optimizing of Mechanically Agitated Vessels by Central Composite Rotatable Design Method

A central composite rotatable design (CCRD) methodology was used to analyze the effect of some operating variables on gas-liquid two phase mixing time in an agitated tank driven by dual 6-blade Rushton turbines. The variables chosen were the impellers rotational speed (x1), gas flow rate (x2), probe location (x3) and tracer injection point (x4). The mathematical relationship of mixing time on the four significant independent variables can be approximated by a nonlinear polynomial model. Predicted values were found to be in good agreement with the experimental values (R-sq of 95.9 percent and R-Sq (Adj) of 95.7 percent for response Y). This study has shown that central composite design could efficiently be applied for the modeling of mixing time, and it is an economical way of obtaining the maximum amount of information with the fewest number of experiments.

Modeling and Optimizing of Mechanically Agitated Vessels by Central Composite Rotatable Design Method

A central composite rotatable design (CCRD) methodology was used to analyze the effect of some operating variables on gas-liquid two phase mixing time in an agitated tank driven by dual 6-blade Rushton turbines. The variables chosen were the impellers rotational speed (x1), gas flow rate (x2), probe location (x3) and tracer injection point (x4). The mathematical relationship of mixing time on the four significant independent variables can be approximated by a nonlinear polynomial model. Predicted values were found to be in good agreement with the experimental values (R-sq of 95.9 percent and R-Sq (Adj) of 95.7 percent for response Y). This study has shown that central composite design could efficiently be applied for the modeling of mixing time, and it is an economical way of obtaining the maximum amount of information with the fewest number of experiments.

Concurrent optimization of machining process parameters and tolerance allocation

With the advent use of sophisticated and high-cost machines coupled with higher labor costs, concurrent optimization of machining process parameters and tolerance allocation plays a vital role in producing the parts economically. In this paper, an effort is made to concurrently optimize the manufacturing cost of piston and cylinder components by optimizing the operating parameters of the machining processes. Design of experiments (DoE) is adopted to investigate systematically the machining process parameters that influence product quality. In addition, tolerance plays a vital role in assembly of parts in manufacturing industries. For the selected piston and cylinder component, improvements efforts are made to reduce the total manufacturing cost of the components. By making use of central composite rotatable design method, a module of DoE, a mathematical model is developed for predicting the standard deviation of the tolerance achieved by grinding process. This mathematical model, which gives 93.3% accuracy, is used to calculate the quality loss cost. The intent of concurrent optimization problem is to minimize total manufacturing cost and quality loss function. Genetic algorithm is followed for optimizing the parameters. The results prove that there is a considerable reduction in manufacturing cost without violating the required tolerance, cutting force, and power.