Prediction of styrene conversion of polystyrene/natural rubber graft copolymerization using reaction conditions: Central composite design versus artificial neural networks

Aroonsingkarat, Kittima; Hansupalak, Nanthiya

doi:10.1002/app.38148

Cited by 12 publications

(6 citation statements)

References 42 publications

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“…According to Aroonsingkarat (Aroonsingkarat and Hansupalak, 2013) have researched the influence of operating states towards transformation in the polystyrene and rubber graft co-polymerization using CCD via RSM. The temperature, amount of chain transfer agent, percentage of deproteinized rubber, and reaction time were the four factors examined.…”

Section: Research Articlementioning

confidence: 99%

Optimization and modeling of reactive conditions for free radical solution polymerization of SA-co-BA copolymer based on the yield using response surface methodology

Elarbe¹,

Elganidi²,

Abdullah³

et al. 2021

Mal. J. Fund. Appl. Sci.

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In the recent years, response surface methodology (RSM) is one of the most common optimization methods employed in the chemical process. The satisfactory model for predicting the maximum yield in solution polymerization has been a challenge due to various conditions during the synthesis process. In this study, interactive impacts of three parameters which are reaction time, concentration of initiator, and reaction temperature on the yield in free radical polymerization of SABA copolymer using toluene as solvent was investigated using experimental design central composite design (CCD) model under response surface methodology (RSM). The result showed the optimization conditions were reaction time of 7 h, initiator concentration of 1 wt %, and reaction temperature of 90 oC with the corresponding yield of 97.31%. The analysis of the regression model (ANOVA) detected an R2 value of 0.9844, that the model is able to clarify 98.44% of the data variation, and just 1.23% of the whole differences is not clarified by the model. Three experimental validation runs were carried out using the optimal replicate conditions and the highest average yield value obtained is 97.15%. There is an error of about 0.97% as compared to the expected value.Therefore, the results indicate that this model is reliable and is able to predict the yield response accurately. it established that the regression model is extremely significant, indicating a strong agreement between the expected and the experimental values of SABA yield.

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Section: Research Articlementioning

confidence: 99%

Optimization and modeling of reactive conditions for free radical solution polymerization of SA-co-BA copolymer based on the yield using response surface methodology

Elarbe¹,

Elganidi²,

Abdullah³

et al. 2021

Mal. J. Fund. Appl. Sci.

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show abstract

“…The experimental yield under the optimum condition was similar to the value forecasted by their derived model, indicating the satisfactory performance of polymerisation. According to Aroonsingkarat and Hansupalak (2013), the four variables of processing conditions examined were reaction time, the number of a chain transfer agent, temperature, and percentage of deproteinised rubber. The research investigated the effect of the reaction parameters on monomer conversion in polystyrene and rubber graft copolymerisation by RSM via CCD.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of reaction parameters for a novel polymeric additives as flow improvers of crude oil using response surface methodology

Elganidi

Elarbe

Ridzuan

et al. 2021

J Petrol Explor Prod Technol

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In recent years, polymeric additives have received considerable attention as a wax control approach to enhance the flowability of waxy crude oil. Furthermore, the satisfactory model for predicting maximum yield in free radical polymerisation has been challenging due to the complexity and rigours of classic kinetic models. This study investigated the influence of operating parameters on a novel synthesised polymer used as a wax deposition inhibitor in a crude oil pipeline. Response surface methodology (RSM) was used to develop a polynomial regression model and investigate the effect of reaction temperature, reaction time, and initiator concentration on the polymerisation yield of behenyl acrylate-co-stearyl methacrylate-co-maleic anhydride (BA-co-SMA-co-MA) polymer by using central composite design (CCD) approach. The modelled optimisation conditions were reaction time of 8.1 h, reaction temperature of 102 °C, and initiator concentration of 1.57 wt%, with the corresponding yield of 93.75%. The regression model analysis (ANOVA) detected an R2 value of 0.9696, indicating that the model can clarify 96.96% of the variation in data variation and does not clarify only 3% of the total differences. Three experimental validation runs were carried out using the optimal conditions, and the highest average yield is 93.20%. An error of about 0.55% was observed compared with the expected value. Therefore, the proposed model is reliable and can predict yield response accurately. Furthermore, the regression model is highly significant, indicating a strong agreement between the expected and experimental values of BA-co-SMA-co-MA yield. Consequently, this study’s findings can help provide a robust model for predicting maximum polymerisation yield to reduce the cost and processing time associated with the polymerisation process.

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“…The RSM is an essential optimization tool that has been widely applied in a wide range of fields such as transesterification [14], [15], solvent extraction, [16], [17]adsorption, [18], [19] Fenton process, [20] drying operations,carrageenan production, [22] synthesis andprocessing [23]- [25]etc.To the best of our knowledge, RSM is considered suitable for acid-base titrations,which will be discussed here. This study, therefore, focused on the determination of optimum conditins of the acid-base titration conditions, developmentof a statistical, computational model for the prediction of the titre values, validation of the developed model and finally, the determination of the strength and limitations of the developed model.…”

Section: Introductionmentioning

confidence: 99%

Developing A New Empirical-Computational Method, for Accurate Acid- Base Quantitative Analysis

Uthman¹,

Akinola²,

Usman³

et al. 2019

Tikrit j. eng. sci.

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The mole ratio of an acid base reaction is one of the important values to state the stoichiometric relationship between acids and bases. However, solving acid-base problems based on stoichiometry is still challenging for new chemists.This issue renders the use of a model for predicting the volume of the acid used an exciting endeavour in academia. This work was to study the individual and interactive effects of the titration parameters such as acid concentration, base concentration and the number of the indicator drops on the volume of acid used in the titration process, using methyl orange as an indicator.We also aimed to study the central composite design (CCD) model of response surface methodology (RSM) for experimental design and modelling of the process. The experimental data were analysed using analysis of variance (ANOVA) and fitted to a second-order polynomial equation using multiple regression analysis. The regression analysis showed a good fit of the experimental data to the second-order polynomial model with a coefficient of determination (R2) value of 0.9751 and model F-value of 43.37. The response surface and contour plots were generated from RSM tool for the interactive effects of the studied parameters on the volume of acid used. The developed model was further validated using existing acid-base titration problems from the Senior Secondary Certificate Examination (SSCE) past questions over 30 years. All observations indicated that the developed model was only valid for a monobasic acid.

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Prediction of styrene conversion of polystyrene/natural rubber graft copolymerization using reaction conditions: Central composite design versus artificial neural networks

Cited by 12 publications

References 42 publications

Optimization and modeling of reactive conditions for free radical solution polymerization of SA-co-BA copolymer based on the yield using response surface methodology

Optimization and modeling of reactive conditions for free radical solution polymerization of SA-co-BA copolymer based on the yield using response surface methodology

Optimisation of reaction parameters for a novel polymeric additives as flow improvers of crude oil using response surface methodology

Developing A New Empirical-Computational Method, for Accurate Acid- Base Quantitative Analysis

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