Dinâmica, otimização e controle de processos de fermentação em estado sólido : desenvolvimento de metodologias em escala laboratorial

Abstract

Solid-state fermentation is characterized by the growth of microorganisms in absence of free water. In one hand, it is advantageous because simulates their natural environment, enabling the use of agro industrial residues in natura. On the other hand, it limits heat transfer between elements, restricting control over the temperature of the medium. In fact, the microbial growth and the product formation dynamics are directly affected by the environmental conditions and variations can be harmful to the process productivity. As a consequence, the temperature increase caused by metabolic heat needs to be avoided. Studies concerning the microbial dynamics dealing with these variations are scarce. Moreover, it was not found any control laws, with guarantee of stability, which was designed for a reference tracking and to minimize the disturbances effects. Thus, two fronts need to be addressed for the solid-state fermentation viability: the development of a mathematical model able to estimate the effects of environmental changes in the process; and a temperature control system able to handle the heat from microbial metabolism. The model was used in a computational algorithm in order to determine if there was a temperature profile that would be more favorable to the products formation. In this work two control laws were studied, a proportional integrative, because it is the most widespread in the industry, and a model base predictive controller, because of its multivariable control versatility. Both control laws were simulated and then implemented in an eleven liters agitated drum bioreactor. Some of the various methods for PI controller parameters settings had their performance and relative stability requirement evaluated. The one that was proved stable was implemented in the bioreactor. Due to the uncertainties of the fermentation process, a self-adjustment mechanism was added to the predictive controller, in spite of the developed mathematical model, in order to avoid some estimation mistakes caused by some non-estimated states of the real process. The controller achieved an adequate performance with this approach. The results showed that the microorganisms were more efficient at a constant 32°C temperature. In addition, both developed controllers presented appropriate results facing the fermentation process requirement, with mean deviances from the referential temperature below 0,6°C and a maximum error of 2,8°C.