Abstract
The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data.
Highlights
Nowadays, dependence on fossil fuels have caused an environmental crisis and a high production volume of pollutants [1,2]
The present study completes the previous study by developing hybrid extreme learning machine (ELM)-response surface methodology (RSM) and ELM-SVR methods that enable us to focus on both prediction and optimization of the complex production system
Data were collected during a transesterification process for producing ethyl and methyl esters (Table 1)
Summary
Dependence on fossil fuels have caused an environmental crisis and a high production volume of pollutants [1,2]. Sharon et al [22] studied trained ANN in a SIMULINK model for biodiesel production and the prediction of a diesel engine performance They studied the effect of NaOH on transesterification of fried oils. Maran and Priya [28] in another study employed RSM and ANN methods for modeling biodiesel production from muskmelon oil using an ultrasound-assisted reactor. In reference [35], the main platform of modeling was to employ the single ANN method, RSM, and hybrid ANFIS methods to estimate the yield and cost of biodiesel production. The present study completes the previous study by developing hybrid ELM-RSM and ELM-SVR methods that enable us to focus on both prediction and optimization of the complex production system (biodiesel production system)
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