Data‐Driven Modeling of Biodiesel Production Using Artificial Neural Networks

Abstract

Data-driven modeling of biodiesel production was developed by simultaneous transesterification and esterification of rapeseed oil and myristic acid with methanol, without catalyst or with different amounts of sulfated zirconia catalyst. An artificial neural network (ANN)-based model was created with experimental literature data. The input data, i.e., reaction time, catalyst, temperature, and methanol-to-oil ratio, and output data, i.e., total fatty acid methyl ester and oleic acid methyl ester, were considered to develop the model. Multiple input single output (MISO) ANN architecture was taken to predict the above targeted two output parameters. The proposed ANN model is computationally efficient and works reasonably well when tested on biodiesel production for solving the MISO model.

Modeling of Solar Photocatalytic Degradation of Rhodamine B Dye by TiO2 Nanoparticles Using an Artificial Neural Network

“…Further, the development of a dynamic model is an important task in order to study the behavior of the process. Recently, data-driven model development has received more attention, and among these models, artificial neural networks (ANN) have shown a wide range of applications [17][18][19][20][21]. Lenzi et al [22] developed a model for the photocatalytic degradation of maxilon blue 5G dye by a TiO 2 -based photocatalyst under artificial UV irradiation using an ANN.…”