Optimisation of physical and mechanical properties of rubber compounds by response surface methodology––Two component modelling using vegetable oil and carbon black

Kukreja, T. R.; Kumar, Deepak; Prasad, Kamlesh; Chauhan, R. C.; Choe, Soonja; Kundu, Patit Paban

doi:10.1016/s0014-3057(02)00005-8

Cited by 30 publications

(16 citation statements)

References 3 publications

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“…The values for lactose (5.7%) and total solids (33%) were estimated from the superimposition of Figure 4 onto Figures 5 and 6, respectively. The derived second‐order polynomial models against cream ( X ) and SMP ( Y ) were found to be adequate as the coefficients of multiple determinants ( R 2 ) were more than 80% 11,12 except for the melting resistance.…”

Section: Resultsmentioning

confidence: 98%

Optimization of ingredients for the manufacture of soft‐serve ice‐cream (Softy) by response surface methodology (RSM)

Sharma

Prasad

Jindal

et al. 2003

Int J of Dairy Tech

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Soft ice‐cream samples were prepared with different proportions of cream and skimmed milk powder (SMP) and were examined for quality by physico‐chemical parameters and sensory attributes. The specific proportions of cream and SMP were designed using the central composite rotatable design (CCRD). Response surface analysis indicated that sensory flavour, texture and overall acceptability (OAA) scores of soft ice‐cream varied from 7.12 to 8.86, 6.66 to 8.03 and 6.90 to 8.45, respectively. The differences in the sensory scores for respective attributes were highly significant (P ≤ 0.01). The soft ice‐cream containing a minimum level of 3.24% cream and 4.94% SMP resulted in a product having a minimum of 4.3% fat, 5.7% lactose and 33% total solids, with a minimum OAA score of 8. The derived mathematical second‐order polynomial models against cream (X) and SMP (Y) were found to be adequate as coefficients of multiple determinants (R2) were more than 80% except for melting resistance.

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Section: Resultsmentioning

confidence: 98%

Optimization of ingredients for the manufacture of soft‐serve ice‐cream (Softy) by response surface methodology (RSM)

Sharma

Prasad

Jindal

et al. 2003

Int J of Dairy Tech

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“…Using response surface methodology with minimum number of experiments, it is possible to obtain quantitative equations for the effect of processing condition on the properties of SBR/organoclay nanocomposites. Application of this method has been reported in rubber field by Kukrejaa et al [19].…”

Section: Experimental Designmentioning

confidence: 91%

Evaluating the effect of processing conditions and organoclay content on the properties of styrene-butadiene rubber/organoclay nanocomposites by response surface methodology

Ghasemi

Karrabi²,

Mohammadi³

et al. 2010

Express Polym. Lett.

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Abstract. In this work, basic mathematical models and response surface graphs have been used to illustrate the relationship between mixing parameters in internal mixer and properties of the SBR (styrene butadiene rubber)/organoclay composites. Using a Box-Behnken statistical design experiment methodology, the effects of mixing temperature (80-140°C), mixing time (4-12 min) and nano filler amount (3-9 phr) in SBR nanocomposites on the properties (tensile properties, scorch time and Mooney viscosity) were evaluated. It was found that the mixing parameters (time and temperature) have the predominant role in properties and morphology of nanocomposite. The R 2 values (the R 2 values indicate the degree of agreement between the experimental results with those predicted by model) of all responses were above 0.85. Increasing temperature and mixing time facilitated a better organoclay dispersion which resulted in a better tensile property. With increase in nanoclay amount in composite the scorch time and Mooney viscosity decreased. The morphology of nanocomposite was studied by XRD (X-ray diffraction) and TEM (Transmission electron microscope). Intercalation and exfoliation of the nanoclay were observed for samples with higher temperature and longer mixing time. Due to thermal degradation of the rubber matrix at 140°C, tensile properties of the nanocomposite were decreased.

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“…In fact, Response surface methodology (RSM) has been described as a very functional statistical tool for determination of optimum processes parameters for lab scale to industrial scale, as highlighted by variousworkers [27–29]. RSM covers experimental design, process optimization and empirical modeling where targeted response may fluctuate with numerous process variables (termed factors).…”

Section: Introductionmentioning

confidence: 99%

Polypropylene Production Optimization in Fluidized Bed Catalytic Reactor (FBCR): Statistical Modeling and Pilot Scale Experimental Validation

2014

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Propylene is one type of plastic that is widely used in our everyday life. This study focuses on the identification and justification of the optimum process parameters for polypropylene production in a novel pilot plant based fluidized bed reactor. This first-of-its-kind statistical modeling with experimental validation for the process parameters of polypropylene production was conducted by applying ANNOVA (Analysis of variance) method to Response Surface Methodology (RSM). Three important process variables i.e., reaction temperature, system pressure and hydrogen percentage were considered as the important input factors for the polypropylene production in the analysis performed. In order to examine the effect of process parameters and their interactions, the ANOVA method was utilized among a range of other statistical diagnostic tools such as the correlation between actual and predicted values, the residuals and predicted response, outlier t plot, 3D response surface and contour analysis plots. The statistical analysis showed that the proposed quadratic model had a good fit with the experimental results. At optimum conditions with temperature of 75°C, system pressure of 25 bar and hydrogen percentage of 2%, the highest polypropylene production obtained is 5.82% per pass. Hence it is concluded that the developed experimental design and proposed model can be successfully employed with over a 95% confidence level for optimum polypropylene production in a fluidized bed catalytic reactor (FBCR).

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Optimisation of physical and mechanical properties of rubber compounds by response surface methodology––Two component modelling using vegetable oil and carbon black

Cited by 30 publications

References 3 publications

Optimization of ingredients for the manufacture of soft‐serve ice‐cream (Softy) by response surface methodology (RSM)

Optimization of ingredients for the manufacture of soft‐serve ice‐cream (Softy) by response surface methodology (RSM)

Evaluating the effect of processing conditions and organoclay content on the properties of styrene-butadiene rubber/organoclay nanocomposites by response surface methodology

Polypropylene Production Optimization in Fluidized Bed Catalytic Reactor (FBCR): Statistical Modeling and Pilot Scale Experimental Validation

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