Dynamic Optimization of Lactic Acid Production from Grape Stalk Solid-State Fermentation with Rhizopus oryzae Applying a Variable Temperature Profile

Abstract

Lactic acid is widely used in the food industry. It can be produced via chemical synthesis or biotechnological pathways by using renewable resources as substrates. The main challenge of sustainable production lies in reaching productivities and yields that allow for their industrial production. In this case, the application of process engineering becomes a crucial tool to improve the performance of bioprocesses. In this work, we performed the solid-state fermentation of grape stalk using Rhizopus oryzae NCIM 1299 to obtain lactic acid, employing three different temperatures (22, 35, and 40 °C) and a relative humidity of 50%. The Logistic and First-Order Plus Dead Time models were adjusted for fungal biomass growth, and the Luedeking and Piret with Delay Time model was used for lactic acid production, obtaining higher R2 values in all cases. At 40 °C, it was observed that Rhizopus oryzae grew in pellet form, resulting in an increase in lactic acid productivity. In this context, the effect of temperature on the kinetic parameters was evaluated with a polynomial correlation. Finally, using this correlation, a smooth and continuous optimal temperature profile was obtained by a dynamic optimization method, improving the final lactic acid concentration by 53%.

Determination of Variable Humidity Profile for Lactic Acid Maximization in Fungal Solid-State Fermentation

“…It is important to note that in the present study, no growth morphology variation was observed in R. oryzae NCIM 1299 was observed, as it always grew in a mycelial form. In contrast, in the previous study on the effect of temperature [25], there was a change in the morphology of the fungus, with pellet growth observed at 40 °C and 50 %RH. This could indicate that there is no direct effect of RH on fungal metabolism related to morphological changes, as is the case with temperature.…”

“…Once the experimental data from the bioprocess were obtained, mathematical models were chosen to adequately represent the kinetic behavior of fungal biomass production and lactic acid production. Mathematical models were adjusted to these kinetic curves: on the one hand, the Logistic (see equation 1 from Figure 1) and First Order Plus Dead Time (FOPDT) model (see equation 2 from Figure 1) models were used to describe biomass generation, and on the other hand, the Luedeking and Piret with Delay Time model (LPwDT) model (see equation 3 from Figure 1) was applied to describe LA production [24,25].…”