Optimization of Biofuel Production Process Using Design of Experiments (Doe)

This study focuses on optimizing the process of biofuel production from citrus peel using the Design of Experiments (DOE) technique. This study aims to determine the optimal values for the variables that have a significant impact on the production of biofuel. The variance within and between data groups was determined using the analysis of variance (ANOVA) table. The ANOVA table shows how much of the response variable's variation (biofuel production) can be explained by the independent variables (A, B, C, D, E, AB, AC, AD, AE, and BJ) and how much is caused by random error. The ANOVA table comprises of three primary parts: the F-statistic, the p-value, the df, the mean square (MS), the source of variation, and the sum of squares (SS). The wellspring of variety alludes to the beginning of the information variety, which can be either the lingering or the model. The amount of squares estimates the information's changeability, with the absolute amount of squares addressing the amount of the squared deviations of the genuine qualities from the mean worth. The residual is the sum of the squared deviations from the predicted values of the actual values, while the model's sum of squares is the sum of the squared deviations from the mean of the predicted values. The model has 10 degrees of freedom (the number of independent variables) and the residual has 4 degrees of freedom (the number of observations minus the number of independent variables). These degrees of freedom represent the number of independent pieces of information used to estimate a parameter. The mean square, which indicates the typical amount of variation for each variation source, is calculated by dividing the sum of squares by the degrees of freedom. The degree to which the model explains the variation in the data is indicated by the F-statistic, which is the ratio of the model's mean square to the residual's mean square. The probability of obtaining an F-statistic that is as large as the one observed if the null hypothesis is true is represented by the p-value. The independent variables' insignificant impact on biofuel production is the null hypothesis in this instance. The model's p-esteem in this study is under 0.05, demonstrating that the free factors essentially affect biofuel creation and that the model is genuinely huge. In addition, the model is significant because the F-statistic is relatively large in comparison to the F-distribution for the 10 and 4 degrees of freedom, respectively. The estimated coefficients for the linear regression model used to investigate the production of biofuel from citrus peel can be found in the ANOVA coefficients table. The table provides a list of the intercept and independent variables' coefficients, standard errors, t-values, and p-values. When all of the independent variables are zero, the intercept has a coefficient of 0.0672, indicating the estimated value of the response variable. The fact that the intercept does not differ significantly from zero is supported by the fact that its p-value is not significant. The fact that the coefficients of the independent variables A, E, AC, AD, AE, and BJ are not statistically significant indicates that these variables have little impact on the response variable. On the other hand, the positive coefficients and significant p-values of the independent variables B and C suggest that an increase in their values could result in an increase in the production of biofuel from citrus peel. In conclusion, the key variables that influence the production of biofuel from citrus peel have been identified thanks to the use of the Design of Experiments (DOE) method. According to the findings of this study, an increase in the production of biofuel from citrus peel may result from an increase in the values of the independent variables B and C. The development of environmentally friendly energy sources and the optimization of biofuel production processes will benefit greatly from these findings