The world’s human population is increasing as is the demand for new sustainable sources of energy. Accordingly, microalgae-based carbohydrates for biofuel production are being considered as an alternative source of raw materials for producing biofuels. Microalgae grow in photobioreactors under constantly changing conditions. Models improve our understanding of microalgae growth. In this paper, a photoacclimated model for continuous microalgae cultures in photobioreactors was used to study the time-varying behavior and sensitivity of solutions under optimal productivity conditions. From the perspective of dynamic simulation in this work, light intensity was found to play an influential role in modifying metabolic pathways as a cell stressor. Enhancing carbohydrate productivity by combining nutritional deficiency and light intensity regulation modeling strategies could be helpful to optimize the process for the highest yield in large-scale cultivation systems. Under the proposed simulation conditions, a maximum carbohydrate productivity of 48.11 gCm${}^{-3}$d${}^{-1}$ was achieved using an optimal dilution rate of 0.2625 d${}^{-1}$ and 350 $\mathsf{\mu }$molm${}^{-2}$s${}^{-1}$ of light intensity. However, it is important to note that, a particular set of manipulated inputs can generate multiple outputs at a steady state. A numerical solution of the sensitivity functions indicated that the model outputs were especially sensitive to changes in parameters corresponding to a minimum nitrogen quota, maximum nitrogen intake rate, dilution rate, and maximum nitrogen quota compared to to other model parameters.

Studies enabled by metabolic models of different species of microalgae have become significant since they allow us to understand changes in their metabolism and physiological stages. The most used method to study cell metabolism is FBA, which commonly focuses on optimizing a single objective function. However, recent studies have brought attention to the exploration of simultaneous optimization of multiple objectives. Such strategies have found application in optimizing biomass and several other bioproducts of interest; they usually use approaches such as multi-level models or enumerations schemes. This work proposes an alternative in silico multiobjective model based on an evolutionary algorithm that offers a broader approximation of the Pareto frontier, allowing a better angle for decision making in metabolic engineering. The proposed strategy is validated on a reduced metabolic network of the microalgae Chlamydomonas reinhardtii while optimizing for the production of protein, carbohydrates, and CO${}_2$ uptake. The results from the conducted experimental design show a favorable difference in the number of solutions achieved compared to a classic tool solving FBA.

Microalgae-based biomass has been extensively studied because of its potential to produce several important biochemicals, such as lipids, proteins, carbohydrates, and pigments, for the manufacturing of value-added products, such as vitamins, bioactive compounds, and antioxidants, as well as for its applications in carbon dioxide sequestration, amongst others. There is also increasing interest in microalgae as renewable feedstock for biofuel production, inspiring a new focus on future biorefineries. This paper is dedicated to an in-depth analysis of the equilibria, stability, and sensitivity of a microalgal growth model developed by Droop (1974) for nutrient-limited batch cultivation. Two equilibrium points were found: the long-term biomass production equilibrium was found to be stable, whereas the equilibrium in the absence of biomass was found to be unstable. Simulations of estimated parameters and initial conditions using literature data were performed to relate the found results to a physical context. In conclusion, an examination of the found equilibria showed that the system does not have isolated fixed points but rather has an infinite number of equilibria, depending on the values of the minimal cell quota and initial conditions of the state variables of the model. The numerical solutions of the sensitivity functions indicate that the model outputs were more sensitive, in particular, to variations in the parameters of the half saturation constant and minimal cell quota than to variations in the maximum inorganic nutrient absorption rate and maximum growth rate.

The current high cost of producing green hydrogen, for use as an energy vector, has motivated the search for the development of non-conventional technologies for its production, joining forces on the path towards energy transition. Hydrogen production by aluminum corrosion in aqueous acid solutions seems to be a promising alternative. In order to evaluate its technical feasibility, a kinetic study was carried out, analyzing the impact of HCl concentration (1.125 to 1.75 M) on the aluminum corrosion capacity under the presence of a saline environment and using a promoter, fitting the proposed models to the data obtained through experimental runs. Although other studies use the shrinking core model to describe the kinetics of this type of reaction, in most cases, it does not fit well with the experimental data and needs to be modified. Finally, by considering the corrosion dynamics (variations in diffusion coefficients and shell thickness) in the kinetic model equations, it was possible to describe its behavior. For low HCl concentrations, a single resistance controls the reaction of the particle throughout; however, for high HCl concentrations, a combination of related equations must be used. The results of this study enable viable continuous reactor designs for a given amount of green hydrogen production.

Given the need to improve bioenergy production processes, it is necessary to focus on low-cost culture media and environmental conditions of radiation and temperature. The Scenedesmus dimorphus species was cultured in eutrophicated lagoon water and Bayfolan 0.3% as culture media under four photoperiods with the objective of evaluating the biomass productivity, bioremediation capacity and influence of illumination on the composition and lipid content. It is concluded that the increase of light hours in the culture with eutrophicated lagoon water produces a decrease in the biomass productivity and COD removal percentage. The highest biomass productivity was obtained in photoperiod F1 (10.5:13.5) hours L:O, 0.053 ± 0.0015 g/L day and a removal of 95.6%. Bayfolan 0.3% with F2 (11.5:12.5) and F3 (12.5:11.5) did not show significant differences in the biomass productivity and COD removal. The increase in light hours in the photoperiod induced an increase of 1.01% and 2.84% of saturated fatty acids and 0.8% and 2.14% of monounsaturated fatty acids, as well as a decrease of 3.85% and 2.88% of polyunsaturated fatty acids in eutrophicated lagoon water and Bayfolan 0.3%, respectively.

Satisfaction is relevant for decision makers (DM, Decision Makers). Satisfaction is the feeling produced in individuals by executing actions to satisfy their needs, for example, the payment of debts, jobs, or academic achievements, and the acquisition of goods or services. In the satisfaction literature, some theories model the satisfaction of individuals from job and customer approaches. However, considering personality elements to influence satisfaction and define preferences in strategies that optimize decision making provides the unique characteristics of a DM. These characteristics favor the scope of solutions closer to the satisfaction expectation. Satisfaction theories do not include specific elements of personality and preferences, so integrating these elements will offer more efficient decisions in computable models. In this work, a model of satisfaction with personality characteristics that influence the preferences of a DM is proposed. The proposed model is integrated into a preference-based optimizer that improves the decision-making process of a Virtual Decision Maker (VDM) in an optimization context. The optimization context addressed in this work is the product selection process within a food product shopping problem. An experimental design is proposed that compares two configurations that represent the cognitive part of an agent’s decision process to validate the operation of the proposed model in the context of optimization: (1) satisfaction, personality, and preferences, and (2) personality and preferences. The results show that considering satisfaction and personality in combination with preferences provides solutions closer to the interests of an individual, reflecting a more realistic behavior. Furthermore, this work demonstrates that it is possible to create a configurable model that allows adapting to different aptitudes and reflecting them in a computable model.