2013
DOI: 10.1016/j.fuproc.2013.03.025
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The optimization of biodiesel production by using response surface methodology and its effect on compression ignition engine

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Cited by 43 publications
(18 citation statements)
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“…In this run the temperature is the significant factor that affects the conversion values even though the alcohol to oil molar ratio and catalyst amount are sufficient. Similar observation has been reported by other researchers work [7,8]. The sequential model sum of squares [Type 1] suggested a mean vs total and quadratic model vs 2FI for further model development as shown in Table 7.…”
supporting
confidence: 85%
“…In this run the temperature is the significant factor that affects the conversion values even though the alcohol to oil molar ratio and catalyst amount are sufficient. Similar observation has been reported by other researchers work [7,8]. The sequential model sum of squares [Type 1] suggested a mean vs total and quadratic model vs 2FI for further model development as shown in Table 7.…”
supporting
confidence: 85%
“…On the other hand, catalyst and methanol concentrations have been the most influencing factors in transesterification, and their effects are related each other [8,33,34]. Therefore, these variables for the first and second stages were considered in the experimental design.…”
Section: Reaction Conditions and Variables Of The Designmentioning
confidence: 99%
“…The central conditions of the design produced biodiesel with average ester content of 93.0%. The results were analyzed through multiple regressions, testing various models such as linear, two-factor interaction, three-factor interaction, two and three factor interaction, cubic, quadratic, and cubic plus quadratic models, with the quadratic one best fitting real data as was seen for the transesterification reaction in previous works [8,33,35]. Equation (1) shows the estimated response model equation for methyl ester content of biodiesel (related to original factors).…”
Section: Regression Model Developmentmentioning
confidence: 99%
“…Radial Basis Functions (RBFs) is becoming an established approach in recent years to simulate different problems in many areas of engineering [1][2][3][4][5][6][7][8][9][10]. This technique was first proposed by Kansa [11], after the work of Hardy [12] on multivariate approximation.…”
Section: Radial Basis Functionsmentioning
confidence: 99%