Artificial neural network approach to modeling of alcoholic fermentation of thick juice from sugar beet processing

Abstract

In this paper the bioethanol production in batch culture by free Saccharomyces cerevisiae cells from thick juice as intermediate product of sugar beet processing was examined. The obtained results suggest that it is possible to decrease fermentation time for the cultivation medium based on thick juice with starting sugar content of 5-15 g kg-1. For the fermentation of cultivation medium based on thick juice with starting sugar content of 20 and 25 g kg-1 significant increase in ethanol content was attained during the whole fermentation process, resulting in 12.51 and 10.95 dm3 m-3 ethanol contents after 48 h, respectively. Other goals of this work were to investigate the possibilities for experimental results prediction using artificial neural networks (ANNs) and to find its optimal topology. A feed-forward back-propagation artificial neural network was used to test the hypothesis. As input variables fermentation time and starting sugar content were used. Neural networks had one output value, ethanol content, yeast cell number or sugar content. There was one hidden layer and the optimal number of neurons was found to be nine for all selected network outputs. In this study transfer function was tansig and the selected learning rule was Levenberg-Marquardt. Results suggest that artificial neural networks are good prediction tool for selected network outputs. It was found that experimental results are in very good agreement with computed ones. The coefficient of determination (the R-squared) was found to be 0.9997, 0.9997 and 0.9999 for ethanol content, yeast cell number and sugar content, respectively

Modelling of hydrochemical and hydromechanical parameters’ synergism in the process of solid deposit creation in geothermal and other hard waters

“…The interest in the neuron network concept has increased over the last few years, 5 because of the ability to predict models based on neuron networks along with their adjustability. 5 The basic advantage of modelling with the use of neuron networks is the fact that it is enough to have the data on the input and output parameters with adequate training of the network to successfully generalize a model of satisfactory accuracy which enables precise prediction of output values for a new set of input data. 5 This advantage led to a wide application of neuron networks in various engineering disciplines.…”

“…5 The basic advantage of modelling with the use of neuron networks is the fact that it is enough to have the data on the input and output parameters with adequate training of the network to successfully generalize a model of satisfactory accuracy which enables precise prediction of output values for a new set of input data. 5 This advantage led to a wide application of neuron networks in various engineering disciplines. 5 For application of this concept on modelling of chemical and biochemical processes usually the feed forward neuron network with a back propagation algorithm is used, composed of three elementary neuron layers; input, hidden and output.…”

Electrostatic and Topological Features as Predictors of Antifungal Potential of Oxazolo Derivatives as Promising Compounds in Treatment of Infections Caused by Candida albicans

“…19 The data normalization was carried out by min-max normalization method. 20,21 Prior to ANN modeling, the analyzed compounds were divided into the training set (compounds 1, 2, 3, 4, 5, 6, 8, 9, 11, 13, 15, 17, 18, 19, 20, 21, 22 and 23), validation set (compounds 12, 14 and 24) and test set (compounds 7, 10 and 16).…”

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

“…The optimal ANN architecture consisted of 8 hidden neurons for all monitored fermentation parameters with raw and thin juice, while for thick juice the optimal number of neurons in the hidden layer was found to be 9 [11].…”