Concern about global warming and the high consumption of fossil fuels has led some countries to seek and invest in new energy sources that are efficient and less polluting. Among these alternatives, hydrogen fuel cells are a potential solution that can generate clean energy. Due to the industrial production of hydrogen being carried out by steam reforming of methane, which uses non-renewable raw material and is endothermic (resulting in high energy costs), the autothermal reform of ethanol has been presenting itself as an interesting technology, as it combines a renewable raw material with the reactions of reform (endothermic) and partial oxidation (exothermic), thus achieving energy self-sufficiency in the process of converting ethanol to hydrogen. Despite the various studies referring to the autothermal reform of ethanol, to our knowledge, no article has presented a detailed review of the main advances made in recent years for this process. Thus, this review presents the main results for the autothermal reform of ethanol, in recent years, in three main areas: Catalysts, Reactor Design and Modeling / Simulation. This work identified that the greatest advances have been made in the development of new catalysts and the design of reactors, while the modeling/simulation area still has few studies to efficiently describe the thermodynamics of the autothermal reform of ethanol.
Brazil is a signatory to the Paris Agreement and aims to reduce 43% of CO2 emissions by 2030, compared to 2005. However, changes in energy policies are needed to achieve this goal, evaluating the produced effects on emissions. One way to predict these effects is through mathematical modeling. In this paper, we carried out a literature review to identify the most used model types and independent variables to forecasting Brazilian CO2 emissions. The review showed that gray models and artificial neural networks are the most used ones. Furthermore, we also identified that economic growth and energy consumption are the main independent variables.
Brazil is a developing country that emits high amounts of CO2 per year. Therefore, controlling these emissions is essential to achieving sustainable development. In this paper, we modeled an Artificial Neural Network capable of quantitatively relating CO2 emissions, energy matrix, and burning in Brazilian biomes, such as the Amazon Forest. The literature still does not have studies that quanti- tatively demonstrate the impact that changes in the Brazilian energy matrix have on CO2 emissions in the country. In addition, there are also no studies that use fires in Brazilian biomes as input in predictive models for emissions. Our results showed that Brazilian CO2 emissions will increase in the coming years. However, partially replacing fossil energy resources with renewables associated with reducing fires in Brazilian biomes could significantly reduce these emissions. In our first scenario, with a partial replacement of 30% of fossil resources by renewable resources and a 70% reduction in the burning of Brazilian biomes, CO2 emissions decreased by 13.58% for the year 2030. In the second scenario analyzed, we replaced fossil fuels by 90% with renewable ones, while the burning in Brazilian biomes was reduced by 90%. We observed a 28.45% reduction in Brazilian CO2 emissions in this situation. Thus, the model developed here can help Brazil to predict and control its CO2 emissions from changes in its energy and environmental indicators to find a balance between development and sustainability. Other developing countries can also use our model. For this, the indicators must be adapted to the reality of the country studied.
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