Modeling the Biocatalytic Method of Lipid Extraction Using Artificial Neural Networks

Abstract

The paper presents the data on lipid fraction extraction from the raw fat of hibernating hunting animals. The processing of valuable raw materials must be maximized. For this purpose, various methods of rendering are used. As a result of temperature exposure, the protein part of raw fat undergoes significant changes. The protein denatures under the influence of temperature, and the dross formed during the rendering process absorbs and retains up to 30% of the fat. The authors propose using proteolytic enzyme preparations for a more complete extraction of fats, as the enzymes will hydrolyze the protein into compounds of lower molecular weight both before and during the rendering process. The experiment proved that the biocatalytic method allows achieving a fat yield of more than 95%. The best result can be obtained if the rendering is carried out at optimal parameters, which can be defined using a mathematical model. Mathematical modeling was carried out using an artificial neural network. During the study, a fully connected neural network was designed; it had eight hidden layers with 64 neurons in each, and its accuracy was measured by mean relative error, which amounted to 5.16%. With the help of the network, the optimal values of applied concentration, temperature and duration of rendering, at which a fat yield of more than 98% is achieved, were determined for each enzyme preparation. After that, the obtained values were confirmed experimentally. Thus, the study showed the efficiency of using artificial neural networks for modeling the biocatalytic method of lipid extraction.