scholarly journals Coverage response modeling for auto base paints formulation

2021 ◽  
Vol 12 (2) ◽  
pp. 129-134
Author(s):  
A. Bello ◽  
A.O. Ameh ◽  
M.T. Isa ◽  
M.S. Galadima ◽  
D.O. Adeoye
Keyword(s):  
Response Surface Methodology ◽  
Statistical Analysis ◽  
Standard Deviation ◽  
Response Surface ◽  
Analysis Of Variance ◽  
Polynomial Model ◽  
Good Relationship ◽  
Order Polynomial ◽  
Response Modeling ◽  
Independent Parameters

Response Surface Methodology (RSM) was used to investigate the effects of binder, solvent, and pigment concentrations on the coverage response. The optimum coverage response of the auto base paint formulated was 1.5m2. Statistical analysis of the results showed that the data were adequately fitted by a second-order polynomial model Analysis of variance (ANOVA) also showed that the interactions between the independent parameters (binder, solvent, and pigment) have significant effects on the response (coverage). Diagnostics case statistics indicated that, the optimum experimental value of the cover age equal that of the predicted. This showed a good relationship between the actual and predicted response as evidenced in the R2 value of 0.9204 and a standard deviation of 0.1 obtained.

A predictive model for the synthesis of polylactide from lactic acid by response surface methodology

e-Polymers ◽  
2011 ◽  
Vol 11 (1) ◽  
Author(s):  
P. Agarwal ◽  
A. Mondal ◽  
P.K. Mishra ◽  
P. Srivastava
Keyword(s):  
Response Surface Methodology ◽  
Statistical Analysis ◽  
Response Surface ◽  
Process Optimization ◽  
Model Equation ◽  
Statistical Significance ◽  
Polynomial Model ◽  
Statistical Process ◽  
Central Composite Experimental Design ◽  
Experimental Values

AbstractThe present work describes the statistical process optimization of a lowcost production process of PLA using organometallic (stannous octoate) compounds. The process optimization for both lactide and polylactide, was developed by factorial design and response surface methodology. The influence of different experimental parameters such as reaction temperature, time, concentration of catalyst and co-initiator concentration on the yield of lactide and polylactide has been evaluated. There are many studies reported on the synthesis of polylactide but no earlier study exists for the application of statistical analysis in determining the interactions among the process variables for lactide and polylactide production. Central composite experimental design with multiple linear regression has been used to estimate the coefficients of the polynomial model equation for the yield(s) of both lactide and polylactide. The statistical significance of polynomial model equation was validated by F test (ANOVA). Determination coefficient (R2) values found to be 0.913 and 0.958 for lactide and polylactide respectively, states that predicted values were in good agreement with the experimental values. Results of the statistical analysis showed that the model fits in all cases. Above synthesised polymer was characterized by FT-IR, 1H-NMR, DSC and GPC to confirm the polymer structure and properties.

Optimized Formulation of APN Toothpaste with Biochemical Materials Using Response Surface Methodology

2013 ◽  
Vol 676 ◽  
pp. 108-113
Author(s):  
Ting Kong ◽  
Chao Yan Zhang ◽  
Bin Bao ◽  
Long Sha ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Experimental Data ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Panax Notoginseng ◽  
Second Order ◽  
Polynomial Model ◽  
Composite Score ◽  
Optimum Formulation ◽  
Order Polynomial ◽  
Addition Amount

Response surface methodology(RSM) was used to optimize the formulation of one toothpaste with aglycone extracted from Panax notoginseng(APN). Biochemical materials are important components in toothpastes. The addition amount of APN, thickener, different ratios of humectant and friction agent were selected as three factors for the design. Our results showed that the experimental data could be adequately fitted into a second-order polynomial model. Addition amount of thickener and humectant : friction agent had a significant effect on the composite score. The optimum formulation for preparing APN toothpaste was predicted to be: APN, 0.12%; thickener, 1.58%; humectant : friction agent, 1.01.

Optimization of Manufacturing Process of Traditional Algerian Jben Cheese Using Response Surface Methodology (RSM)

2018 ◽  
Vol 8 (1) ◽  
pp. 31-42
Author(s):  
M. Amimour ◽  
T. Idoui ◽  
A. Cheriguene
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Manufacturing Process ◽  
Milk Powder ◽  
Statistical Design ◽  
Polynomial Equation ◽  
Enzymatic Extract ◽  
Traditional Cheese ◽  
Order Polynomial ◽  
Predicted Values

The Aim of this study was to develop an optimized method for manufacturing process of traditional Algerian Jben cheese, using response surface methodology (RSM). In order to develop the objective method of making this traditional cheese, several factors have been studied and a Plackett-Burman statistical design was applied. The effects of the four screened factors (enrichment with milk powder, 10 - 20 g/l; pH of milk, 5.75 - 6.75, enzymatic extract dose, 0.5 - 1.5 ml and coagulation temperature 40 - 60 °C) on the response were investigated, using a Box-Behnken statistical design. Multiple regression analysis was used so that experimental data fits to a second-order polynomial equation. This multiple analysis showed that the model explains about 90.73% of the variation. Based on statistical results, it can be noticed that enrichment with milk powder and pH of milk (Ë‚0.0001***) were highly significant factor influincing cheese yield. The optimal production parame-ters that maximized cheese product (20 g/l enrichment with milk powder, 5.75 pH of milk, 1.29 ml enzymatic extract dose and 60°C coagulation temperature) and the maximal predicted cheese yield (52.68 % ) were found out through response surface methodology. Under these conditions, a verification experiment was carried out and cheese yield was found to be 49.46 %. The overall percentage of agreement for the experimental results (more than 93 % validity) with the predicted values indicates the validation of the statistical model and the success of the optimization process.

scholarly journals Effect of Process Parameters on the Surface Roughness and Kerf Width of Mild Steel during Plasma Arc Cutting Using Response Surface Methodology

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Ekhaesomi A Agbonoga ◽  
Oyewole Adedipe ◽  
Uzoma G Okoro ◽  
Fidelis J Usman ◽  
Kafayat T Obanimomo ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Analysis Of Variance ◽  
Process Parameters ◽  
Cutting Speed ◽  
Gas Pressure ◽  
Kerf Width ◽  
Plasma Arc ◽  
Plasma Arc Cutting

This study investigated the effects of process parameters of plasma arc cutting (PAC) of low carbon steel material using analysis of variance. Three process parameters, cutting speed, cutting current and gas pressure were considered and experiments were conducted based on response surface methodology (RSM) via the box-Behnken approach. Process responses viz. surface roughness (Ra) and kerf width of cut surface were measured for each experimental run. Analysis of Variance (ANOVA) was performed to get the contribution of process parameters on responses. Cutting current has the most significant effect of 33.43% on the surface roughness and gas pressure has the most significant effect on  kerf width of  41.99% . For minimum surface roughness and minimum kerf width, process parameters were optimized using the RSM. Keywords: Cutting speed, cutting current, gas pressure,   surface roughness, kerf width

Enhanced Alkali Pretreatment of Narrow Leaves Cattail by Response Surface Methodology

2014 ◽  
Vol 875-877 ◽  
pp. 1637-1641
Author(s):  
Arrisa Sopajarn ◽  
Chayanoot Sangwichien
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Residence Time ◽  
Preliminary Test ◽  
Polynomial Equation ◽  
Alkali Concentration ◽  
Pretreatment Condition ◽  
Order Polynomial ◽  
Optimized Conditions ◽  
Central Composite

The purpose of this work is to develop a pretreatment process of lingo-cellulosic ethanol production from narrow leaves cattail (Typha angustifolia) by using alkali catalysis with the response surface methodology (RSM) as a central composite design (CCD). The first step, LiOH, NaOH, and KOH were used as catalytic alkali for preliminary test. Second, the suitable alkali from first step was selected to optimize of pretreatment condition of three independent variables (alkali concentration, temperature, and residence time) that varies at CCD five codes (-2, -1, 0, 1, 2). Sodium hydroxide (NaOH) is the proper alkali because it could increase cellulose more than KOH and nearby LiOH while it is cheapest. RSM result shows the optimized pretreatment condition based on cellulose increased which obtained from this study that is NaOH 5 % w/v at 100 °C and residence time for 120 min. Beside, this condition was analyzed using an ANOVA with a second order polynomial equation after eliminated non-significant terms. At the optimized conditions, cellulose increased, hemicellulose decreased and weight recovery were achieved 77.81%, 80.59, and 41.65%, respectively. Moreover, the model was reasonable to predict the response of strength with less than 5% error.

Growth Characteristics and Development of a Predictive Model for Bacillus cereus in Fresh Wet Noodles with Added Ethanol and Thiamine

2011 ◽  
Vol 74 (4) ◽  
pp. 658-664 ◽  
Author(s):  
BO-YEON KIM ◽  
JI-YOUNG LEE ◽  
SANG-DO HA
Keyword(s):  
Growth Rate ◽  
Response Surface Methodology ◽  
Bacillus Cereus ◽  
Predictive Model ◽  
Response Surface ◽  
Growth Rates ◽  
Vitamin B1 ◽  
Growth Characteristics ◽  
Quadratic Polynomial ◽  
Polynomial Model

Response surface methodology was used to determine growth characteristics and to develop a predictive model to describe specific growth rates of Bacillus cereus in wet noodles containing a combination of ethanol (0 to 2% [vol/wt]) and vitamin B1 (0 to 2 g/liter). B. cereus F4810/72, which produces an emetic toxin, was used in this study. The noodles containing B. cereus were incubated at 10°C. The growth curves were fitted to the modified Gompertz equation using nonlinear regression, and the growth rate values from the curves were used to establish the predictive model using a response surface methodology quadratic polynomial equation as a function of concentrations of ethanol and vitamin B1. The model was shown to fit the data very well (r2 = 0.9505 to 0.9991) and could be used to accurately predict growth rates. The quadratic polynomial model was validated, and the predicted growth rate values were in good agreement with the experimental values. The polynomial model was found to be an appropriate secondary model for growth rate (GR) and lag time (LT) based on the correlation of determination (r2 = 0.9899 for GR, 0.9782 for LT), bias factor (Bf = 1.006 for GR, 0.992 for LT), and accuracy factor (Af = 1.024 for GR, 1.011 for LT). Thus, this model holds great promise for use in predicting the growth of B. cereus in fresh wet noodles using only the bacterial concentration, an important contribution to the manufacturing of safe products.

Optimization of Conditions for Collagen Extraction from the Swim Bladders of Grass Carp (Ctenopharyngodon idella) by Response Surface Methodology

2010 ◽  
Vol 6 (3) ◽  
Author(s):  
Bao Zhang ◽  
Yunzhong Chen ◽  
Xuefei Wei ◽  
Mingqi Li ◽  
Mengjin Wang
Keyword(s):  
Response Surface Methodology ◽  
Acetic Acid ◽  
Statistical Analysis ◽  
Response Surface ◽  
Acid Concentration ◽  
Grass Carp ◽  
Ctenopharyngodon Idella ◽  
Acetic Acid Concentration ◽  
Optimal Conditions ◽  
Solid Ratio

The effects of liquid-solid ratio, acetic acid concentration and extraction time on the yield of acid-soluble collagen(ASC) from the swim bladders of grass carp were optimized by statistical analysis using response surface methodology. The response surface methodology (RSM) was used to optimize the yield of ASC by implementing the Box-Wilson design. Statistical analysis of the results showed that the linear and quadric terms of these three variables had significant effects. However, no interactions between the three variables were found to contribute to the response at a significant level. The optimal conditions for higher yield of ASC were a liquid-solid ratio of 17.85, an acetic-acid concentration of 0.54 M and a time of 34 h. Under these conditions, the model predicted an ASC yield of 8.39%. Verification of the optimization showed that an ASC yield of 8.21±0.15% was observed under the optimal conditions. The experimental values agreed with the predicted values, using analysis of variance, indicating an excellent fit of the model used and the success of response surface methodology for modeling extraction of ASC from the swim bladders of grass carp.

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