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ABSTRACT
The present study is focussed to understand the importance and consequence of alternative possibilities of simulating, modeling, and optimizing methodologies and of the procedural enhancement in soft computing, particularly, in the process of transesterification to produce biodiesel. Here, grapeseed oil is extracted from its feedstock through a combination of cold pressing and soxhlet extraction process. Single-stage transesterification was employed with catalyst concentration, molar ratio, and reaction duration as the process parameters for the transformation of mono-alky triglycerides to fatty-acid methyl esters. The effectiveness and coherence of the response surface methodology (RSM) tool in predicting the biodiesel yield were measured in accordance with central composite designusing the interdependence ANOVA coefficients. Furthermore, the optimal process parameters of the transesterification process which produced maximum grapeseed biodiesel were compared with the outcome of RSM methodology to understand its efficacy. The RSM tool predicted the maximum yield of biodiesel as 97.62% from catalyst concentration of 1.045 g of NaOH, molar ratio of 0.2758 v/v, and reaction duration of 66.6 min, which was then validated experimentally with a yield of 97.7% grapeseed biodiesel. The grapeseed biodiesel thus produced was tested for its physiochemical properties and was found to be within ASTM standards.
Nomenclature and Abbreviation
| CCRD | = | central composite rotatable design |
| ANN | = | artificial neural network |
| ANFIS | = | adaptive neurofuzzy interference |
| RSM | = | response surface methodology |
| FAME | = | fatty acid methyl esters |
| RMSE | = | root mean square error |
| FAEE | = | fatty acid ethyl esters |
| FFA | = | free fatty acids |
| MAPD | = | mean absolute percent deviation |
| CCD | = | central composite design |
| MRPD | = | mean relative percent deviation |
| MSE | = | mean square error |
| R2 | = | Coefficient of determination |
| R | = | Correlation coefficient |
| MOR | = | Molar ratio |
| CC | = | catalyst concentration |
| TI | = | reaction time |
| ANOVA | = | analysis of variance |
| MF | = | membership function |
| SEP | = | standard error percentage |
Additional information
Notes on contributors
Hariram Venkatesan
Hariram Venkatesan holds a doctoral degree from Anna University Chennai, India. He finished his masters in Automobile Engineering from the same institution. His research interest includes Bio-energy and Engine Combustion.
Bose A
Bose A is a Post graduate student in the Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai, India.
Seralathan Sivamani
Seralathan Sivamani holds a doctoral degree from Hindustan Institute of Technology and Science, Chennai, India. He completed his masters from IIT Madras. His research interest includes Energy, Turbo machines and Wind turbine.