Inovações na produção de hidrolisados de soja: modelagem e análise econômica de processo

Abstract

Soybean and its derivatives have the desired properties for food either for humans or various animals because of its high protein content, which is associated to a good balance of amino acids. However, when the protein is hydrolyzed into smaller peptides, the nutritional value can increase further. A product with high nutritional value added but which does not present economic viability is not interesting for the industry. To analyze such viability, there are softwares that simulate and analyze a process plant. Thus, this study is two-fold: the mathematical modeling of the hydrolysis process and a preliminary analysis of economic viability. Concerning the modeling, two models were fitted: one that relates time with the degree of enzymatic hydrolysis of proteins of concentrated soybean meal and another that relates the degree of hydrolysis with the molecular weight distribution of the pseudocomponents. The analysis of economic viability was conducted using software SuperPro Designer. For this purpose, a degree of hydrolysis and its molecular weight distribution was chosen, and then, an industrial plant was modeled and simulated in a preliminary project level. For the kinetic model, hydrolysis was conducted in laboratorial scale, using a pHstat and an enzyme called Novo-Pro D. The product was analyzed by liquid chromatography. The data were analyzed and the ones obtained in the best conditions were used to fit the model. The best conditions were 55oC, pH 9, 90% humidity, 0,5% m(enz/sub). The kinetic fitting was well described by the Michaelis-Menten with competitive inhibition and neural network described with high fidelity the relation between the concentration of the pseudocomponents and the degree of hydrolysis. Regarding the analysis of the preliminary project of an industrial plant for the production of hydrolyzed protein of concentrated soybean meal, the plant was economic viable, but the project is in a very preliminary stage, due to a lot of unknown data so they were estimated increasing the uncertainty of results.