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%.

Microalgae possess the remarkable ability to autotrophically grow, utilizing atmospheric carbon dioxide (CO₂) for photosynthesis, thereby converting solar energy into chemical energy and releasing oxygen. This capacity makes them an effective tool for mitigating industrial CO₂ emissions. Mathematical models are crucial for predicting microalgal growth kinetics and thus assessing their potential as industrial CO₂ sequestration agents under controlled conditions. This study innovatively evaluated the effect of continuously supplying CO₂ from winemaking processes on microalgal cultivation and biomass production, demonstrating a novel approach to both carbon capture and the valorization of a valuable by-product. To analyze microalgal growth kinetics, three mathematical models were employed: Logistic, First Order Plus Dead Time, and Second Order Plus Dead Time. Optimal parameter values for each model were identified using a hybrid search algorithm developed by our research group. First, an integrated microvinification system was established, utilizing two microalgae species, Chlorella spp. (FAUBA-17) and Desmodesmus spinosus (FAUBA-4), in conjunction with yeast fermenters. This system facilitated a comparison of the biomass kinetics of these two microalgae species, selecting Chlorella spp. (FAUBA-17) as the most suitable candidate for subsequent cultivation. A pilot-scale vertical column photobioreactor was then constructed and installed at the Casimiro Wines boutique winery in Angaco, San Juan, Argentina. After 15 days of operation within the photobioreactor, a biomass growth of 1.04 ± 0.05 g/L and 1.07 ± 0.1 g/L was obtained in Photobioreactors 1 and 2, respectively. This novel integrated approach to CO₂ capture in the winemaking process is unprecedented. These findings highlight the potential for producing high-value microalgal biomass, promoting the establishment of a local biorefinery and fostering a circular economy and sustainable social development.

In this work, we were able to generalize the applicability of the Luedeking and Piret with delay time model previously proposed by our team. To demonstrate this, experimental data and kinetic parameters were collected from four different bioproduction. To these experimental data, the first order plus dead time (FOPDT) model was fitted for biomass production kinetics, while the Luedeking and Piret with delay time model was for different metabolites production kinetics. The use of the Luedeking and Piret with delay time model proves the delay that exists between the production time of microbial biomass and metabolites or products. The use of the FOPDT model allowed this delay to be determined in a simple and fast way. This model's combination shows an increase in R2 in all cases, demonstrating the quality of the fit and the simplicity of the proposed method. The use of delay times for bioproduction with non-zero kinetic parameters has not been previously reported. Application of this model would improve the accuracy of scaling bioprocesses to enable industrial-scale production. This delay time is an essential property of the bio-process, and its determination is crucial for control and optimization.

In the musts of wines there are spoilage yeasts that produce negative effects on the flavor. To try to eliminate it SO2 is used in wineries. However, this is toxic to human health and the World Health Organization (WHO) promotes an immediate reduction in its consumption. An alternative to the use of SO2 is the addition of killer yeast to eliminate or reduce the undesirable yeast. In previous works, was found that pH is a relevant variable in controlling the population of killer yeast and, for that reason, for the growth of undesirable yeast. It improves the fitness of the killer yeast and the inhibitor. In addition, it is a manipulable variable through the organic acids allowed in wines. Moreover, a new improvement to the mathematical model is proposed, incorporating the parametric variation with pH. Likewise, the new model was simulated using Matlab for the optimization by Monte Carlo method for the minimization of the spoilage yeasts population and the maximization of the growth of the killer. The optimum value for the objectives was pH 3.98. The results of the simulation were validated experimentally. The improved model is a need for the oenology industry, it allows the spoilage yeasts control.

This paper develops a dynamic optimization methodology based on direct numerical methods, for the bioethanol fed-batch production from glucose and fructose as a substrate. The mathematical model that governs the process consists of six differential equations and is highly nonlinear. The proposed strategy uses the Fourier trigonometric basis and normalized orthogonal polynomials for substrate feeding rate parameterization. Then, evolutionary algorithms and gradient methods are combined to search parameters that generate the best control action. This parameterization methodology requires a minimum number of parameters to optimize. Also, the continuous and differentiable nature of the optimal profile enables its direct implementation in the physical process, eliminating the necessity for filtering or smoothing it. In addition, they are ideal for bioprocesses, in which it is preferable to avoid abrupt changes in the operating modes of the process to promote cell growth. As a result, using only 3 parameters, a 3.5% increase in ethanol production was achieved, while the reference uses at least 10 parameters and provides a stepped feed profile. The simulations have yielded promising results, making this proposal an alternative with excellent potential for process optimization.