Integrating renewable and intermittent energy sources into the electricity sector challenges traditional energy systems based on predictability and constant supply. Studies oncomplementarity between climate-related resources from different regions and countries are proving to be an efficient means to overcome the variability of single-source use. Although Rio de Janeiro State (Brazil) has set goals of increasing its use of clean and low carbon energy, there is no study to support the expansion process. Given that, this work aims to assess the complementarity potential of small hydropower plants, wind farms, and photovoltaic panels in the state. Power output estimates have been based on wind speeds, solar radiation and river flow data and without generation technologies assumptions. The Pearson correlation coefficient and linear programming have been used to comprehend and optimize the renewable mix. Daily complementarity has been observed among the energy sources considered, especially between hydro and solar resources. The optimization process showed an improvement of 61% in the total power standard deviation, from the worst—100% hydro power—to the best case—62% of photovoltaic, 21% of wind, and 17% of hydro power. The results highlight the benefits of appropriately joining the three sources and suggest investing in photovoltaic generation.

This article presents an alternative way of evaluating the efficiency of the electric distribution companies in Brazil. This assessment is currently performed and designed by the National Electric Energy Agency (ANEEL), a Brazilian regulatory agency, to regulate energy prices. This involves calculating the X-factor, which represents the efficiency evolution in the price-cap regulation model. The proposed model aims to use a network Data Envelopment Analysis (DEA) model with the network dimension as an intermediate variable and to use Kohonen Self-Organizing Maps (SOM) to correct the difficulties presented by environmental variables. In order to find which environmental variables influence the efficiency, factor analysis was used to reduce the dimensionality of the model. The analysis still uses multiple regression with the previous efficiency as the dependent variable and the four factors extracted from factor analysis as independent variables. The SOM generated four clusters based on the environment and the efficiency for each distributor in each group. This allows for a better evaluation of the correction in the X-factor, since it can be conducted inside each cluster with a maintained margin for comparison. It is expected that the use of this model will reduce the margin of questioning by distributors about the evaluation.

This study compares the carbon footprints of two municipal solid waste treatment technologies—anaerobic digestion and a gas recovery system—with the aim of evaluating their potential for biogas recovery and greenhouse gas (GHG) mitigation. The analysis applies the 2006 IPCC model to real operational data from the Paracambi Waste Treatment Complex (Rio de Janeiro, Brazil), integrating carbon footprint estimation and environmental compensation modeling through tree planting. From a different perspective, this work evaluates the replacement of biogas recovery with a biologically controlled system based on material segregation. Within the limits and parameters defined for the system, anaerobic digestion achieved net emissions of 0.0029 tCO₂eq per ton of organic waste, compared to 1.14 tCO₂eq per ton for the biogas recovery system. This represents a potential 393-fold reduction in GHG emissions. However, this result is specific to the modeled conditions and does not consider the full life cycle impacts of non-organic waste fractions. The results suggest that anaerobic digestion, when integrated into an efficient selective collection system, can significantly improve energy recovery and mitigate the carbon footprint of waste management systems.

Transformations from the traditional industry model to the Industry 4.0 model presents great challenges for Industrial Engineering professionals and students. Consequently, it becomes necessary to adapt already consolidated concepts in the academic and professional environment to meet the peculiarities and needs of this new business model. To understand these challenges of the fourth industrial revolution for those professionals, it was performed a bibliometric research to comprehend the publication’s profile involving the main concepts of Industry 4.0 and Industrial Engineering topics. This research consists of the analysis and interpretation of Scopus and Web of Science databases from January 2012 to November 2019. The results found indicate significant growth of scientific articles covering Industry 4.0 concepts applied in Industrial Engineering fields, as well as the distribution of publications by branch. The main contribution of this research article is to present the applicability of Industry 4.0 concepts in Industrial Engineering branches, plus the trends and opportunities for future research.