Desenvolvimento de um protótipo de processo de produção de celulose bacteriana em meio estático

Abstract

Bacterial cellulose (CB) by definition is a flexible biopolymer, formed by fine fibers and nanometric pores. Recently, bacterial cellulose has received research attention due to its unique physicochemical properties when compared to vegetable cellulose. One of the most relevant applications of CB is in the production of cellulosic dressings and immobilization of drugs, enzymes and microorganisms. Large-scale production is considerably limited by the cost of the process as the growing medium can account for up to 30% of the total cost of production. The search for alternative products that can replace the synthetic medium, commonly used, appears as a possibility to make the process feasible on a larger scale. In this context, soybean molasses, a by-product of the soybean processing industry, means an interesting alternative to the synthetic medium, since it contains many nutrients as the main total reducing sugars that makes it a medium with fermentative potential. The process of producing bacterial cellulose from soybean molasses goes through several stages involving acid hydrolysis, filtration of precipitates formed during the hydrolysis, pH adjustment, elimination of possible contaminants through sterilization, addition of ethanol for nutritional enrichment of the medium, inoculum of the microorganism for fermentation, purification of the membranes obtained, neutralization of the films and, finally, drying. This work aimed to contribute to the development of a large - scale bacterial cellulose production process model from experimental data. The constructed model had as steps the mass balance for the determination of the inflow and outflow, calculation of the energy consumption of the equipment used in the process from data specific to the manufacturers as the net power and the cost of the global machinery calculated by of an American tool called Matches. In addition, a schedule of CB production was created on a larger scale by means of the experimental information to estimate the time spent by each stage. The set of this information will enable the implementation of the model in process simulators for further studies on the feasibility and optimization of the built prototype.