Abstract
On the verge of Cyber-Physical Systems (CPS), companies will need, sooner or later, to adapt their systems in order to follow the emergent visions of Industrie 4.0 and Industrial Internet demanding the digitalization of their processes, preventing the losing of their competiveness levels. The engineering of such innovative manufacturing control systems assumes a crucial challenge without which becomes hard to convince researchers and, primarily, practitioners of the proposed architecture potentials. This paper describes the engineering aspects of deploying an ADACOR (ADAptive holonic COntrol aRchitecture for distributed manufacturing systems) based CPS for a real small-scale production system. Since the solution is using agent technology, a special attention is devoted to the interface from the agent control layer to the physical resources using the industrially adopted OPC-UA (OPC Unified Architecture). At the end, some lessons learned in engineering this CPS are drawn.

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Intelligent Compaction (IC), which is a part of compaction management, is a real-time automatic adjustment and continuous compaction control technology of geomaterials and asphalt layers. Adjustment of the compaction parameters by the equipment is conducted simultaneously to the compaction process, as well as the continuous measurement of a dynamic compaction value, which is an indicator of the material's degree of compaction. This chapter seeks to assess the advantages and disadvantages of IC, as well as formulating a comparison with conventional compaction methods in terms of efficiency. This goal was achieved through in situ application of various technologies to two distinct types of material: a soil-rockfill mixture and a sandy soil. Data was obtained and analysed by the IC continuous information, as well as by the application of several different conventional compaction control tests and methods. Results show that the IC technology presents a superior performance, as well as various advantages when compared to conventional compactors.

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Wind power probabilistic forecast is being used as input in several decision-making problems, such as stochastic unit commitment, operating reserve setting and electricity market bidding. This work introduces a new on-line quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. Its application to the field of wind power forecasting involves a discussion on the choice of the bias term of the quantile models, and the consideration of the operational framework in order to mimic real conditions. Benchmark against linear and splines quantile regression models was performed for a real case study during a 18 months period. Model parameter selection was based on k-fold cross-validation. Results showed a noticeable improvement in terms of calibration, a key criterion for the wind power industry. Modest improvements in terms of Continuous Ranked Probability Score (CRPS) were also observed for prediction horizons between 6 and 20 h ahead.

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The Brazilian power generation sector faces a paradigm change driven, on one hand, by a shift from a hydropower dominated mix and, on the other, by international goals for reducing greenhouse gases emissions. The objective of this work was to evaluate five scenarios for the Brazilian power system until 2050 using a multi-criteria decision analysis tool. These scenarios include a baseline trend and low carbon policy scenarios based on carbon taxes and carbon emission limits. To support the applied methodology, a questionnaire was elaborated to integrate the perceptions of experts on the scenario evaluation process. Taking into account the results from multicriteria analysis, scenario preference followed the order of increasing share of renewables in the power system. The preferable option for the future Brazilian power system is a scenario where wind and biomass have a major contribution. The robustness of the multi-criteria tool applied in this study was tested by a sensitivity analysis. This analysis demonstrated that, regardless the respondents' preferences and backgrounds, scenarios with higher shares of fossil fuel sources are the least preferable option, while scenarios with major contributions from wind and biomass are the preferable option to supply electricity in Brazil through 2050.

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