Application of design of experiments and artificial neural network in optimization of ultrasonic energy-assisted transesterification ofSardinella longicepsfish oil to biodiesel

Autor:	P. Arul Franco, K. Ramesh, N. Shenbaga Vinayaga Moorthi
Rok vydání:	2015
Předmět:	Engineering Biodiesel Petroleum engineering business.industry Sardine General Engineering Transesterification Fish oil Pulp and paper industry chemistry.chemical_compound chemistry Biodiesel production Multilayer perceptron Response surface methodology Methanol business
Zdroj:	Journal of the Chinese Institute of Engineers. 38:731-741
ISSN:	2158-7299 0253-3839
DOI:	10.1080/02533839.2015.1027740
Popis:	The competent and efficient utilization of feedstocks is highly essential in the transesterification of biodiesel from sardine fish oil. The identification of optimal reaction parameters is of high importance to maximize the yield of biodiesel produced from sardine fish oil at low cost. Application of ultrasonic energy-assisted biodiesel production from sardine fish oil catalyzed by KOH catalyst has been studied under different conditions. Response surface methodology (RSM) based on central composite rotatable design (CCRD) was employed to optimize the three important process parameters: methanol/oil molar ratio (X1), KOH catalyst concentration (X2), and reaction time (X3) for transesterification of sardine fish oil using ultrasonic energy. Artificial neural network (ANN) models with two feed-forward back-propagation neural network architecture, multilayer perceptron networks and radial basis function networks have been developed to obtain a good correlation between the input variables responsible for the...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e7cc2486b680882dd6e077c2eaac2fd5 https://doi.org/10.1080/02533839.2015.1027740 Zobrazit plný text záznamu