Due to growing worldwide energy demand, the search for diversification of the energy matrix stands out as an important research topic. Bioethanol represents a notable alternative of renewable and environmental-friendly energy sources extracted from biomass, the bioenergy. Thus, the assurance of optimal growth conditions in the fermenter through operational variables manipulation is cardinal for the maximization of the ethanol production process yield. The current work focuses in the determination of optimal control scheme for the fermenter feed rate and batch end-time, evaluating different parametrization profiles, and comparing evolutionary computation techniques, the genetic algorithm (GA) and differential evolution (DE), using a dynamic real-time optimization (DRTO) approach for the in silico ethanol production optimization. The DRTO was able to optimize the reactor feed rate considering disturbances in the process input. Open-loop tests results obtained for the algorithms were superior to several works presented in the literature. The results indicate that the interaction between the intervals of DRTO cycles and parametrization profile is more significant for the GA, both in terms of ethanol productivity and batch time. In general lines, the present work presents a methodology for control and optimization studies applicable to other bioenergy generation systems.

Countless problems have been caused by the excessive emission of polluting gases, such as carbon dioxide (CO₂), into the atmosphere. Therefore, more effective monitoring of CO₂ is essential, especially in central or industrial regions. Thus, this work shows the development of a platform for monitoring the CO₂ concentration, which is composed of autonomous and independent sensors that have their own energy source, storage capacity, and data replication for the central server. To validate the platform, CO₂ measurements were taken at three strategic points in an outdoor environment in a Brazilian urban center. This platform proved to be an evolution over another system previously proposed by the group that was based on the use of a wireless network of sensors to monitor CO₂. This new project managed to overcome limitations that compromised the efficiency of the first platform, which were mainly related to the interference in the communication signals between the network sensors due to the existence of physical barriers in the monitoring environment. With that, this new platform showed greater security in the maintenance of collected data and allowed for the expansion of the physical complexity of the environments that can be monitored.

Hydrogen has received considerable attention as a sustainable energy source to the future and biological hydrogen production has been gaining consideration as a potential method. One microorganism that produces hydrogen is the nitrogen-fixing cyanobacterium Cyanothece sp. ATCC 51142, which stands out for its rate of hydrogen production. However, there are still some challenges for understanding and improving the hydrogen production process by this cyanobacterium. Thus, this work aimed to perform an in silico study of a feeding profile strategy for hydrogen production by Cyanothece sp. ATCC 51142 in batch/fed-batch process to improve the amount of hydrogen generated during the process. Two meta-heuristic methods were used to find the best feeding profile and the switch time between batch and fed-batch modes. Hydrogen production is similar in the results of both methods, but PSO indicated a simpler feeding policy, leading to 19.5% more hydrogen produced than that reported in the literature.

Effective immunization scenarios by vaccination against COVID-19 obtained through Bayesian analysis