Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks

Abstract

a b s t r a c tThe aim of this work was to model and predict the process of bioethanol production from intermediates and byproduct of sugar beet processing by applying artificial neural networks. Prediction of one substrate fermentation by neural networks had the same input variables (fermentation time and starting sugar content) and one output value (ethanol content, yeast cell number or sugar content). Results showed that a good prediction model could be obtained by networks with single hidden layer. The neural network configuration that gave the best prediction for raw or thin juice fermentation was one with 8 neurons in hidden layer for all observed outputs. On the other side, the optimal number of neurons in hidden layer was found to be 9 and 10 for thick juice and molasses, respectively. Further, all substrates data were merged, which led to introducing an additional input (substrate type) and defining all outputs optimal network architecture to 3-12-1. From the results the conclusion was that artificial neural networks are a good prediction tool for the selected network outputs. Also, these predictive capabilities allowed the application of the Garson's equation for estimating the contribution of selected process parameters on the defined outputs with satisfactory accuracy.

Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

“…Non-linear models are becoming more and more popular in practice. A particular increase can be observed in the use of artificial neural networks in agriculture, where better analysis results are often obtained than with classical statistical methods [1,[6][7][8][11][12][13][14][15][16][17][18][19].…”

Fermentation: Metabolism, Kinetic Models, and Bioprocessing

“…Another way to construct mathematical models of microbial growth is the use of FFNs, which describe the behavior of different configurations of bioreactors. An example of this type of applications is the modeling of the production of bioethanol obtained from sugar beets [49]. Here, a three-layer FFN is used to describe the dynamic behavior of the reactor.…”

Application of artificial neural networks in modeling and optimization of biofuels production

“…The aim of the study reported by Grahovac et al (Grahovac et al, 2016) was modeling of the bioprocess for production of bioethanol from intermediates and byproducts of sugar beet processing using artificial neural networks based on multilayer perceptron architecture. Prediction of fermentation of each substrate using neural network had the same input variables (fermentation time and initial sugar content) and one output variable (ethanol content, yeast cells number or sugar content), while transfer function was hyperbolic tangential function.…”