Ultrasound assisted biodiesel production from sesame (Sesamum indicum L.) oil using barium hydroxide as a heterogeneous catalyst: Comparative assessment of prediction abilities between response surface methodology (RSM) and artificial neural network (ANN)

Abstract

The present study estimates the prediction capability of response surface methodology (RSM) and artificial neural network (ANN) models for biodiesel synthesis from sesame (Sesamum indicum L.) oil under ultrasonication (20 kHz and 1.2 kW) using barium hydroxide as a basic heterogeneous catalyst. RSM based on a five level, four factor central composite design, was employed to obtain the best possible combination of catalyst concentration, methanol to oil molar ratio, temperature and reaction time for maximum FAME content. Experimental data were evaluated by applying RSM integrating with desirability function approach. The importance of each independent variable on the response was investigated by using sensitivity analysis. The optimum conditions were found to be catalyst concentration (1.79 wt%), methanol to oil molar ratio (6.69:1), temperature (31.92°C), and reaction time (40.30 min). For these conditions, experimental FAME content of 98.6% was obtained, which was in reasonable agreement with predicted one. The sensitivity analysis confirmed that catalyst concentration was the main factors affecting the FAME content with the relative importance of 36.93%. The lower values of correlation coefficient (R(2)=0.781), root mean square error (RMSE=4.81), standard error of prediction (SEP=6.03) and relative percent deviation (RPD=4.92) for ANN compared to those R(2) (0.596), RMSE (6.79), SEP (8.54) and RPD (6.48) for RSM proved better prediction capability of ANN in predicting the FAME content.

Potential of Ripe Plantain Fruit Peels as an Ecofriendly Catalyst for Biodiesel Synthesis: Optimization by Artificial Neural Network Integrated with Genetic Algorithm

“…The number of hidden neurons was determined by testing several neural networks iteratively until the root mean square error (RMSE) value of the output was minimized [8,22]. The experimental data set was divided into two subsets: training set and testing set [21].…”

“…One key advantage of ANN is that it can be used to predict outputs of a new input data set once a data set has been trained well by a neural network. ANNs have been successfully applied as a modeling tool in many research areas, including esterification [19] and transesterification [8,20,21] processes. In the work of Betiku and Ajala [8] in which the efficacy of ANN was tested, the authors used calcined unripe plantain peel as a heterogeneous catalyst, yellow oleander oil and methanol for the transesterification reaction.…”

“…Whereas, Avramovic et al [20] applied ANN to the transesterification reaction in which synthetic CaO as a heterogeneous catalyst, sunflower oil and ethanol were used. While Sarve et al [21] employed ANN in the ultrasound-assisted transesterification using barium hydroxide as a heterogeneous catalyst, sesame oil and methanol. In order to maximize or minimize the response of an objective function, models generated using ANN are usually coupled with an optimization tool such as genetic algorithm, GA [20,22], particle swarm Sustainability 2018, 10, 707 3 of 15 optimization, PSO [23], rotation inherit optimization, RIO [24], to achieve this by optimizing the operating input variables involved.…”

Development of a sample preparation strategy for the determination of tungsten in soil samples by inductively coupled plasma mass spectrometry using a response surface methodology

“…32,34,35 Among the variables and according to Table 2, temperature is the most significant factor (P < 0.01), while extraction time is significant. Additionally, the individual effects of HCl and phosphoric acid are statistically insignificant.…”

“…b, k, and e are the regression coefficient, number of factors studied, and random error, respectively. 32,33,42 The data obtained from the CCD were analyzed in terms of tungsten levels in the solutions using the Design Expert (version 7.0.0, STAT-EASE Inc., Minneapolis, MN, USA) software package, and all runs were analyzed in triplicate using the method of ICP-MS.…”